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Call for papers - Artificial intelligence for omics data analysis

Guest Editors

Zeeshan Ahmed, PhD, Rutgers, The State University of New Jersey, USA
Shibiao Wan, PhD, University of Nebraska Medical Center, USA
Fan Zhang, PhD, University of Colorado School of Medicine, USA
Wen Zhong, PhD, Linköping University, Sweden

Submission Status: Open   |   Submission Deadline: 1 September 2024 


BMC Methods is calling for submissions to our Collection on Artificial intelligence for omics data analysis. We encourage submissions that elucidate how AI can be mobilized to process, analyze, visualize, and interpret omics data. 

Meet the Guest Editors

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Zeeshan Ahmed, PhD, Rutgers, The State University of New Jersey, USA

Dr Zeeshan Ahmed is an Assistant Professor at the Department of Medicine / Cardiovascular Disease and Hypertension, Rutgers Robert Wood Johnson Medical School (RWJMS); and Core Faculty Member at the Institute for Health, Health Care Policy and Aging Research (IFH), Rutgers, The State University of New Jersey. Dr Ahmed’s lab at Rutgers is focused on implementing Artificial Intelligence (AI), Machine Learning (ML), and standard bioinformatics approaches to multi-omics/genomic and phenotypic data for the identification of patterns revealing predictive biomarkers and risk factors to support earlier diagnosis of patients with complex traits. 

Shibiao Wan, PhD, University of Nebraska Medical Center, USA

Dr Shibiao Wan is an Assistant Professor and the Assistant Director for Bioinformatics and Systems Biology Core at University of Nebraska Medical Center (UNMC). With more than 13 years of experience in bioinformatics and machine learning, Dr Wan has published >40 articles in prestigious journals. Dr Wan is an editorial board member for a series of prestigious journals including Briefings in Functional Genomics, and BMC Bioinformatics and also a reviewer for >50 high-impact journals. He is a TPC member for >20 machine learning related international conferences including IEEE ICTAI and IEEE IAICT and is an IEEE Senior Member.

Fan Zhang, PhD, University of Colorado School of Medicine, USA

Dr Fan Zhang and her lab are at the intersection of the Department of Medicine Division of Rheumatology and the Department of Biomedical Informatics Center for Health Artificial Intelligence in the University of Colorado Anschutz Medical Campus. The Zhang Lab focuses on developing advanced computational AI and statistical methods for single-cell omics to study immune-mediated inflammatory disease pathogenesis for translational medicine. As primary authors, her lab has published high-impact papers at Nature, Nature Immunology, Science Translational Medicine, BMC Bioinformatics, Bioinformatics, etc.

Wen Zhong, PhD, Linköping University, Sweden

Dr Wen Zhong is a docent and Assistant Professor at Linköping University. Her research mainly focuses on the integration of multi-omics, the interplay between genetics and phenotypes, and the development of data-driven strategies/tools for precision medicine. The aim is to investigate the molecular biomarkers for the estimation of disease risks, early diagnosis of disease, stratification of drug treatment response, disease progression monitoring, and the stratification of patients.

About the Collection

In recent years, the proliferation of high-throughput technologies has led to a rapid accumulation of “big” omics data, including genomes, transcriptomes, proteomes, and metabolomes. How best to extract insights from these vast datasets remains a challenge in bioinformatics, but recent progress in artificial intelligence techniques – including deep learning, natural language processing, and predictive models – have advanced understandings in the field. AI’s ability to parse big and complex data has enabled researchers to accelerate discoveries in areas like personalized medicine, disease diagnostics, drug development, and biological pathway elucidation. 

In light of these developments, BMC Methods is welcoming submissions to our Collection, Artificial intelligence for omics data analysis. We invite contributions on:

  • AI-based methods for omics data: AI-based models, methods, and software for processing, analyzing, visualizing, and interpreting omics data
  • AI in biomarker discovery: AI-based algorithms for identifying novel biomarkers across diverse datasets for diseases and conditions 
  • AI-based platforms for improving disease diagnosis, precision medicine, and patient care
  • AI in molecular biology: AI-based approaches for protein structure prediction, gene function prediction, and drug discovery
  • Multi-omics integration: Strategies and techniques for integrating multi-omics data to reveal comprehensive biological insights


Image credit: woravut / stock.adobe.com

  1. Recent technological advancements have vastly improved access to high-throughput biological instrumentation, sparking an unparalleled surge in omics data generation. The implementation of artificial intelligen...

    Authors: Zeeshan Ahmed, Shibiao Wan, Fan Zhang and Wen Zhong
    Citation: BMC Methods 2024 1:4
    Content type: Editorial Published on:

Submission Guidelines

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This Collection welcomes submission of original Methodology and Protocol Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select Artificial intelligence for omics data analysis from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.