Anticancer drug response prediction with deep learning technology has become the
foundation of precision medicine. It is essential for anticancer drug response prediction to …

The nanomaterials are frequently employed in a variety of heat transfer applications arising
in energy generation, engine cooling, extrusion procedures, heat exchanger, thermos …

Diagnosing cancer subtypes is a prerequisite for precise treatment. Existing multi-omics data
fusion-based diagnostic solutions build on the requisite of sufficient samples with complete …

Efficiently preserving task-relevant information while removing noise and redundancy in
multi-view data remains a core challenge. The information bottleneck principle offers an …

The issue of drug safety has received increasing attention in modern society. Estimating the
frequency of drug side effects proves to be an effective approach to improving drug …

Relying solely on a single medical data for cancer diagnosis may increase the risk of
misdiagnosis and missed diagnosis. Multi-modal data provides comprehensive information …

Background and Objective: With the rapid development of the accumulation of large-scale
multiomics data sets, integrating various omics data to provide a thorough study from …

A self-optimization regression method based on multivariate data screening and multi-model
fusion is proposed for the regression prediction of vacuum glass insulation performance …

Advanced machine learning (ML) and deep learning (DL) methods have recently been
utilized in Drug Response Prediction (DRP), and these models use the details from genomic …

This chapter take a fascinating voyage across the complex bioinformatics landscapes. The
authors begin by defining bioinformatics as the essential connection that connects …