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/

Pramod singh | Omics Data Analysis | Research Excellence Award

Published on by Computational Biologists

Research Excellence Award

Pramod singh
Affiliation Netaji Subhas University
Country India
Google Scholar zxaambsAAAAJ&hl
Documents 53
Citations 355
h-index 11
Subject Area Omics Data Analysis
Event Computational Biologists Awards

PRAMOD SINGH

Netaji Subhas University, India

Pramod singh is an academic researcher associated with Netaji Subhas University whose scholarly activities focus on Omics Data Analysis and computational approaches for interpreting biological information. His publication record, citation profile, and continuing research contributions demonstrate sustained engagement with multidisciplinary investigations involving genomics, bioinformatics, and data-driven biological interpretation. The following article presents an encyclopedic overview of his academic profile, research interests, publication activity, and relevance for recognition through the Computational Biologists Awards.[1]

Abstract

Pramod singh has developed a scholarly profile centered on Omics Data Analysis through interdisciplinary computational research integrating biological datasets with analytical methodologies. His publications contribute to improved interpretation of genomic information, biological variability, and computational modeling that supports modern life science investigations. Citation indicators and documented research productivity reflect continued academic engagement within bioinformatics and systems biology. His work emphasizes reproducible scientific analysis, collaborative investigation, and evidence-based interpretation of complex biological information, providing valuable support for researchers seeking meaningful biological insights from rapidly expanding omics resources while encouraging future computational innovations.[1]

Keywords

Omics Data Analysis, Bioinformatics, Computational Biology, Genomics, Systems Biology, Biological Data Mining, Data Integration, Scientific Computing.

Introduction

Computational biology increasingly depends upon advanced analytical methods capable of interpreting extensive biological datasets produced through modern experimental technologies. Researchers working within omics sciences contribute by combining computational techniques with biological knowledge to reveal meaningful relationships among genes, proteins, and molecular pathways. Pramod singh has participated in this evolving research landscape through studies supporting data interpretation and biological discovery while maintaining an active academic publication record.[2]

Research Profile

The research profile demonstrates sustained scholarly productivity, including fifty-three documented publications, three hundred thirty-five citations, and an h-index of eleven according to the supplied academic metrics. Affiliated with Netaji Subhas University, the researcher investigates computational methodologies applicable to omics data interpretation and contributes to multidisciplinary biological research through quantitative analysis, scientific collaboration, and publication in recognized scholarly literature.[1]

Research Contributions

Research contributions emphasize computational strategies for analyzing complex biological information generated from modern omics technologies. Such work commonly supports improved biological interpretation through statistical modeling, computational workflows, and integrated data analysis. These contributions encourage reproducibility, facilitate interdisciplinary collaboration, and strengthen scientific understanding by transforming large-scale biological observations into interpretable knowledge supporting future experimental and computational investigations.[2]

Publications

The documented publication portfolio reflects continued scholarly engagement across computational biology and omics research themes. Published studies contribute to scientific communication by presenting analytical findings, computational methodologies, and biological interpretations relevant to contemporary bioinformatics. The publication record, together with citation performance, indicates ongoing visibility within the research community and demonstrates active participation in knowledge dissemination.[1]

Research Impact

Research impact is reflected through measurable citation performance and the broader applicability of computational approaches for biological data analysis. Published findings support subsequent investigations by providing analytical perspectives and methodological references that may assist researchers working across genomics, molecular biology, and bioinformatics. Such influence contributes to cumulative scientific progress while encouraging continued interdisciplinary research development.[2]

Award Suitability

Based on the documented academic profile, publication productivity, citation metrics, and specialization in Omics Data Analysis,Pramod singh demonstrates characteristics commonly considered during scholarly recognition processes. His sustained research activity and measurable academic contributions align with the objectives of the Computational Biologists Awards, which acknowledge researchers advancing computational methods for biological discovery through high-quality scientific investigation and interdisciplinary collaboration.[1]

Conclusion

Pramod singh represents an active researcher contributing to computational biology through Omics Data Analysis and scholarly publication. His documented academic indicators, interdisciplinary research interests, and continuing engagement with computational biological sciences illustrate a consistent commitment to scientific advancement. Collectively, these characteristics provide a strong academic foundation supporting recognition within professional research award programs dedicated to computational biology excellence.[2]

External Links

References

  1. Google Scholar Profile of Pramod singh.
    https://scholar.google.com/citations?user=zxaambsAAAAJ&hl=en
  2. Biochemistry Research International.(2026.) Characterization of Seed Storage Proteins from Chickpea Using 2D Electrophoresis Coupled with Mass Spectrometry.
    https://doi.org/10.1155/2016/1049462
  3. Computational Biologists Awards.(2026.) Official Award Website.
    https://computationalbiologists.com/

Akhilesh Mishra | Single Cell Genomics | Best Researcher Award

Published on by Computational Biologists

Assist.Prof.Dr.Akhilesh  Mishra |  Single Cell Genomics |  Best Researcher Award 

Assistant Professor at  National Institute of Technology Rourkela,  India.

Dr. Akhilesh Mishra is an accomplished Assistant Professor in the Department of Life Science at the National Institute of Technology (NIT) Rourkela, Odisha, India. His academic path began with a Bachelor’s degree in Botany and Chemistry, followed by dual Master’s degrees—one in Bioinformatics and the other in Botany. He earned his Ph.D. from the Supercomputing Facility for Bioinformatics and Computational Biology (SCFBio) at IIT Delhi, where he decoded the physicochemical and structural language of DNA. Dr. Mishra further honed his expertise through postdoctoral fellowships at UT Southwestern Medical Center and St. Jude Children’s Research Hospital, USA.

🎓 Educational Background:

Dr. Akhilesh Mishra’s academic journey reflects a strong foundation in both life sciences and computational biology. He began his higher education with a Bachelor’s degree in Botany and Chemistry, where he developed an early interest in biological systems and their underlying mechanisms. Driven by a passion for understanding complex biological data, he pursued dual postgraduate degrees—a Master’s in Bioinformatics and a Master’s in Botany—equipping himself with a rare blend of computational skills and deep biological insight. His pursuit of research excellence led him to earn a Ph.D. from the Supercomputing Facility for Bioinformatics and Computational Biology (SCFBio) at IIT Delhi, one of India’s premier institutions. During his doctoral studies, he focused on the physicochemical and structural features of DNA, laying the groundwork for his future contributions to computational oncology. Further enriching his academic experience, Dr. Mishra completed postdoctoral fellowships at UT Southwestern Medical Center and St. Jude Children’s Research Hospital in the United States, where he engaged in cutting-edge cancer genomics and bioinformatics research.

Profile:

Research Focus:

Dr. Mishra leads the Computational Oncology Lab at NIT Rourkela, where he applies cutting-edge multi-omics and computational tools to decode the complexities of cancer biology. His vision is rooted in leveraging interdisciplinary approaches—integrating genomics, transcriptomics, epigenetics, and spatial data—to unravel molecular drivers of renal cell carcinoma (RCC), pediatric brain tumors, and antimicrobial resistance.

Publications:

  • Molecular dynamics simulation-based trinucleotide and tetranucleotide level structural and energy characterization of the functional units of genomic DNA. Physical Chemistry Chemical Physics, 2023.

  • HIF2 Inactivation and Tumor Suppression with a Tumor-Directed RNA-Silencing Drug in Mice and Humans. Clinical Cancer Research, 2022.

  • Molecular Genetic Determinants of Shorter Time on Active Surveillance in a Prospective Phase 2 Clinical Trial in Metastatic Renal Cell Carcinoma. European Urology, 2022.

  • Intron exon boundary junctions in human genome have in-built unique structural and energetic signals. Nucleic Acids Research, 2021.

  • A novel method SEProm for prokaryotic promoter prediction based on DNA structure and energetics. Bioinformatics, 2020.

  • PRIMER SETS, BIOMARKERS, KIT AND APPLICATIONS THEREOF. Indian Institute of Technology Delhi, Patent, 2020.

  • Prion protein transcription is auto-regulated through dynamic interactions with G-quadruplex motifs in its own promoter. Biochimica et Biophysica Acta (BBA) – Gene Regulatory Mechanisms, 2020.

  • ChemGenome2.1: An ab initio gene prediction software. In Gene Prediction, Book Chapter, 2019.

  • Toward a Universal Structural and Energetic Model for Prokaryotic Promoters. Biophysical Journal, 2018.

  • A computational protocol for the discovery of lead molecules targeting DNA unique to pathogens. Methods, 2017.

  • Physico-chemical fingerprinting of RNA genes. Nucleic Acids Research, 2017.

  • Onco-Regulon: an integrated database and software suite for site specific targeting of transcription factors of cancer genes. Database, 2016.

  • A Novel Anticlustering Filtering Algorithm for the Prediction of Genes as a Drug Target. American Journal of Biomedical Engineering, 2012.