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 …

Advances in single-cell isolation and barcoding technologies offer unprecedented
opportunities to profile DNA, mRNA, and proteins at a single-cell resolution. Recently, bulk …

The recent boom in microfluidics and combinatorial indexing strategies, combined with low
sequencing costs, has empowered single-cell sequencing technology. Thousands—or even …

The rapid progress of protocols for sequencing single-cell transcriptomes over the past
decade has been accompanied by equally impressive advances in the computational …

Spatially resolved genomic technologies have allowed us to study the physical organization
of cells and tissues, and promise an understanding of local interactions between cells …

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 sequencing (scRNA-seq) technologies allow the dissection of gene
expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A …

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 …

Technological advances have enabled the profiling of multiple molecular layers at single-
cell resolution, assaying cells from multiple samples or conditions. Consequently, there is a …

An explosion in single-cell technologies has revealed a previously underappreciated
heterogeneity of cell types and novel cell-state associations with sex, disease, development …