Guest Editors:
Casey S. Greene, PhD, University of Colorado School of Medicine, USA
Qin Ma, PhD, College of Medicine and The James Comprehensive Cancer Center, The Ohio State University, USA
Jiangning Song, PhD, Monash Biomedicine Discovery Institute, Monash University, Australia
Casey S. Greene, PhD, University of Colorado School of Medicine, USA
Dr Greene is the Chair of and a Professor in the Department of Biomedical Informatics at the University of Colorado School of Medicine. His lab develops and evaluates approaches to integrate distinct large-scale datasets to extract the rich and intrinsic information embedded in such data. The overarching theme of his work has been the development and evaluation of methods that acknowledge the emergent complexity of biological systems.
Qin Ma, PhD, College of Medicine and The James Comprehensive Cancer Center, The Ohio State University, USA
Qin Ma is a Professor and Division Chief of the Bioinformatics and Computational Biology Section in the Department of Biomedical Informatics, the Ohio State University (OSU), and Leader of the Immuno-Oncology Informatics Group, Pelotonia Institute for Immuno-Oncology at The OSU Comprehensive Cancer Center. He received his PhD in Operational Research from Shandong University and then did his postdoc in Bioinformatics at the University of Georgia, specializing in high-throughput sequencing data mining and modeling. His lab focuses on developing cutting-edge deep-learning methods for single-cell and spatial omics data, aiming to discover underlying mechanisms (e.g., gene regulation, cell-cell interactions, and cellular senescence) in diverse biological systems and complex diseases.
Jiangning Song, PhD, Monash Biomedicine Discovery Institute, Monash University, Australia
Jiangning Song is a Professor of Bioinformatics and Group Leader at the Biomedicine Discovery Institute (BDI) within the Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia. He directs the AI-driven Bioinformatics and Biomedicine Laboratory and is an Associate Investigator at the ARC Centre of Excellence in Advanced Molecular Imaging and The Centre to Impact AMR (Antimicrobial Resistance). Trained as a bioinformatician and data-savvy biomedical scientist, his research interests span AI, bioinformatics, comparative genomics, cancer genomics, bacterial genomics, computational biomedicine, data mining, infection and immunity, machine learning, proteomics, and biomedical big data analytics. Jiangning is an Associate Editor for several leading journals and serves on the editorial boards of many others. Jiangning and his team have developed over 60 bioinformatics algorithms and tools widely used in the research community.
The integration of machine learning, particularly LLMs built using transformers, into biological research has opened up new avenues for understanding complex biological data. These advanced computational models have shown promise in processing vast datasets, from genomic sequences to protein structures, enabling researchers to extract meaningful insights and identify patterns that were previously unattainable.
The employment of LLMs in biology has enhanced our capabilities in areas such as genomics, transcriptomics, and proteomics. For instance, LLMs have been successfully applied to predict the functional consequences of genetic variants in both coding and non-coding genomes, facilitating targeted precision medicine and personalized therapies. Furthermore, these models enable researchers to automate the annotation of DNA and protein sequences, accelerating the pace of discovery and innovation in biotechnology and pharmaceutical research. Additionally, LLMs are increasingly being used to explore the regulation of gene expression, including the identification and characterization of regulatory elements such as enhancers, promoters, and transcription factor binding sites.
Future advancements may lead to the creation of more sophisticated models capable of integrating multi-omic data, facilitating the understanding of complex biological systems. Such innovations could potentially enable real-time analysis and prediction of biological responses, transforming our approach to disease modeling, drug discovery, and synthetic biology. The incorporation of regulatory elements into machine learning models will be crucial for uncovering the mechanisms that govern cellular behavior and tissue-specific gene regulation.
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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.