Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …

Abstract Vision Transformers (ViTs) and their multi-scale and hierarchical variations have
been successful at capturing image representations but their use has been generally …

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …

Inferring single-cell compositions and their contributions to global gene expression changes
from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology. Here we …

Although it has long been known that the immune cell composition has a strong prognostic
and predictive value in colorectal cancer (CRC), scoring systems such as the immunoscore …

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 …

The clinical use of molecular targeted therapy is rapidly evolving but has primarily focused
on genomic alterations. Transcriptomic analysis offers an opportunity to dissect the …

Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic
potential and poor prognosis, and has limited treatment options. The current standard of …