Deep learning model predicts how individual cells influence disease outcomes

A computational method called scSurv, developed by researchers at Institute of Science Tokyo, links individual cells to ...
Scientific Research Publishing

A Multimodal Machine-Learning Framework for Predicting Lithium-Associated Renal Dysfunction from Synthetic Biomarker Trajectories ()

Long-term lithium therapy remains the most effective maintenance treatment for bipolar disorder, yet it poses a significant risk of progressive renal impairment in a subset of patients. Early ...
Frontiers

From Omics to Outcomes: Translational Bioinformatics Powered by Foundation Models

Translational bioinformatics lies at the intersection of computational biology, multi-omics, and clinical data science, offering a powerful toolkit for ...
Frontiers

Integrative Multi-Omics and Bioinformatics Approaches in Cardiovascular Disease

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, driven by complex and heterogeneous molecular mechanisms ...
EurekAlert!

Jeonbuk National University researchers develop DDINet for accurate and scalable drug-drug interaction prediction

Drug-drug interactions (DDI) can cause adverse drug reactions during the co-administration of multiple drugs, necessitating accurate and scalable prediction tools. While deep learning models have ...
IEEE

Federated Learning Framework for Privacy-Preserving Bioinformatics and Genome Sequence Data Analytics

Abstract: The rapid expansion of genomic data and bioinformatics research has intensified the need for secure and collaborative data analysis while maintaining patient confidentiality. Conventional ...
IEEE

A comparative study of ensemble learning methods for classification in bioinformatics

Abstract: In this research work, we have proposed a novel ensemble learning approach “BBS method” which stands for Bagging, Boosting and Stacking with appropriate base classifiers for the ...
GitHub

LINKER: Learning Interactions Between Functional Groups and Residues with Chemical Knowledge-Enhanced Reasoning and Explainability

LINKER is a framework for modeling and explaining protein–ligand interactions by explicitly learning interactions between ligand functional groups and protein residues. The method integrates chemical ...
Harvard Business Review

AI Is Changing How We Learn at Work

During a recent workshop, a senior executive said to me, “None of us knows how people will learn in this new era.” Lynda Gratton is a professor of management practice at London Business School and the ...