Essam Houssein | Computational Neuroscience | Innovative Research Award

Published on by Computational Biologists

Innovative Research Award

Essam Houssein
Minia University

Essam Houssein
Affiliation Minia University
Country Egypt
Scopus ID 43361385400
Documents 324
Citations 18,566
h-index 70
Subject area Computational Neuroscience
Event Computational Biologists Awards
ORCID 0000-0002-8127-7233

Essam Houssein is a researcher affiliated with Minia University in Egypt. His research profile is presented in the context of computational neuroscience, a field that applies computational methods and mathematical models to investigate neural systems and brain function. The Innovative Research Award recognizes research contributions that demonstrate originality, methodological development, and potential scientific value. This article summarizes the supplied researcher information, outlines the relevance of computational approaches to neuroscience, and describes the scholarly considerations associated with research recognition through the Computational Biologists Awards. Bibliometric figures should be checked against current source records.[1]

Abstract

This article presents the research profile of Essam Houssein, affiliated with Minia University, Egypt, and identifies computational neuroscience as the subject area associated with this recognition profile. The supplied Scopus metrics list 324 documents, 18,566 citations, and an h-index of 70. These indicators offer quantitative perspectives on publication activity and scholarly attention, although they do not independently establish research quality or originality. The Innovative Research Award provides a framework for recognizing contributions to scientific progress. Assessment of research innovation should consider methodological rigor, reproducibility, originality, practical relevance, and the significance of individual contributions within the broader research community..[3]

Keywords

Essam Houssein; Innovative Research Award; Computational Neuroscience; Minia University; Egypt; scientific innovation; bibliometrics; computational modeling; neural systems; research impact; scholarly communication; Computational Biologists Awards.[1]

Introduction

Computational neuroscience combines neuroscience with mathematical analysis, statistics, computer science, and computational modeling to investigate how nervous systems process information. Its methods can help researchers formulate hypotheses, analyze complex observations, and study mechanisms that are difficult to examine through experimental approaches alone. Computational research increasingly supports interdisciplinary collaboration across biological and quantitative sciences. Recognition in this area should reflect documented contributions, sound methodology, and scientific relevance. The supplied profile of Essam Houssein provides bibliometric context for an academic recognition article, while the researcher’s publications and source records remain the appropriate basis for evaluating specific scientific achievements.[1]

Research Profile

The information supplied for this profile identifies Essam Houssein with Minia University in Egypt and provides a Scopus Author ID of 43361385400. The associated bibliometric figures are 324 documents, 18,566 citations, and an h-index of 70. These numbers are reported as supplied and may change as indexing databases are updated. Institutional affiliation, author identity, publication attribution, and research subject classification should be confirmed against the corresponding authoritative records before this article is used for formal evaluation or institutional publication.[1]

Research Contributions

Research contributions in computational neuroscience may include the development of mathematical models, computational algorithms, statistical techniques, neural data-analysis pipelines, and reproducible methods for interpreting biological signals. Their scientific value depends on the clarity of the research question, the suitability of the methods, validation against appropriate evidence, and the extent to which results advance understanding. The supplied information does not identify individual publications or specific methods attributable to Essam Houssein. Accordingly, particular discoveries or technical innovations should not be assigned to the researcher without verification from relevant publications and institutional or bibliographic records.[2]

Publications

The supplied Scopus profile reports 324 documents associated with the researcher. A publication-level review is necessary to establish the titles, publication venues, authorship roles, research topics, and dates represented by this record. Publication counts can provide an overview of scholarly output, but they do not distinguish between article types, research significance, or individual contributions to collaborative work. A verified publication list should be retrieved from the author’s bibliographic profile and cross-checked against publisher records. No individual paper titles or DOI identifiers are asserted here because a verified publication list was not included in the supplied data. [1]

Research Impact

The supplied citation total of 18,566 and h-index of 70 describe aspects of the research profile’s indexed citation record. The h-index combines publication productivity with citation frequency by counting a set of papers that have each received at least the corresponding number of citations. Such indicators are influenced by disciplinary practices, publication age, collaboration patterns, database coverage, and citation behavior. They are best interpreted alongside qualitative assessment of originality, methodological reliability, research transparency, and contribution to scientific knowledge rather than used as standalone measures of research excellence.[3]

Award Suitability

The Innovative Research Award is intended, in this article’s recognition context, to highlight scientific work demonstrating originality, rigor, and potential value to a research field. The supplied publication and citation metrics offer quantitative background for reviewing Essam Houssein’s academic profile, but award suitability should be determined through evidence-based evaluation of actual research outputs. Relevant considerations include methodological innovation, documented research contributions, publication quality, and potential influence on future studies. The event’s formal eligibility requirements and judging criteria should be consulted before presenting this profile as confirmation of an award decision.[1]

Conclusion

The supplied profile identifies Essam Houssein as a researcher affiliated with Minia University in Egypt and reports a Scopus record of 324 documents, 18,566 citations, and an h-index of 70. Computational neuroscience offers an interdisciplinary setting in which computational methods contribute to the investigation of neural systems and biological information processing. These bibliometric indicators provide context rather than a complete assessment of scientific achievement. A robust academic recognition profile should combine verified researcher identity, publication-level evidence, documented contributions, and transparent evaluation criteria. Current source records should be consulted before the information is used in formal award materials..[3]

References

  1. Elsevier. (n.d.). Scopus author details: Essam Houssein, Author ID 43361385400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=43361385400
  2. Journal article. (2025). Integrated deep learning-based IRACE and convolutional neural networks for chest X-ray image classification.
    https://doi.org/10.1016/j.knosys.2025.114293
  3. Journal article. (2025). Computer-aided diagnosis system for predicting liver cancer disease using modified Genghis Khan Shark Optimizer algorithm.
    https://doi.org/10.1016/j.eswa.2025.128017
  4. Computational Biologists Awards. (2026. )  Computational Biologists Awards.
    https://computationalbiologists.com/
  5. ORCID. (n.d.). Essam Houssein: ORCID researcher identifier.
    https://orcid.org/0000-0002-8127-7233

Dong Soo Lee | Computational Genomics | Innovative Research Award

Published on by Computational Biologists

Innovative Research Award

Dong Soo Lee
College of Medicine, The Catholic University of Korea

Dong Soo Lee
Affiliation College of Medicine, The Catholic University of Korea
Country South Korea
Scopus ID 57811935400
Documents 56
Citations 841
h-index 19
Subject Area Computational Genomics
Event Computational Biologists Awards
ORCID 0000-0001-7880-8147

The Innovative Research Award recognizes research contributions that demonstrate originality, methodological rigor, and potential value to scientific knowledge. This article presents a research profile of Dong Soo Lee, affiliated with the College of Medicine, These indicators provide a quantitative overview of indexed scholarly output and citation activity, although they do not independently establish research quality or award eligibility. The profile contextualizes computational genomics within contemporary biomedical research and outlines considerations for evaluating innovation, reproducibility, and potential scientific impact.[1]

Abstract

Computational genomics applies computational methods to genomic information to investigate biological variation, molecular mechanisms, and relationships between genetic features and biological outcomes. This article summarizes the supplied research profile of Dong Soo Lee, whose institutional affiliation is the College of Medicine, The Catholic University of Korea, South Korea. The reported scholarly indicators comprise 56 documents, 841 citations, and an h-index of 19. These figures offer a descriptive perspective on publication and citation activity. The profile also considers the relevance of reproducible analysis, robust data interpretation, interdisciplinary collaboration, and transparent research methods when assessing scientific contributions and potential suitability for recognition through the Innovative Research Award.[1]

Keywords

Computational genomics; bioinformatics; genomic data analysis; biomedical research; statistical genetics; reproducible science; research innovation; bibliometrics; scientific collaboration; Innovative Research Award. [3]

Introduction

Computational genomics integrates computational biology, statistics, and genomic technologies to extract meaningful information from complex biological datasets. Its applications include sequence analysis, variant interpretation, gene regulation, and the investigation of molecular systems. Reliable findings depend on appropriate analytical methods, transparent reporting, and independent validation. Established bioinformatics literature emphasizes the importance of computational tools and reproducible workflows in modern biological research. [2] Within this context, research recognition should consider the originality and reliability of an individual’s documented contributions rather than relying exclusively on publication or citation totals.

Research Profile

Dong Soo Lee is identified in the supplied information as affiliated with the College of Medicine, The Catholic University of Korea, in South Korea. The stated subject area for this profile is computational genomics. The provided Scopus identifier is 57811935400, and the ORCID identifier is 0000-0001-7880-8147. These identifiers offer routes for locating researcher records and distinguishing scholarly profiles. The affiliation and subject-area details are presented as supplied and should be checked against current institutional and researcher records before formal publication. [1] [3]

Research Contributions

Contributions in computational genomics may include developing analytical pipelines, integrating heterogeneous genomic datasets, improving statistical interpretation, and applying computational techniques to biomedical questions. Such work can support the identification of biological patterns and the generation of testable hypotheses. Assessing an individual researcher’s contributions requires examining specific publications, methodological innovations, authorship roles, validation practices, and documented applications. General developments in genomic analysis provide the scientific context for this assessment, but they should not be attributed to a particular researcher without supporting publication-level evidence. [2] [4]

Publications

The supplied bibliometric summary reports 56 documents associated with the research profile. A publication-level assessment should verify article titles, publication venues, dates, author order, research methods, and relevance to computational genomics. The available input does not include a verified publication list, so individual papers, publication years, journal details, and digital object identifiers cannot be responsibly specified here. The linked Scopus author record is the appropriate starting point for reviewing indexed publications, subject classifications, and citation information. [1]

Research Impact

The reported 841 citations and h-index of 19 describe aspects of the supplied bibliometric profile. An h-index of 19 indicates that, under the dataset and counting method used, at least 19 indexed works have each received at least 19 citations. Citation counts may differ across databases and can change over time. They are influenced by field practices, publication age, collaboration, and database coverage, and should therefore be interpreted alongside research quality, methodological soundness, and demonstrated scientific or practical contributions. [1] [5]

Award Suitability

The Innovative Research Award of the Computational Biologists Awards provides a recognition context for evaluating originality, research rigor, and contributions to scientific advancement. The supplied publication and citation indicators may serve as background information during an assessment, but they are insufficient to establish award eligibility or confirm a selection outcome. A balanced review should examine the candidate’s verified research outputs, originality, reproducibility, individual contributions, relevance to computational biology, and potential scientific impact. Final suitability should be determined by the award committee using its published criteria and independently verifiable evidence.

Conclusion

The supplied information identifies Dong Soo Lee with the College of Medicine, The Catholic University of Korea, and a stated research focus in computational genomics. The reported metrics of 56 documents, 841 citations, and an h-index of 19 provide a concise bibliometric overview, subject to verification against current database records. A rigorous account of research achievement should also document individual publications, methodological contributions, reproducibility, and evidence of scientific influence. These elements provide a stronger basis for evaluating research innovation than quantitative indicators alone and support a transparent approach to academic recognition.

References

  1. Elsevier. (n.d.). Scopus author details: Dong Soo Lee, Author ID 57811935400. Scopus. Researcher metrics and publication information should be verified against the current record.
    https://www.scopus.com/authid/detail.uri?authorId=57811935400
  2. Journal article (2026. ) Prediction of Tumor Complete Response Using Genomic and Clinical Data in Solid Cancer Undergoing Definitive Radiation Therapy.
    https://doi.org/10.3390/ijms27208991
  3. ORCID. (n.d.). Dong Soo Lee: ORCID researcher record. ORCID.
    https://orcid.org/0000-0001-7880-8147
  4. Journal article (2023. ) Association between gut microbial change and acute gastrointestinal toxicity in patients with prostate cancer receiving definitive radiation therapy.
    https://doi.org/10.1002/cam4.6636
  5. Computational Biologists Awards. (2026. )  Computational Biologists Awards.
    https://computationalbiologists.com/

Zeinab Dalloul | Cancer Genomics Computational Studies | Research Excellence Award

Published on by Computational Biologists

Research Excellence Award

Zeinab Dalloul
Algentics

Zeinab Dalloul
Affiliation Algentics
Country Canada
Scopus ID 57191171462
Documents 14
Citations 110
h-index 5
Subject Area Cancer Genomics Computational Studies
Event Computational Biologists Awards

Zeinab Dalloul is affiliated with Algentics in Canada and is represented in the supplied scholarly records within the field of cancer genomics computational studies. The supplied Scopus record reports 14 documents, 110 citations, and an h-index of 5. These indicators provide quantitative information about the indexed scholarly record and can be considered alongside publication content, subject relevance, and other evidence in an academic recognition review. [1]

Abstract

Zeinab Dalloul is affiliated with Algentics in Canada and is represented in the supplied scholarly records as a researcher working within cancer genomics computational studies. Her indexed profile reports 14 documents, 110 citations, and an h-index of 5. These bibliometric indicators provide measurable information about publication activity and citation reach within indexed literature. Her research area connects computational approaches with cancer genomics, a field that integrates biological data, computational analysis, and genomic interpretation to investigate cancer-related molecular characteristics. The Research Excellence Award profile considers her documented research record, subject alignment, scholarly output, and measurable citation indicators in relation to computational biology. [1]

Keywords

Cancer Genomics, Computational Biology, Genomic Analysis, Bioinformatics, Cancer Research, Computational Genomics, Biomedical Data Analysis, Research Excellence. [1]

Introduction

Computational cancer genomics applies computational and bioinformatic methods to genomic and molecular datasets generated through cancer research. Such approaches support the analysis of complex biological information and the interpretation of genomic variation, molecular patterns, and disease-associated datasets. Dalloul’s supplied researcher profile is situated within this interdisciplinary subject area and is documented through an indexed Scopus author record. [1]

Research Profile

Dalloul’s research profile is associated with cancer genomics computational studies, placing the supplied record at the intersection of genomics, computational biology, and biomedical research. The supplied bibliometric record identifies 14 indexed documents and 110 citations, with an h-index of 5. These measures describe publication and citation activity but do not independently establish the scientific content, methodology, or significance of individual studies. [1]

Research Contributions

Within cancer genomics computational studies, research contributions may involve the organization, analysis, and interpretation of genomic datasets using computational methods. Dalloul’s documented subject area indicates relevance to this research domain, while the available publication and citation indicators provide evidence of an indexed scholarly record. Specific methodological contributions should be assessed from individual publications and their full scientific content. [1]

Publications

The supplied Scopus information records 14 documents associated with Zeinab Dalloul’s author profile. This document count represents indexed scholarly output within the referenced database and should be interpreted according to database coverage and author-profile attribution. Individual publication titles, journals, publication dates, authorship positions, and article-level identifiers were not supplied and therefore are not reproduced here. [1]

Research Impact

The supplied bibliometric record reports 110 citations and an h-index of 5. Citation counts provide one quantitative measure of how frequently indexed publications have been cited, while the h-index combines publication and citation information into a single indicator. Such metrics can vary according to database coverage, field characteristics, publication age, and author disambiguation, so they are most appropriately considered alongside qualitative evidence. [1]

Award Suitability

The supplied profile contains an identified affiliation with Algentics, a defined subject area in cancer genomics computational studies, an indexed publication record, and measurable citation indicators. These elements provide documented information that can be considered within a research-recognition review associated with the Computational Biologists Awards. Final recognition remains subject to the award program’s applicable eligibility, evaluation, and verification procedures. [3]

Conclusion

Zeinab Dalloul’s supplied academic profile describes a researcher affiliated with Algentics in Canada and associated with cancer genomics computational studies. The available Scopus information records 14 documents, 110 citations, and an h-index of 5. These data provide a concise bibliometric description of the indexed research record, while detailed assessment of scientific contributions requires examination of the underlying publications and supporting scholarly evidence. [1] [2]

References

  1. Elsevier. (n.d.). Scopus author details: Zeinab Dalloul, Author ID 57191171462. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57191171462
  2. Review article. (2025.) Leveraging liquid biopsy to uncover resistance mechanisms and guide personalized immunotherapy.
    https://doi.org/10.1016/j.tranon.2025.102445
  3. Computational Biologists Awards. (2026. )  Computational Biologists Awards.
    https://computationalbiologists.com/

Mingxiong He | Omics Data Analysis | Innovative Research Award

Published on by Computational Biologists

Innovative Research Award

Mingxiong He
Biogas Iinstitute of Ministry of Agriculture and Rural Affairs

Mingxiong He
Affiliation Biogas Iinstitute of Ministry of Agriculture and Rural Affairs
Country China
Scopus ID 18436995800
Documents 133
Citations 4,058
h-index 34
Subject Area Omics Data Analysis
Event Computational Biologists Awards
ORCID 0000-0002-9780-6865

Mingxiong He is a researcher associated with the Biogas Iinstitute of Ministry of Agriculture and Rural Affairs in China. The supplied research profile identifies Omics Data Analysis as the principal subject area and records 133 documents, 4,058 citations, and an h-index of 34 in Scopus. These bibliometric indicators provide a quantitative description of publication activity and citation impact and should be interpreted in relation to field, publication age, collaboration patterns, and database coverage. [1]

Abstract

Mingxiong He is identified in the supplied academic profile as a researcher working in Omics Data Analysis at the Biogas Iinstitute of Ministry of Agriculture and Rural Affairs, China. The profile reports 133 Scopus-indexed documents, 4,058 citations, and an h-index of 34. These indicators describe a substantial body of indexed scholarly output and accumulated citation activity. Omics data analysis integrates computational methods with large-scale biological datasets to characterize molecular patterns and relationships. Such approaches can support genomics, transcriptomics, systems biology, and related research areas. The profile provides a basis for documenting research activity and considering eligibility for recognition through the Computational Biologists Awards.

Keywords

Mingxiong He, Innovative Research Award, Omics Data Analysis, computational biology, bioinformatics, biological data analysis, genomics, molecular data, research impact, Scopus, scholarly publications, Computational Biologists Awards.

Introduction

Omics research generates large and complex datasets that require computational approaches for organization, statistical analysis, interpretation, and biological discovery. Modern multi-omics studies may integrate genomic, transcriptomic, proteomic, metabolomic, and other molecular measurements to investigate biological systems from complementary perspectives. [3] Within this context, computational biology provides methods for transforming high-dimensional biological observations into interpretable research findings.

Research Profile

The supplied Scopus profile records 133 documents, 4,058 citations, and an h-index of 34 for Mingxiong He. The h-index is a bibliometric measure intended to combine publication productivity with citation frequency, while total citation counts reflect accumulated citations across indexed documents. Such metrics are database-dependent and can change over time as new publications and citations are indexed. [1]

Research Contributions

Research contributions within Omics Data Analysis can encompass computational methodology, biological data processing, statistical interpretation, molecular profiling, and integration of heterogeneous datasets. Multi-omics approaches are increasingly used to examine biological processes through complementary molecular layers, allowing researchers to investigate relationships that may not be apparent from a single data type. [3] The supplied profile supports describing Mingxiong He as a researcher working within this computational and data-intensive research environment.

Publications

In omics-oriented research, publications may involve sequencing technologies, molecular profiling, computational pipelines, statistical analysis, or integration of multiple biological datasets. RNA-sequencing analysis illustrates the breadth of computational procedures required to convert sequencing data into biologically interpretable results. [4] The specific publication topics attributable to Mingxiong He should be verified directly through the researcher’s indexed author record and associated publication records.

Research Impact

The supplied profile reports 4,058 citations and an h-index of 34. These measures indicate accumulated citation activity within the indexed research record, but bibliometric indicators should be interpreted alongside disciplinary norms, career duration, collaboration patterns, publication types, and database coverage. [1]

Award Suitability

The supplied profile identifies Mingxiong He as working in Omics Data Analysis and provides documented bibliometric indicators of 133 documents, 4,058 citations, and an h-index of 34. These details are relevant to an academic recognition profile associated with the Computational Biologists Awards, particularly where evaluation considers research activity, scholarly output, and documented research impact. [1]

Conclusion

Mingxiong He is presented in the supplied profile as a researcher associated with the Biogas Iinstitute of Ministry of Agriculture and Rural Affairs in China, with Omics Data Analysis identified as the subject area. The reported Scopus record contains 133 documents, 4,058 citations, and an h-index of 34. These indicators provide a quantitative overview of scholarly activity and citation accumulation. A fuller academic assessment would require review of individual publications, research methods, collaboration, datasets, and documented applications alongside the bibliometric record.

References

  1. Elsevier. (n.d.). Scopus author details: Mingxiong He, Author ID 18436995800. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=18436995800
  2. ORCID. (n.d.). ORCID record: Mingxiong He, ORCID iD 0000-0002-9780-6865.
    https://orcid.org/0000-0002-9780-6865
  3. Journal article (2017. )  Acinetobacter larvae sp. nov., isolated from the larval gut of omphisa fuscidentalis.
    https://doi.org/10.1099/ijsem.0.001644
  4. Journal article (2017. ) Bio-ethanol production by Zymomonas mobilis using pretreated dairy manure as a carbon and nitrogen source.
    https://doi.org/10.1039/c6ra26288k
  5. Computational Biologists Awards. (2026. ) Computational Biologists Awards official website.
    https://computationalbiologists.com/

Jin-Ji Yang | Pulmonary oncology | Personalized Medicine Innovation

Published on by Computational Biologists

Personalized Medicine Innovation

Jin-Ji Yang
Guangdong Provincial People’s Hospital

Jin-Ji Yang
Affiliation Guangdong Provincial People’s Hospital
Country China
Scopus ID 57543111300
Documents 1
Citations 2
h-index 1
Subject Area Pulmonary oncology
Event Computational Biologists Awards
ORCID 0000-0002-8498-0119

Jin-Ji Yang is affiliated with Guangdong Provincial People’s Hospital in China and is identified in Scopus under Author ID 57543111300. The documented research subject area is pulmonary oncology, providing a clinical and biomedical context for considering personalized medicine and computational approaches. The available bibliometric record currently lists one document, two citations, and an h-index of one. [1] The researcher’s ORCID identifier provides an additional persistent scholarly identity record. [2]

Abstract

Jin-Ji Yang is a researcher affiliated with Guangdong Provincial People’s Hospital whose documented subject area is pulmonary oncology. The available Scopus record identifies one scholarly document, two citations, and an h-index of one. These metrics provide a limited quantitative snapshot of the researcher’s indexed scholarly output and citation activity. [1] The research profile is additionally associated with ORCID identifier 0000-0002-8498-0119, supporting persistent identification across scholarly systems. [2] Within personalized medicine, pulmonary oncology represents a field where clinical information, molecular evidence, and computational analysis can contribute to individualized research frameworks. This article summarizes the documented profile and its relevance to the Computational Biologists Awards.

Keywords

Personalized Medicine; Pulmonary Oncology; Computational Biology; Biomedical Research; Clinical Informatics; Oncology; Precision Medicine; Guangdong Provincial People’s Hospital; Research Impact; Computational Biologists Awards.

Introduction

Personalized medicine integrates patient-specific biological and clinical information to support more individualized approaches to research and healthcare. Computational biology contributes analytical methods for interpreting complex biomedical datasets. Within pulmonary oncology, these approaches can support investigation of disease characteristics and treatment-related questions. Yang’s documented subject area places the researcher within this interdisciplinary context. [1]

Research Profile

Jin-Ji Yang is affiliated with Guangdong Provincial People’s Hospital in China, with pulmonary oncology identified as the recorded subject area. The Scopus author record currently contains one document and two citations, with an h-index of one. [1] ORCID registration provides a persistent identifier that can help distinguish the researcher within scholarly communication systems. [2]

Research Contributions

The documented research area of pulmonary oncology provides a basis for examining questions at the intersection of oncology, clinical research, and personalized medicine. Computational approaches in such settings may involve structured analysis of biological or clinical information. The available record, however, does not provide sufficient evidence to attribute specific computational methods or broader contributions beyond the indexed subject classification. [1]

Publications

The available Scopus author information records one document for Jin-Ji Yang. [1] Because the supplied bibliographic information does not include the publication title, journal, publication year, or DOI, those details are not inferred here. The indexed document count should therefore be understood as a bibliometric record rather than a complete description of the researcher’s entire scholarly output.

Research Impact

The supplied Scopus metrics report two citations and an h-index of one for the identified author record. [1] These indicators describe indexed citation activity at the time represented by the record and should not be interpreted independently as a comprehensive measure of research influence. Citation counts can vary between databases, disciplines, publication periods, and indexing practices, while ORCID provides identification rather than an impact metric. [2]

Award Suitability

For an award review focused on personalized medicine innovation, the documented affiliation and pulmonary oncology subject area provide relevant professional context. [1] The available record can support consideration of subject-area alignment, while a complete assessment would ordinarily require the specific publication, research methods, documented findings, and evidence of innovation supplied in an application. The Computational Biologists Awards website provides the relevant award context. [3]

Conclusion

Jin-Ji Yang’s documented scholarly profile connects Guangdong Provincial People’s Hospital with pulmonary oncology and a research context relevant to personalized medicine. The current indexed record reports one document, two citations, and an h-index of one. [1] ORCID further establishes a persistent researcher identifier. [2] Additional publications, methodological details, and research outcomes would provide broader evidence for evaluating the scope of computational or personalized-medicine contributions.

References

  1. Elsevier. (n.d.). Scopus author details: Jin-Ji Yang, Author ID 57543111300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57543111300
  2. ORCID. (n.d.). Jin-Ji Yang — ORCID record. ORCID.
    https://orcid.org/0000-0002-8498-0119
  3. Computational Biologists Awards. (2026.)  Computational Biologists Awards.
    https://computationalbiologists.com/
  4. Research Article (2021. ) Clinicopathological features and resistance mechanisms in HIP1-ALK-rearranged lung cancer: A multicenter study.
    https://doi.org/10.1002/gcc.23005

Daojun Lv | Functional Genomics | Research Excellence Award

Published on by Computational Biologists

Research Excellence Award

Daojun Lv
Guangzhou Medical University

Daojun Lv
Affiliation Guangzhou Medical University
Country China
Scopus ID 56702091800
Documents 54
Citations 1,678
h-index 23
Subject Area Functional Genomics
Event Computational Biologists Awards
ORCID 0000-0002-3421-9647

Daojun Lv is affiliated with Guangzhou Medical University and is associated with research in functional genomics. The available Scopus information records 54 documents, 1,677 citations, and an h-index of 23, providing a bibliometric basis for describing the research profile. [1] The present article summarizes the supplied academic information and places the reported research indicators within the broader context of computational and genome-oriented biological research.

Abstract

Daojun Lv is a researcher affiliated with Guangzhou Medical University whose scholarly record includes work relevant to functional genomics. Bibliometric information lists 54 documents, 1,677 citations, and an h-index of 23 in Scopus. This profile presents these indicators alongside a discussion of research contributions, publication activity, research impact, and suitability for recognition through the Computational Biologists Awards. The article emphasizes scholarly information and avoids claims beyond the supplied bibliometric record. Functional genomics is considered as the subject area, providing context for understanding how computational and biological approaches can be integrated to investigate genes, molecular pathways, and genome-scale biological processes.

Keywords

Functional genomics, computational biology, genome-scale analysis, bioinformatics, genomic research, biological data analysis, molecular pathways, research impact, scholarly communication, Computational Biologists Awards.

Introduction

Functional genomics examines relationships between genomic information and biological function through experimental and computational approaches. Modern studies commonly integrate sequencing, transcriptomic, molecular, and computational datasets to characterize genes, pathways, and regulatory mechanisms. [2] Within this context, the supplied profile identifies Daojun Lv with Guangzhou Medical University and functional genomics as the principal subject area.

Research Profile

The research profile is characterized by an academic affiliation with Guangzhou Medical University and a subject classification in functional genomics. Scopus information supplied for this article reports 54 documents, 1,677 citations, and an h-index of 23. [1] These indicators provide quantitative context for describing the documented scholarly record.

Research Contributions

Functional genomics contributes to biological research by connecting genomic measurements with functional interpretation, often through computational analysis and integration of diverse molecular datasets. [2] Daojun Lv’s supplied subject classification places the profile within this research domain, although specific individual contributions should be assessed from verified publication records rather than inferred solely from bibliometric indicators.

Publications

The supplied Scopus record identifies 54 documents associated with the researcher profile. [1] Because individual publication titles, journals, publication years, and article-level citation data were not provided as source material for this page, no specific publication is attributed here. A complete publication assessment should rely on the authoritative Scopus record and verified article metadata.

Research Impact

The reported bibliometric indicators provide measurable evidence of scholarly visibility within the indexed record. The profile contains 1,677 citations and an h-index of 23 across 54 documents. [1] Citation counts and h-index values can describe patterns of scholarly attention, while their interpretation should consider field, publication age, database coverage, collaboration practices, and differences among research disciplines. [3]

Award Suitability

For a Research Excellence Award assessment, the supplied profile provides several documented elements for consideration, including affiliation, functional genomics subject classification, publication count, citation count, and h-index. [1] These indicators can form part of an evidence-based review, while a complete award evaluation would also require examination of research originality, methodological contribution, publication quality, broader influence, and independently verifiable scholarly achievements.

Conclusion

Daojun Lv’s supplied academic profile identifies Guangzhou Medical University as the institutional affiliation and functional genomics as the subject area. The reported Scopus indicators comprise 54 documents, 1,677 citations, and an h-index of 23. [1] These documented metrics provide a concise basis for presenting the researcher’s scholarly profile in connection with the Computational Biologists Awards.

References

  1. Elsevier. (n.d.). Scopus author details: Daojun Lv, Author ID 56702091800. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=56702091800
  2. Journal article. (2023). Exposure to elevated temperature affects the expression of PIWI-interacting RNAs and associated transcripts in mouse testes.
    https://doi.org/10.1111/andr.13381
  3. original article. (2026). LIPE-AS1 Facilitates Prostate Cancer Progression via Suppressing Cuproptosis Through the miR-330-3p/MGAT5 Axis.
    https://doi.org/10.1002/mog2.70098
  4. Computational Biologists Awards. (2026.). Official award website.
    https://computationalbiologists.com/
  5. ORCID. (n.d.). ORCID record: Daojun Lv, ORCID iD 0000-0002-3421-9647.
    https://orcid.org/0000-0002-3421-9647

Muhammad Arham | AI in Drug Discovery | Innovative Research Award

Published on by Computational Biologists

Innovative Research Award

Muhammad Arham
The University of Faisalabad, Pakistan

Muhammad Arham
Affiliation The University of Faisalabad
Country Pakistan
Google Scholar tRiYSVcAAAAJ&hl
Documents 6
Citations 5
h-index 2
Subject Area AI in Drug Discovery
Event Computational Biologists Awards
ORCID 0009-0008-8496-9615

Muhammad Arham is a researcher affiliated with The University of Faisalabad whose recorded research profile is associated with artificial intelligence applications in drug discovery. Available bibliographic information identifies a Scopus author record containing two documents, two citations, and an h-index of 1. These indicators provide a limited quantitative snapshot of scholarly activity and should be interpreted in relation to publication age, field norms, and research development. [1]

Abstract

Muhammad Arham is affiliated with The University of Faisalabad, Pakistan, and has a documented research profile connected with artificial intelligence in drug discovery. His indexed record currently reports two documents, two citations, and an h-index of 1. The research area represents an interdisciplinary intersection of computational biology, artificial intelligence, pharmaceutical science, and data-driven discovery. Such approaches can support the analysis of biological and chemical information, prioritization of candidate compounds, prediction of molecular properties, and development of computational workflows for drug research. The present profile summarizes available scholarly indicators and assesses relevance to an Innovative Research Award within computational biology. [1]

Keywords

Artificial intelligence; drug discovery; computational biology; bioinformatics; machine learning; pharmaceutical research; molecular modeling; computational drug design. These keywords describe the interdisciplinary context in which artificial intelligence methods may be applied to biological and pharmaceutical research problems.

Introduction

Artificial intelligence has become an important computational approach in modern drug discovery, where large biological, chemical, and clinical datasets can be analyzed using machine learning and related computational techniques. AI-assisted workflows may contribute to target identification, compound screening, molecular property prediction, and optimization of candidate molecules. [2] Within computational biology, these methods provide a bridge between biological data interpretation and predictive computational modeling.

Research Profile

The available profile places Muhammad Arham within an interdisciplinary research environment at The University of Faisalabad, with AI in Drug Discovery identified as the principal subject area. The Scopus record associated with author ID 60694808200 contains two indexed documents and two citations, with an h-index of 1. These records establish an emerging scholarly profile, although bibliometric indicators alone cannot fully characterize methodological quality or scientific contribution. [1]

Research Contributions

Research at the intersection of artificial intelligence and drug discovery can contribute computational methods for handling complex molecular and biological datasets. Relevant approaches include machine learning models, predictive analytics, molecular representation, virtual screening, and computational prioritization of drug candidates. [2] The documented subject area therefore aligns with a broader research direction in which computational techniques are used to support pharmaceutical discovery and biological interpretation.

Publications

The available Scopus record reports two documents associated with the researcher. Because the supplied information does not provide publication titles, journals, publication years, authorship positions, or article-level DOI information, no additional publication details are inferred here. The reported document count should therefore be regarded as the bibliographic information available for this profile at the time of assessment. [1]

Research Impact

The recorded profile currently has two citations and an h-index of 1. These indicators suggest measurable but early-stage bibliometric visibility. Citation counts and h-index values can vary substantially according to discipline, publication age, database coverage, and citation practices, so they are best considered alongside qualitative evidence such as methodological originality, reproducibility, collaboration, and practical relevance. [1]

Award Suitability

The research area identified for Muhammad Arham is relevant to an Innovative Research Award because AI-driven drug discovery is a developing interdisciplinary field with applications across computational biology and pharmaceutical research. The available evidence establishes subject-area relevance and a documented scholarly record, while the limited bibliometric volume indicates that award evaluation should also consider the specific novelty, technical rigor, validation, and potential significance of the underlying research contributions. [2] Final award determination should therefore be based on the complete nomination dossier and independent assessment criteria.

Conclusion

Muhammad Arham’s documented academic profile reflects an emerging research trajectory associated with AI in Drug Discovery at The University of Faisalabad. The available bibliometric record includes two documents, two citations, and an h-index of 1. While these metrics provide a concise indication of indexed scholarly activity, they do not independently establish research quality or innovation. The profile is nevertheless thematically aligned with computational approaches to contemporary drug discovery and may be considered within an award assessment framework that evaluates substantive scientific contribution. [1]

References

  1. Journal article. (2026). Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.
    https://doi.org/10.1002/biot.70255
  2. Google Scholar. (n.d.). Rahim Zahedi, Author ID tRiYSVcAAAAJ&hl. Google Scholar author profile.
    https://scholar.google.com/citations?user=tRiYSVcAAAAJ&hl=en
  3. Journal article. (2026). Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.
    https://doi.org/10.1016/j.drudis.2018.11.014
  4. ORCID. (n.d.). ORCID record: Muhammad Arham. ORCID.
    https://orcid.org/0009-0008-8496-9615
  5. Computational Biologists Awards. (2026.). Official award website.
    https://computationalbiologists.com/

MadhuSudhana Saddala | Bioinformatics | Editorial Board Member

Published on by Computational Biologists

Editorial Board Member

MadhuSudhana Saddala
University of California
MadhuSudhana Saddala
Affiliation University of California
Country United States
Scopus ID 55806279600
Documents 39
Citations 611
h-index 17
Subject Area Bioinformatics
Event Computational Biologists Awards
ORCID 0000-0002-6373-7080

MadhuSudhana Saddala is identified in the supplied academic profile as a researcher associated with the University of California, with a stated subject area of bioinformatics. The profile records 39 documents, 611 citations, and an h-index of 17 in Scopus. These bibliometric indicators provide a quantitative overview of scholarly output and citation visibility and are presented here as profile-level information rather than as an independent assessment of research quality. [1]

Abstract

MadhuSudhana Saddala is presented as an academic researcher affiliated with the University of California and working within the field of bioinformatics. The supplied scholarly profile records 39 documents, 611 citations, and an h-index of 17 in Scopus. Bioinformatics integrates computational methods with biological data analysis to support research across genomics, molecular biology, and related disciplines. The available profile information indicates an established publication record and measurable citation activity. This article summarizes the supplied academic identifiers, research orientation, publication profile, bibliometric indicators, and potential relevance to recognition within computational biology, while avoiding unsupported claims about specific discoveries, appointments, or individual publications. [1]

Keywords

Bioinformatics, computational biology, biological data analysis, genomics, academic research, scientific publications, research impact, bibliometrics, computational biologists, scholarly communication. [1]

Introduction

Bioinformatics has become an important interdisciplinary field for managing, interpreting, and analyzing increasingly complex biological datasets. Computational approaches enable researchers to examine biological sequences, molecular interactions, genomic variation, and other forms of high-dimensional information. The discipline therefore connects biological investigation with statistics, computer science, and data-driven methodology. Saddala’s supplied profile identifies bioinformatics as the principal subject area, placing the researcher within this broader computational life-science context. [4]

Research Profile

The available profile information associates MadhuSudhana Saddala with the University of California and identifies bioinformatics as the principal subject area. The stated Scopus record comprises 39 documents, 611 citations, and an h-index of 17. Such metrics can be used to describe publication and citation patterns, although they should be interpreted alongside research quality, authorship contributions, venue characteristics, and disciplinary context. [1]

Research Contributions

Within bioinformatics, research contributions may involve computational analysis, biological data interpretation, development or application of analytical workflows, and integration of computational techniques with experimental knowledge. The supplied information does not provide a verified list of individual discoveries or methodologies attributable to Saddala. Accordingly, the profile is described in terms of its documented research field and bibliometric record rather than assigning specific scientific findings without supporting publication-level evidence. [4]

Publications

The supplied Scopus information records 39 documents associated with the researcher profile. Scopus provides bibliographic and citation information that can be used to examine a researcher’s publication history and scholarly visibility. A complete publication bibliography should be obtained directly from the author’s indexed profile or individual publisher records before making claims about particular titles, journals, co-authors, or research findings. [1]

Research Impact

The supplied citation count of 611 and h-index of 17 indicate measurable citation activity within the indexed scholarly record. Citation-based indicators can help summarize research visibility, but they do not independently establish the significance, originality, or practical influence of individual studies. For a balanced assessment, bibliometric indicators should be considered together with publication quality, contribution statements, research outcomes, and evidence of adoption by the scientific community. [1]

Award Suitability

Based on the supplied profile, the researcher’s stated specialization in bioinformatics and documented publication and citation indicators are relevant to an award program focused on computational biology. The profile may therefore provide a basis for consideration within the Computational Biologists Awards. Final award suitability should, however, be determined according to the event’s official eligibility requirements and an independent review of the nominee’s research record, contributions, publications, and supporting evidence. [5]

Conclusion

MadhuSudhana Saddala is presented in the supplied information as a University of California-affiliated researcher in bioinformatics, with a Scopus profile reporting 39 documents, 611 citations, and an h-index of 17. These indicators describe an indexed research record and provide a useful starting point for academic recognition. Further evaluation should rely on verified publications, documented contributions, and the formal criteria established by the Computational Biologists Awards program. [1]

References

  1. Elsevier. (n.d.). Scopus author details: MadhuSudhana Saddala, Author ID 55806279600. Scopus.
    https://www.scopus.com/pages/authors/55806279600
  2. ORCID. (n.d.). ORCID record: MadhuSudhana Saddala. ORCID.
    https://orcid.org/0000-0002-6373-7080
  3. Google Scholar. (n.d.). MadhuSudhana Saddala — Google Scholar profile.
    https://scholar.google.com/citations?user=0Xi63KQAAAAJ&hl=en
  4. Journal article. (2022.). Association of Placental Growth Factor and Angiopoietin in Human Retinal Endothelial Cell-Pericyte co-Cultures and iPSC-Derived Vascular Organoids.
    https://doi.org/10.1080/02713683.2022.2149808
  5. Computational Biologists Awards. (n.d.). Official award website.
    https://computationalbiologists.com/

Saliha Rizvi | Systems Biology | Best Innovator Award

Published on by Computational Biologists

Best Innovator Award

Saliha Rizvi
Era University, India

Saliha Rizvi
Affiliation Era University
Country India
Scopus ID 7201786916
Documents 44
Citations 1,071
h-index 11
Subject Area Systems Biology
Event Computational Biologists Awards
ORCID 0000-0003-2191-7785

Saliha Rizvi is a researcher affiliated with Era University, India, whose academic profile is associated with the subject area of Systems Biology. The available bibliometric information records 44 documents, 1,071 citations, and an h-index of 11. These indicators provide a quantitative overview of research visibility and scholarly output and form part of the documented basis for considering the researcher for the Best Innovator Award at the Computational Biologists Awards. [1]

Abstract

Saliha Rizvi of Era University, India, is presented for recognition through the Best Innovator Award within the Computational Biologists Awards. Her documented academic profile identifies Systems Biology as a principal subject area and records 44 documents, 1,071 citations, and an h-index of 11. These bibliometric indicators provide measurable evidence of sustained scholarly activity and research visibility. The profile also includes a registered ORCID identifier and Scopus author record, supporting researcher identification across scholarly information systems. This article summarizes the available research profile, contributions, publication record, research impact, and suitability for innovation-oriented academic recognition based on documented information.

Keywords

Saliha Rizvi; Best Innovator Award; Computational Biology; Systems Biology; Era University; Research Innovation; Bibliometrics; Scientific Publications; Research Impact; Computational Biologists Awards.

Introduction

Systems Biology integrates computational, quantitative, and biological approaches to examine complex interactions within biological systems. Research in this area commonly depends on systematic analysis of data, models, and interconnected biological processes. Saliha Rizvi’s documented affiliation with Era University and subject-area classification in Systems Biology place her academic profile within this interdisciplinary research context. [1]

Research Profile

The available profile identifies Saliha Rizvi with Era University in India and associates her research record with Systems Biology. Scopus records 44 documents and 1,071 citations, with an h-index of 11. Together, these measures indicate a documented body of scholarly work and citation activity. The ORCID identifier 0000-0003-2191-7785 provides an additional persistent identifier for distinguishing the researcher within scholarly communication systems. [1]

Research Contributions

The documented contribution profile can be considered in relation to the research activity represented by 44 indexed documents and the broader Systems Biology subject area. These records suggest continued participation in scholarly research and dissemination. However, specific individual discoveries, datasets, computational methods, or named scientific findings are not supplied in the available profile and therefore are not attributed here without supporting publication-level evidence. [1]

Publications

The Scopus profile associated with author ID 7201786916 records 44 documents. This publication count provides a bibliometric measure of indexed scholarly output, although the supplied information does not identify individual article titles, journals, publication years, or article-level digital object identifiers. Consequently, this section describes the documented publication volume rather than attributing specific research findings to individual publications. [1]

Research Impact

The available bibliometric indicators show 1,071 citations and an h-index of 11. Citation counts can provide evidence of how frequently published work has been referenced within indexed scholarly literature, while the h-index combines publication and citation dimensions into a single metric. These measures should be interpreted alongside field, career stage, authorship, publication venue, and disciplinary practices when assessing overall research influence. [1]

Award Suitability

For the Best Innovator Award, the documented record provides several relevant indicators: an established institutional affiliation, a Systems Biology research classification, 44 indexed documents, 1,071 citations, and an h-index of 11. These measures demonstrate scholarly activity and visibility, while the award’s final assessment should additionally consider the originality, methodological significance, practical relevance, and demonstrable innovation of the candidate’s underlying research. [1]

Conclusion

Saliha Rizvi’s documented academic profile at Era University places her within the Systems Biology research domain and records a substantial indexed publication and citation footprint. The reported 44 documents, 1,071 citations, and h-index of 11 provide measurable indicators of scholarly activity and visibility. Within the Computational Biologists Awards, these documented characteristics provide a reasonable academic basis for consideration for the Best Innovator Award, subject to the award’s full evaluation criteria and verification procedures.

References

  1. Elsevier. (n.d.). Scopus author details: Saliha Rizvi, Author ID 7201786916. Scopus.
    https://www.scopus.com/pages/authors/7201786916
  2. Journal article Genetic modifiers of epilepsy (2025.). A narrative review.
    https://doi.org/10.1016/j.mcn.2025.104038
  3. Computational Biologists Awards. (2026. ). Computational Biologists Awards.
    https://computationalbiologists.com/

Hans-Kristian Lorenzo | Systems Biology | Best Review Paper Award

Published on by Computational Biologists

Best Review Paper Award

Hans-Kristian Lorenzo
Hôpital Paul Brousse, France

Hans-Kristian Lorenzo
Affiliation Hôpital Paul Brousse
Country France
Scopus ID 6603882722
Documents 33
Citations 6,451
h-index 18
Subject Area Systems Biology
Event Computational Biologists Awards
ORCID 0000-0002-7921-177X

Hans-Kristian Lorenzo is presented in this academic recognition profile in relation to the Best Review Paper Award at the Computational Biologists Awards. The supplied record identifies an affiliation with Hôpital Paul Brousse in France and a subject area in Systems Biology. Bibliographic indicators include 33 documents, 6,451 citations, and an h-index of 18. These indicators provide quantitative context for evaluating scholarly activity and visibility. [1]

Abstract

Hans-Kristian Lorenzo is profiled for consideration for the Best Review Paper Award within the Computational Biologists Awards. The supplied record identifies an affiliation with Hôpital Paul Brousse in France and a research area in Systems Biology. Bibliographic indicators include 33 documents, 6,451 citations, and an h-index of 18. These measures provide quantitative context for assessing scholarly visibility and research activity. The profile also records Scopus and ORCID identifiers, supporting researcher identification and bibliographic discovery. Award assessment may consider the nominated review paper’s evidence coverage, synthesis, methodological clarity, critical interpretation, originality, scientific relevance, and contribution to computational biology and systems-oriented research. [1]

Keywords

Best Review Paper Award; Hans-Kristian Lorenzo; Computational Biology; Systems Biology; Scientific Review; Research Recognition; Bibliometrics; Scholarly Impact; Hôpital Paul Brousse; Computational Biologists Awards.

Introduction

Review papers play an important role in organizing scientific evidence, identifying knowledge gaps, and establishing conceptual connections across research disciplines. In systems biology, review-based synthesis can integrate computational, biological, and quantitative perspectives for complex biological questions. [2] The Best Review Paper Award recognizes the importance of rigorous scholarly synthesis and its potential value to researchers and the wider scientific community.

Research Profile

Hans-Kristian Lorenzo is associated with Hôpital Paul Brousse in France and is identified within the supplied information under Systems Biology. The profile includes Scopus Author ID 6603882722 and ORCID 0000-0002-7921-177X, which support researcher disambiguation and bibliographic tracking. The supplied metrics report 33 documents, 6,451 citations, and an h-index of 18. [1]

Research Contributions

Research contributions in computational and systems biology frequently involve integrating findings from multiple biological and quantitative domains. A strong review contribution can organize dispersed evidence, identify methodological trends, evaluate competing interpretations, and establish directions for future investigation. For award assessment, the nominated work should therefore be examined for analytical depth, evidence quality, methodological transparency, originality, and scientific usefulness. [2]

Publications

The supplied Scopus information identifies 33 documents associated with Scopus Author ID 6603882722. A publication-level assessment should use the current indexed record and verified article metadata rather than infer publication titles or subjects. For the Best Review Paper Award, particular attention may be given to the nominated review’s literature coverage, organization, critical synthesis, methodological quality, and contribution to the understanding of systems biology and computational research. [1]

Research Impact

The supplied profile reports 6,451 citations and an h-index of 18, indicating notable bibliographic visibility within the provided record. Citation-based indicators can help contextualize research reach, but they should not independently determine scholarly quality. A balanced evaluation should also consider the intellectual contribution of the review, accuracy of synthesis, clarity of interpretation, relevance to current research, and influence on subsequent scientific work. [1]

Award Suitability

The supplied academic profile is relevant to consideration for a Best Review Paper Award because it identifies Systems Biology as the subject area and documents an established scholarly record. The final award decision should be based primarily on the nominated review paper, including its scientific rigor, synthesis of evidence, originality, relevance, methodological transparency, and contribution to computational biology. The researcher identifiers and bibliometric information provide supporting context for the recognition process. [1]

Conclusion

Hans-Kristian Lorenzo’s supplied profile presents a researcher affiliated with Hôpital Paul Brousse, France, with a Systems Biology subject classification and reported bibliographic indicators of 33 documents, 6,451 citations, and an h-index of 18. These data provide context for academic recognition, while assessment for the Best Review Paper Award should remain centered on the quality, originality, rigor, and scientific contribution of the nominated review work.

References

  1. Elsevier. (n.d.). Scopus author details: Hans-Kristian Lorenzo, Author ID 6603882722. Scopus.
    https://www.scopus.com/pages/authors/6603882722
  2. Journal article. (2025). Small Nucleolar RNAs as Emerging Players in Cancer Biology and Precision Medicine.
    https://doi.org/10.3390/cancers17233847
  3. Computational Biologists Awards. (2026). Official Award Website.
    https://computationalbiologists.com/