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 quantitative biomarker development have accelerated new forms of data-driven
insights for patients with cancer. However, most approaches are limited to a single mode of …

Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

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

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

In the past decade, advances in precision oncology have resulted in an increased demand
for predictive assays that enable the selection and stratification of patients for treatment. The …

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 …

Nuclear segmentation and classification within Haematoxylin & Eosin stained histology
images is a fundamental prerequisite in the digital pathology work-flow. The development of …

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that
primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate …

Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing
additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic …