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 …

Technological advances have made it possible to study a patient from multiple angles with
high-dimensional, high-throughput multiscale biomedical data. In oncology, massive …

Cell line drug screening datasets can be utilized for a range of different drug discovery
applications from drug biomarker discovery to building translational models of drug …

With biotechnological advancements, innovative omics technologies are constantly
emerging that have enabled researchers to access multi-layer information from the genome …

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 …

The advancement of precision medicine in medical care has led behind the conventional
symptom-driven treatment process by allowing early risk prediction of disease through …

This Perspective explores the application of machine learning toward improved diagnosis
and treatment. We outline a vision for how machine learning can transform three broad …

Cancer progression is driven in part by genomic alterations. The genomic characterization of
cancers has shown interpatient heterogeneity regarding driver alterations, leading to the …

The gut microbiome has been implicated in cancer in several ways, as specific microbial
signatures are known to promote cancer development and influence safety, tolerability and …

Machine learning, a collection of data-analytical techniques aimed at building predictive
models from multi-dimensional datasets, is becoming integral to modern biological research …