This review provides the feasible literature on drug discovery through ML tools and
techniques that are enforced in every phase of drug development to accelerate the research …

Increased availability of high-throughput technologies has generated an ever-growing
number of omics data that seek to portray many different but complementary biological …

In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful
technique for investigating cellular heterogeneity and structure. However, analyzing scRNA …

To the Editor—Methods for analyzing single-cell data 1, 2, 3, 4 perform a core set of
computational tasks. These tasks include dimensionality reduction, cell clustering, cell-state …

Drug discovery and development pipelines are long, complex and depend on numerous
factors. Machine learning (ML) approaches provide a set of tools that can improve discovery …

Single‐cell RNA‐seq has enabled gene expression to be studied at an unprecedented
resolution. The promise of this technology is attracting a growing user base for single‐cell …

Single-cell transcriptome measurements can reveal unexplored biological diversity, but they
suffer from technical noise and bias that must be modeled to account for the resulting …

Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues
detailing the transcriptomes of individual cells. Unsupervised clustering is of central …

Recent technological advancements have enabled the profiling of a large number of
genome-wide features in individual cells. However, single-cell data present unique …

To define the cell populations that drive joint inflammation in rheumatoid arthritis (RA), we
applied single-cell RNA sequencing (scRNA-seq), mass cytometry, bulk RNA sequencing …