Human activity recognition is a key to a lot of applications such as healthcare and smart
home. In this study, we provide a comprehensive survey on recent advances and challenges …

Abstract Machine learning has become a pivotal tool for many projects in computational
biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical …

Explainable artificial intelligence is an emerging research direction helping the user or
developer of machine learning models understand why models behave the way they do …

Few studies have explored the impact of rare variants (minor allele frequency< 1%) on
highly heritable plasma metabolites identified in metabolomic screens. The Finnish …

Timely identification of COVID-19 patients at high risk of mortality can significantly improve
patient management and resource allocation within hospitals. This study seeks to develop …

In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts
present in each cell are sequenced. This leads to unreliable quantification of genes with low …

Multi‐omics studies promise the improved characterization of biological processes across
molecular layers. However, methods for the unsupervised integration of the resulting …

Missing data is common problem faced by researchers and data scientists. Therefore, it is
required to handle them appropriately in order to get better and accurate results of data …

The vast majority of coding variants are rare, and assessment of the contribution of rare
variants to complex traits is hampered by low statistical power and limited functional data …

Phenotype-based compound screening has advantages over target-based drug discovery,
but is unscalable and lacks understanding of mechanism of drug action. A chemical-induced …