Call for papers - High-dimensional statistics and omics data analysis

Guest Editors

Zeeshan Ahmed, PhD, Rutgers The State University of New Jersey, USA
Niansheng Tang, PhD, Yunnan University, China
Chao Xu, PhD, The University of Oklahoma Health Sciences Center, USA

Submission Status: Open | Submission Deadline: 12 December 2025

BMC Medical Research Methodology is calling for submissions to our Collection on advanced data analytic techniques that have the potential to uncover the hidden patterns and associations that contribute to personalized medicine, disease prediction, and therapeutic interventions.

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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, Rutgers Robert Wood Johnson Medical School, and Core Faculty Member at the Institute for Health, Health Care Policy and Aging Research, Rutgers Health. His lab is focused on implementing novel artificial intelligence and machine learning (AI/ML), as well as orthodox statistics, bioinformatics, and biomedical informatics approaches to investigate multimodal and omics data for the identification of patterns revealing predictive biomarkers and risk factors to support earlier diagnosis of patients with complex, common, and rare diseases. Dr Ahmed is driven towards innovative and collaborative research with high productivity and has published over 100 peer reviewed articles as the first, last, or corresponding author.

Niansheng Tang, PhD, Yunnan University, China

Dr Tang is Yangtze River Scholars Distinguished Professor of Statistics in the Department of Statistics and Data Science at the Yunnan University, China and the Dean of School of Mathematics and Statistics at the Yunnan University. He earned a PhD degree (2000) from Southeast University in China. He is an elected member of the International Statistical Institute, IMS (Institute of Mathematical Statistics). Dr Tang’s research interests include missing data analysis, biostatistics, Bayesian statistics, high-dimensional data analysis, big data analysis, and machine learning. Currently, he serves as Associate Editor of Statistics and Its Interface, Journal of American Statistical Association (Applications and Case Studies), and is a member of IMS Committee: Hall Prize. He has published over 200 papers in peer-reviewed journals, including JASA, Biometrika, Annals of Statistics, Journal of Machine Learning Research. He is a permanent member of ICSA.

Chao Xu, PhD, The University of Oklahoma Health Sciences, USA

Dr Xu is an Assistant Professor at the University of Oklahoma Health Sciences and a member of the Stephenson Cancer Center Biostatistics and Research Design Shared Resource. He received a PhD in Biostatistics from Tulane University, USA. He has a broad background in statistical genetics, biostatistics, bioinformatics, and genetic epidemiology. His recent research focuses on high-dimensional analysis, integrative analysis of multi-omics data, medical image analysis, survey sampling, and deep learning.

About the Collection

BMC Medical Research Methodology is calling for submissions to our Collection on High-dimensional statistics and omics data analysis. The rise of multi-modal and omics approaches —such as genomics, transcriptomics, proteomics, and metabolomics—has led to an explosion of high-dimensional data in biomedical research. Such complex, heterogeneous, and high-volume datasets present unique challenges and opportunities for the artificial intelligence (AI), machine learning (ML), statistical and bioinformatics analysis, requiring innovative and efficient methodologies to extract meaningful insights, novel biomarkers and predict diseases with high accuracy. This Collection seeks to explore advancements in high-dimensional AI/ML, statistical and bioinformatics approaches applied to omics data, including approaches for managing and analyzing large-scale datasets.

Multimodal and multi-omics integration allows researchers to harmonize diverse biological and clinical data types and understand the intricate relationships underlying health and disease factors. Advanced data analytic techniques have the potential to uncover the hidden patterns and associations that contribute to personalized medicine, disease prediction, and therapeutic interventions. As the volume of biomedical data continues to grow, robust data and knowledge-oriented frameworks specifically tailored for high-dimensional data are essential for translating this information into actionable insights.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of original Research 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 "High-dimensional statistics and omics data analysis" from the dropdown menu.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.