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

Eva Falkensammer | Digital Health and Computational Biology | Innovative Research Award

Published on by Computational Biologists

Innovative Research Award

Eva Falkensammer
Paracelsus Medizinische Universität, Austria

Eva Falkensammer
Affiliation Paracelsus Medizinische Universität
Country Austria
Scopus ID 57073845600
Documents 11
Citations 732
h-index 6
Subject Area Digital Health and Computational Biology
Event Computational Biologists Awards
ORCID 0000-0002-8340-446X

Eva Falkensammer is a researcher affiliated with Paracelsus Medizinische Universität in Austria whose documented scholarly profile is situated within the intersection of digital health and computational biology. The available bibliometric information records 11 documents, 732 citations, and an h-index of 6 in Scopus. These indicators provide a quantitative context for assessing the visibility and scholarly influence of the research record, while the stated subject area provides a basis for considering its relevance to computational approaches in contemporary biomedical research. [1]

Abstract

Eva Falkensammer is a researcher at Paracelsus Medizinische Universität, Austria, whose academic profile is associated with digital health and computational biology. Available Scopus information identifies 11 documents, 732 citations, and an h-index of 6, indicating a research record with measurable scholarly visibility. Her profile reflects the growing integration of computational methods with biomedical and health-related research, where digital approaches can support data interpretation, evidence generation, and scientific collaboration. The Innovative Research Award recognizes research profiles demonstrating meaningful contributions to computationally informed scientific inquiry and innovation within contemporary biological and health sciences. [1]

Keywords

Eva Falkensammer; Innovative Research Award; Computational Biology; Digital Health; Biomedical Research; Bioinformatics; Health Data; Computational Methods; Research Impact; Paracelsus Medizinische Universität.

Introduction

Computational biology and digital health increasingly depend on the systematic analysis of complex biological and clinical information. Researchers working across these fields contribute to methodological development, data interpretation, and the translation of computational evidence into biomedical contexts. Within this landscape, Falkensammer’s documented subject classification places her scholarly profile within an interdisciplinary research environment connecting computational science and health research. [2]

Research Profile

Falkensammer’s profile is characterized by its association with Digital Health and Computational Biology at Paracelsus Medizinische Universität. The available bibliometric record comprises 11 documents and 732 citations, with an h-index of 6. These data indicate a sustained scholarly presence and provide measurable evidence for evaluating the reach of the documented publication record. [1]

Research Contributions

The stated subject area suggests contributions positioned at the interface of computational biology and digital health. Such interdisciplinary research can involve computational analysis of biomedical information, digital approaches to health research, and the application of quantitative methods to biological questions. Because detailed publication-level findings are not supplied in the available profile data, specific methodological or experimental contributions should be interpreted conservatively. [3]

Publications

The Scopus profile associated with author ID 57073845600 records 11 documents for Eva Falkensammer. The available information does not provide a complete publication bibliography, individual article titles, journal information, or article-level citation data. Consequently, the publication record is best described using the verified aggregate indicators supplied for this recognition profile rather than attributing specific findings or papers without supporting bibliographic evidence. [1]

Research Impact

The reported 732 citations and h-index of 6 provide quantitative indicators of scholarly impact within the indexed research literature. Citation measures are useful for describing research visibility, although they should not be interpreted independently of disciplinary context, publication age, collaboration patterns, and differences in citation practices between fields. In this profile, the indicators therefore serve as supporting evidence rather than as a standalone measure of research quality. [1] [4]

Award Suitability

The profile is suitable for consideration for an Innovative Research Award because its documented subject area directly corresponds with computational biology and digital health, two fields in which computational approaches play an important role in contemporary biomedical research. The combination of an identifiable institutional affiliation, 11 indexed documents, 732 citations, and an h-index of 6 provides an objective bibliometric basis for recognition, while final award decisions should also consider the originality, methodological significance, and practical or scientific contribution of the underlying research. [1] [3]

Conclusion

Eva Falkensammer’s documented academic profile reflects engagement with Digital Health and Computational Biology at Paracelsus Medizinische Universität. The available bibliometric record of 11 documents, 732 citations, and an h-index of 6 supports the assessment of an established scholarly presence. On the evidence supplied, the profile demonstrates relevance to an Innovative Research Award focused on computationally informed biological and health research, while detailed assessment of research originality should be based on the underlying publications and contributions. [1]

References

  1. Elsevier. (n.d.). Scopus author details: Eva Falkensammer, Author ID 57073845600. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57073845600
  2. Journal article. (2026.). Development of DEEP-URO, a Generic Research Tool for Enhancing Antimicrobial Stewardship in a Surgical Specialty.
    https://doi.org/10.3390/antibiotics15010074
  3. Journal article. (2024. ) Systematic Review and Meta-Analysis Provide no Guidance on Management of Asymptomatic Bacteriuria within the First Year after Kidney Transplantation.
    https://doi.org/10.3390/antibiotics13050442
  4. Computational Biologists Awards. (2026.). Official award website.
    https://computationalbiologists.com/
  5. ORCID. (n.d.). ORCID record: Eva Falkensammer, 0000-0002-8340-446X. ORCID.
    https://orcid.org/0000-0002-8340-446X

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/

David Hong | Cancer Genomics Computational Studies | Best Researcher Award

Published on by Computational Biologists

Best Researcher Award

David Hong
MD Anderson Cancer Center, United States

David Hong
Affiliation MD Anderson Cancer Center
Country United States
Scopus ID 34770051000
Documents 560
Citations 39,661
h-index 100
Subject Area Cancer Genomics Computational Studies
Event Computational Biologists Awards
ORCID 0000-0001-8721-1609

David S. Hong is a professor in Investigational Cancer Therapeutics at The University of Texas MD Anderson Cancer Center. His documented research profile encompasses oncology, early-phase clinical trials, molecularly informed cancer treatment, and translational investigation. His ORCID record identifies the same Scopus Author ID supplied for this profile and lists phase I clinical trials, oncology, and cancer among his research keywords. [1] His published work also includes studies involving clinical next-generation sequencing and genomic characterization intended to support precision oncology. [2]

Abstract

David S. Hong is a physician-scientist and professor at The University of Texas MD Anderson Cancer Center whose research integrates investigational therapeutics, molecular oncology, precision medicine, and cancer genomics. His scholarly record includes clinical studies examining genomic alterations, targeted therapies, immunotherapy, and early-phase therapeutic development. His ORCID profile associates him with phase I clinical trials, oncology, and cancer, while published studies demonstrate participation in genomic profiling and molecular characterization of tumors. [1] [2] These activities provide a substantive basis for consideration within a research recognition framework focused on computationally informed cancer biology.

Keywords

Cancer genomics, computational oncology, precision medicine, molecular oncology, next-generation sequencing, investigational therapeutics, phase I clinical trials, targeted therapy, translational research, genomic profiling.

Introduction

Computational approaches increasingly connect genomic measurements with clinical decision-making in precision oncology. Within this research environment, David S. Hong has contributed to investigations in which molecular profiling, cancer biology, and therapeutic development intersect. His institutional profile identifies him as a professor in Investigational Cancer Therapeutics at MD Anderson Cancer Center. [3] His publication record includes research addressing actionable genomic alterations and molecular characterization of advanced cancers, supporting the relevance of his work to data-driven cancer research. [2]

Research Profile

Hong’s research profile is centered on investigational cancer therapeutics and the molecular basis of cancer, with particular relevance to early-phase clinical development. His ORCID record identifies phase I clinical trials, oncology, and cancer as keywords, while institutional information places his work within Investigational Cancer Therapeutics. [1] [3] This combination creates a translational research pathway in which molecular evidence can inform therapeutic hypotheses, clinical trial design, and assessment of emerging treatment strategies.

Research Contributions

A significant component of Hong’s documented research involves the use of molecular and genomic information in oncology. A study of clinical next-generation sequencing in a phase I program examined recurrent hotspot mutations across cancer-related genes and was associated with Bioinformatics & Computational Biology and Systems Biology research areas at MD Anderson. [2] Other work has addressed comprehensive molecular characterization of KRAS G12C-mutant colorectal cancer, demonstrating the relevance of genomic alterations to precision oncology research. [4]

Publications

Hong has participated in peer-reviewed research covering clinical next-generation sequencing, genomic profiling, molecularly defined cancers, targeted treatment, and investigational therapeutics. His ORCID record currently lists hundreds of works in its broader publication record, including recent articles and contributions indexed through Crossref. [1] A representative genomic study, “Clinical Next-Generation Sequencing for Precision Oncology in Rare Cancers,” reported the application of sequencing approaches to precision oncology and included Hong among its authors. [5]

Research Impact

The supplied bibliometric profile reports 560 documents, 39,661 citations, and an h-index of 100. These figures are presented here as supplied profile data rather than independently recalculated metrics. The underlying research record demonstrates engagement with genomic characterization, molecular profiling, and therapeutic development, areas that contribute to the broader evidence base for precision cancer medicine. [2] [5] Recent institutional and ORCID records also confirm an ongoing research role at MD Anderson. [1] [3]

Award Suitability

For the Best Researcher Award associated with the Computational Biologists Awards, Hong’s profile is relevant through its intersection of cancer genomics, computationally supported molecular research, precision oncology, and translational therapeutics. His documented participation in studies using next-generation sequencing and genomic characterization provides direct subject-area relevance, while his institutional position and publication record indicate sustained engagement with cancer research. [2] [3] Final award decisions should remain subject to the organizer’s formal evaluation criteria and independent verification of submitted bibliometric information.

Conclusion

David S. Hong’s documented research connects investigational cancer therapeutics with molecular oncology and genomic approaches to precision medicine. Evidence from institutional, ORCID, and scholarly sources supports his involvement in clinical and translational cancer research, including genomic profiling and molecular characterization. [1] [3] On the basis of the supplied profile information, his research trajectory presents a credible alignment with a recognition category emphasizing sustained scholarly contribution and computationally informed cancer research.

References

  1. Elsevier. (n.d.). Scopus author details: David Hong, Author ID 34770051000. Scopus.
    https://orcid.org/0000-0001-8721-1609
  2. cancer Resear. (2024). Abstract 1250: Activating PIK3CA mutations and hedgehog signaling may confer resistance to KRAS inhibition in colorectal cancer.
    https://doi.org/10.1158/1538-7445.AM2024-1250
  3. Clinical cancer Resear .(2010). A First-in-Human Study of Conatumumab in Adult Patients with Advanced Solid Tumors.
    https://doi.org/10.1158/1078-0432.CCR-10-0631
  4. ORCID. (n.d.). ORCID record for David Hong.
    https://orcid.org/0000-0001-8721-1609
  5. Computational Biologists Awards. (2026). Official Award Website.
    https://computationalbiologists.com/

Suresh Kumar Balasubramanian | Biological Data Science | Research Excellence Award

Published on by Computational Biologists

Research Excellence Award

Suresh Kumar Balasubramanian
Wayne State University

Suresh Kumar Balasubramanian
Affiliation Wayne State University
Country United States
Scopus ID 57117214200
Documents 36
Citations 1,335
h-index 19
Subject Area Biological Data Science
Event Computational Biologists Awards
ORCID 0000-0001-9044-1309

Suresh Kumar Balasubramanian is a researcher affiliated with Wayne State University whose supplied academic profile is associated with Biological Data Science. The reported record contains 36 documents, 1,335 citations, and an h-index of 19. These bibliometric indicators provide a quantitative context for understanding the research profile and are subject to change as indexing databases are updated.
[1]

Abstract

This academic recognition profile presents Suresh Kumar Balasubramanian in connection with the Research Excellence Award and Computational Biologists Awards. He is identified with Wayne State University in the United States and the subject area of Biological Data Science. The supplied bibliometric record reports 36 documents, 1,335 citations, and an h-index of 19. The profile summarizes his researcher identity, scholarly publication activity, bibliometric visibility, and relevance to computational biology. These details provide a structured academic overview based on the supplied information, while recognizing that researcher identifiers, publication records, citation counts, and institutional affiliations should be independently verified through authoritative databases and official sources.
[1]

Keywords

Biological Data Science, Computational Biology, Bioinformatics, Research Excellence, Biomedical Data, Research Impact, Bibliometrics, Computational Biologists Awards.

Introduction

Computational biology combines biological investigation with computational, statistical, and data-driven methods to examine complex scientific problems. Researcher profiles and bibliometric databases provide structured information that can assist in describing scholarly activity, publication output, and citation visibility. [2]

Research Profile

Suresh Kumar Balasubramanian is identified as being affiliated with Wayne State University and associated with Biological Data Science. The supplied researcher identifiers include Scopus Author ID 57117214200 and ORCID 0000-0001-9044-1309. Persistent identifiers such as ORCID can support reliable attribution of scholarly work, while indexing identifiers can provide access to publication and citation information maintained by relevant databases. [1] [3]

Research Contributions

The research profile is situated within Biological Data Science, an interdisciplinary area that applies computational and analytical approaches to biological information. Such research can involve the organization, analysis, interpretation, and integration of biological datasets. The supplied information establishes the researcher’s broad subject-area association but does not provide sufficient evidence to attribute particular discoveries or methodologies beyond the documented profile.

Publications

The supplied Scopus information reports 36 documents associated with the researcher profile. Publication counts depend on database coverage, document classification, author disambiguation, and indexing practices. Accordingly, the complete publication record should be examined through the corresponding author profile when specific publication titles, journals, publication dates, or co-authorship information are required.
[1]

Research Impact

The reported citation count of 1,335 and h-index of 19 provide quantitative indicators of scholarly visibility within the supplied bibliometric record. The h-index was introduced as a measure combining publication productivity with citation impact, although it should not be treated as a complete measure of research quality or broader academic contribution. [5]

Award Suitability

The supplied academic profile is relevant to the Computational Biologists Awards because the stated subject area is Biological Data Science and the profile documents scholarly activity in an interdisciplinary computational research context. The combination of institutional affiliation, researcher identifiers, publication activity, citation indicators, and research-area classification provides information that may be considered in an academic recognition process. Final eligibility and selection remain subject to the official requirements and evaluation procedures of the award organizer. [4]

Conclusion

Suresh Kumar Balasubramanian is presented as a Wayne State University researcher associated with Biological Data Science and computationally oriented biological research. The supplied record reports 36 documents, 1,335 citations, and an h-index of 19. Together with the listed Scopus and ORCID identifiers, these details provide a structured basis for an academic recognition profile while allowing readers to verify the information through the relevant official researcher and indexing sources.
[1]

References

  1. Elsevier. (n.d.). Scopus author details: Suresh Kumar Balasubramanian, Author ID 57117214200. Scopus.
    https://www.scopus.com/pages/authors/57117214200
  2. Journal article . (2025.). Clinical and Molecular Characterization of Myeloid Sarcoma: A Systematic Review and Meta-Analysis.
    https://doi.org/10.3390/cancers17243975
  3. Journal article. (2025. ). Molecular Insights and Therapeutic Advances in Low-Risk Myelodysplastic Neoplasms: A Clinical Review.
    https://doi.org/10.3390/cancers17223610
  4. ORCID. (n.d.). ORCID record for Suresh Kumar Balasubramanian.
    https://orcid.org/0000-0001-9044-1309
  5. Computational Biologists Awards. (2026). Official Award Website.
    https://computationalbiologists.com/

Mehrnaz Hatami | Computational Synthetic Biology | Innovative Research Award

Published on by Computational Biologists

Innovative Research Award

Mehrnaz Hatami
Department of Medicinal Plants, Iran

Mehrnaz Hatami
Affiliation Department of Medicinal Plants
Country Iran
Scopus ID 55221393500
Documents 92
Citations 2,484
h-index 29
Google Scholar zdp8nHkAAAAJ&hl
Subject Area Computational Synthetic Biology
Event Computational Biologists Awards
ORCID 0000-0002-7873-144X

The Innovative Research Award recognizes research profiles demonstrating interdisciplinary scholarship, methodological development, and meaningful contributions to contemporary scientific research. Mehrnaz Hatami is associated with the Department of Medicinal Plants in Iran and is identified in the supplied profile with the subject area of Computational Synthetic Biology. The profile reflects an interdisciplinary connection between computational methods, biological research, and medicinal plant science. [1]

Abstract

Mehrnaz Hatami is recognized under the Innovative Research Award for research associated with Computational Synthetic Biology and medicinal plant science. Her supplied profile reflects interdisciplinary work combining computational approaches with biological research. [2]

Keywords

Computational Synthetic Biology, Computational Biology, Synthetic Biology, Medicinal Plants, Bioinformatics, Systems Biology, Biotechnology, Research Innovation.

Introduction

The integration of computational methods with medicinal plant research creates opportunities for investigating biological characteristics through data-driven approaches. Computational biology can support sequence analysis, molecular modelling, pathway investigation, and systematic interpretation of biological information. This interdisciplinary framework provides the scientific context for considering the supplied research profile within the Computational Biologists Awards. [3]

Research Profile

The supplied profile identifies Mehrnaz Hatami with the Department of Medicinal Plants in Iran and lists Computational Synthetic Biology as the principal subject area. The provided bibliometric information records 92 documents, 2,484 citations, and an h-index of 29. These indicators provide quantitative context regarding the supplied scholarly profile and should be considered together with publication quality, research contribution, methodology, authorship, and scientific relevance. [4]

Research Contributions

Research in Computational Synthetic Biology can contribute to biological modelling, computational analysis, biological system design, data integration, and interpretation of molecular information. Computational methods provide structured approaches for examining relationships among biological components and can assist researchers in developing hypotheses for experimental investigation. These capabilities are increasingly important within interdisciplinary bioscience research. [3]

Publications

For detailed scholarly evaluation, publication records should be assessed according to article-level contribution, research methodology, journal context, authorship, citation performance, reproducibility, and scientific significance. Bibliometric indicators can provide useful context, but they should not be treated as a standalone measure of research quality. [5]

Research Impact

The supplied profile reports 2,484 citations and an h-index of 29. These indicators provide quantitative information concerning citation activity associated with the supplied profile. Citation patterns can vary among disciplines and publication types, so bibliometric information is most appropriately considered alongside qualitative evidence concerning originality, methodological contribution, scientific influence, and reproducibility. [5]

Award Suitability

The supplied profile is relevant to consideration for the Innovative Research Award because it identifies Computational Synthetic Biology as the principal subject area and reports substantial scholarly activity. The combination of computational approaches, biological research, and medicinal plant affiliation is consistent with the interdisciplinary scope commonly associated with computational biology. Final award assessment should nevertheless be based on official award criteria and independently verified scholarly evidence. [1]

Conclusion

Mehrnaz Hatami’s supplied academic profile represents a researcher associated with the Department of Medicinal Plants in Iran and the subject area of Computational Synthetic Biology. The reported documents, citations, and h-index provide quantitative context for the profile, while the interdisciplinary subject classification connects computational methodology with biological research. The Innovative Research Award provides a recognition framework for evaluating such interdisciplinary research, subject to verification against official award requirements and authoritative scholarly records. [4]

References

  1. Elsevier. (n.d.). Scopus author details: Mehrnaz Hatami, Author ID 57207830108. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=55221393500
  2. Journal article. (2022.). Morphological diversity, phenolic acids, and antioxidant properties in eryngo (Eryngium caucasicum Trautv): Selection of superior populations for agri-food industry.
    https://doi.org/10.1002/fsn3.2987
  3. Journal article. (2022.).RETRACTED: Foliar-applied nanoscale zero-valent iron (nZVI) and iron oxide (Fe3O4) induce differential responses in growth, physiology, antioxidative defense and biochemical indices in Leonurus cardiaca L.
    https://doi.org/10.1016/j.envres.2022.114254
  4. ORCID. (n.d.).Mehrnaz Hatami ORCID record. ORCID.
    https://orcid.org/0000-0002-7873-144X
  5. Computational Biologists Awards. (2026). Official Award Website.
    https://computationalbiologists.com/

 

Natalia Tavares | Epigenomic Data Integration | Research Excellence Award

Published on by Computational Biologists

 

Research Excellence Award

Natalia Tavares
Oswaldo Cruz Foundation, Brazil

Natalia Tavares
Affiliation Oswaldo Cruz Foundation
Country Brazil
Google Scholar 3yhepgMAAAAJ
Scopus ID 57207830108
Documents 1
Citations 17
h-index 1
Subject Area Epigenomic Data Integration
Event Computational Biologists Awards
ORCID 0000-0002-4026-679X

Natalia Tavares is a researcher affiliated with the Oswaldo Cruz Foundation in Brazil whose documented scholarly profile includes work associated with epigenomic data integration. Her research profile is considered in the context of computational biology, where integrated analysis of biological datasets can support the interpretation of molecular mechanisms and complex biological systems. The Research Excellence Award recognizes scholarly contributions according to documented research activity, publication evidence, research relevance, and broader academic contribution.
[1]

Abstract

Natalia Tavares is affiliated with the Oswaldo Cruz Foundation in Brazil and is associated with research involving epigenomic data integration. Her documented scholarly profile provides evidence of research activity relevant to computational biology and the analysis of complex biological datasets. Integration of epigenomic information can contribute to understanding molecular regulation, biological variation, and relationships among genomic and regulatory processes. This article presents a neutral academic profile for recognition through the Research Excellence Award under the Computational Biologists Awards event. It summarizes available researcher information, research orientation, publication record, citation indicators, research impact, and suitability within the computational biology field.
[1]

Keywords

Natalia Tavares; Oswaldo Cruz Foundation; Brazil; computational biology; epigenomic data integration; bioinformatics; genomic data analysis; biological data integration; Research Excellence Award; Computational Biologists Awards.

Introduction

Computational biology combines biological research with computational methods to process, interpret, and integrate complex datasets. Epigenomic research is particularly dependent on computational approaches because regulatory information may be derived from multiple experimental and molecular data sources. Integrated analysis can help researchers identify relationships between genomic features and regulatory states and can provide a structured basis for biological interpretation
[2].

Research Profile

The available researcher information identifies Natalia Tavares with the Oswaldo Cruz Foundation in Brazil and associates her scholarly activity with epigenomic data integration. Her Scopus record lists one documented document, seventeen citations, and an h-index of one. These indicators provide bibliometric context but should be interpreted alongside the nature, scope, and quality of individual research contributions rather than as independent measures of scientific significance
[1].

Research Contributions

Research in epigenomic data integration contributes to computational biology by bringing together heterogeneous molecular datasets and analytical strategies for interpreting regulatory processes. Such work can involve data harmonization, computational analysis, statistical interpretation, and integration of genomic and epigenomic measurements. The documented subject area associated with Tavares places her profile within this interdisciplinary research environment, where computational methods provide an important foundation for extracting biological meaning from large-scale molecular information
[3].

Publications

The available profile information records one scholarly document associated with Natalia Tavares in the supplied bibliometric record. The publication record includes research concerning immune responses and Leishmania infection, providing a documented connection between her scholarly output and biomedical research.
[2]

Research Impact

The supplied bibliometric profile records seventeen citations for the documented publication output. Citation counts can provide one indication of scholarly visibility, although they do not independently establish research quality or broader scientific impact. The documented publications address biomedical and immunological research involving Leishmania infection, providing evidence of scholarly activity in a research area with relevance to infectious disease biology.
[3]

Award Suitability

Natalia Tavares’s documented affiliation, research subject area, publication record, citation information, and association with computationally relevant biological data provide an academic basis for consideration within a Computational Biologists Awards framework, The profile is particularly relevant where evaluation criteria emphasize computational approaches, biological data integration,and documented scholarly activity.
[5]

Conclusion

Natalia Tavares is a Brazil-based researcher affiliated with the Oswaldo Cruz Foundation whose supplied academic profile identifies epigenomic data integration as a principal subject area. Her documented bibliometric record includes one document, seventeen citations, and an h-index of one The supplied publication information also documents research concerning Leishmania infection and immunological mechanisms
[3].

References

    1. Elsevier. (n.d.). Scopus author details: Natalia Tavares, Author ID 57207830108. Scopus.
      https://www.scopus.com/pages/authors/57207830108
    2. Journal article (2020). Inflammasome Activation by CD8+ T Cells from Patients with Cutaneous Leishmaniasis Caused by Leishmania braziliensis in the Immunopathogenesis of the Disease.
      https://doi.org/10.1016/j.jid.2020.05.106
    3. Journal article (2014). Understanding the Mechanisms Controlling Leishmania amazonensis Infection In Vitro: The Role of LTB4 Derived From Human Neutrophils.
      https://doi.org/10.1093/infdis/jiu158
    4. ORCID. (n.d.). Natalia Tavares ORCID record. ORCID.
      https://orcid.org/0000-0002-4026-679X
    5. Computational Biologists Awards. (2026). Official Award Website.
      https://computationalbiologists.com/