Huseyin Cimen | Omics Data Analysis | Best Research Article Award

Best Research Article Award

Huseyin Cimen
Gebze Technical University

Huseyin Cimen
Affiliation Gebze Technical University
Country Turkey
Scopus ID 35195463600
Documents 15
Citations 971
h-index 10
Subject Area Omics Data Analysis
Event Computational Biologists Awards
ORCID 0000-0003-0801-3554

The Best Research Article Award profile presents Huseyin Cimen of Gebze Technical University, Turkey, in the context of computational biology and omics data analysis. The supplied information includes researcher identifiers and bibliometric indicators that provide context for scholarly activity. Omics data analysis integrates computational and statistical methods to investigate complex biological datasets and support scientific interpretation. Publication counts, citations, and h-index values should be considered alongside the quality, reproducibility, and significance of individual research contributions. This article outlines the academic context of the award without asserting an unverified nomination, selection, or award outcome. [1]

Abstract

This article presents the supplied academic profile of Huseyin Cimen, affiliated with Gebze Technical University in Turkey, in relation to the Best Research Article Award associated with the Computational Biologists Awards. The stated subject area is omics data analysis, which uses computational and statistical approaches to investigate biological information. The profile includes a Scopus author identifier, an ORCID identifier, and reported publication and citation metrics. These indicators offer a quantitative overview of scholarly activity but do not independently establish the quality of a specific research article. The article discusses research context, publication assessment, academic impact, and transparent recognition of scientific contributions.[1]

Keywords

Best Research Article Award; Huseyin Cimen; computational biology; omics data analysis; bioinformatics; Gebze Technical University; scholarly publications; citation analysis; research evaluation; Computational Biologists Awards.[2]

Introduction

Computational biology applies computational techniques, mathematics, and statistics to biological questions. Advances in high-throughput technologies have generated large datasets that require robust analytical methods and careful interpretation. Research articles in this area can contribute new methods, reproducible workflows, integrated datasets, or biological insights. Recognition of research articles should consider methodological rigor, originality, transparency, and relevance to the scientific community. Bibliometric indicators can complement this assessment but should not replace evaluation of the research itself. [2]

Research Profile

The information supplied identifies Huseyin Cimen as affiliated with Gebze Technical University in Turkey and lists omics data analysis as the subject area. The Scopus author ID is 35195463600, and the ORCID identifier is 0000-0003-0801-3554. The reported profile metrics are 15 documents, 971 citations, and an h-index of 10. These values are presented as supplied and should be verified against the current records because database coverage and citation counts can change. Research topics and specific accomplishments should be established using the researcher’s verified publications. [1] [3]

Research Contributions

Omics data analysis involves extracting meaningful patterns from large-scale biological measurements, including genomic, transcriptomic, proteomic, and metabolomic datasets. Relevant research approaches may include statistical modeling, data integration, quality control, machine learning, and reproducible computational workflows. The value of these approaches depends on data quality, appropriate methodology, validation, and clear reporting. Without a verified publication list or nominated article, specific discoveries or methods cannot responsibly be attributed to the researcher. The field nevertheless provides an important framework for studying biological systems and supporting evidence-based scientific interpretation. [2]

Publications

The supplied profile reports 15 documents. A verified bibliography containing publication titles, author lists, journal details, publication years, and digital object identifiers was not included in the source information for this article. For accuracy, individual papers and DOIs are not invented or assigned without confirmation. The linked Scopus author profile and ORCID record provide starting points for locating publications and checking author attribution. A formal award assessment should identify the specific nominated research article and evaluate its findings, methodology, originality, and relevance to the field. [1]

Research Impact

The reported citation count and h-index provide quantitative indicators of publication visibility and citation activity. The h-index combines publication productivity with citation frequency, but it does not directly measure originality, practical value, or methodological quality. Citation metrics can differ across databases and vary according to discipline, publication age, and citation practices. A balanced assessment should also consider reproducibility, data accessibility, scientific validity, and influence on subsequent studies. For these reasons, bibliometric indicators are best used as supporting evidence within a broader qualitative evaluation.[3]

Award Suitability

The Best Research Article Award is intended, by its title, to recognize scholarly articles demonstrating research value and scientific contribution. For computational biology submissions, relevant evaluation considerations may include originality, research design, analytical rigor, data quality, reproducibility, clarity of presentation, and significance to biological research. The supplied profile provides identifying information and bibliometric context but does not specify a nominated article or provide official confirmation of an award decision. Eligibility and suitability should therefore be determined from the actual submission and the award organizer’s published criteria. [2]

Conclusion

This article provides a structured academic profile for Huseyin Cimen using the researcher details and bibliometric figures supplied. The profile connects the stated subject area of omics data analysis with the wider role of computational methods in biological research. Publication-level evidence remains necessary to establish specific research contributions and assess the suitability of an individual article for recognition. Verifying identifiers, current metrics, publication records, and official award information supports accurate and transparent academic communication. The article should be updated when authoritative publication records or confirmed award details become available.[3]

References

  1. Elsevier. (n.d.). Scopus author details: Huseyin Cimen, Author ID 35195463600.
    https://www.scopus.com/authid/detail.uri?authorId=35195463600
  2. Journal article. (2026.) Technofunctional Properties and Proteomic Profiling of Sesame Cake Protein Isolates Obtained by Green Extraction Methods and Treated with Cold Plasma.
    https://doi.org/10.3390/molecules31152638
  3. Computational Biologists Awards. (2026.). Official website.
    https://computationalbiologists.com/

Mingxiong He | Omics Data Analysis | Innovative Research Award

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/