Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets.

Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suitable for assigning subtype labels to new cancer specimens from other studies or clinical trials. Here, we address this barrier by applying five different machine learning approaches to multi-omic data from 8,791 TCGA tumor samples comprising 106 subtypes from 26 different cancer cohorts to build models based upon small numbers of features that can classify new samples into previously defined TCGA molecular subtypes-a step toward molecular subtype application in the clinic.

Correction: Computational capabilities of a multicellular reservoir computing system.

[This corrects the article DOI: 10.1371/journal.pone.

A multiscale model of immune surveillance in micrometastases gives insights on cancer patient digital twins.

Metastasis is the leading cause of death in patients with cancer, driving considerable scientific and clinical interest in immunosurveillance of micrometastases. We investigated this process by creating a multiscale mathematical model to study the interactions between the immune system and the progression of micrometastases in general epithelial tissue. We analyzed the parameter space of the model using high-throughput computing resources to generate over 100,000 virtual patient trajectories.

Generating Biomedical Knowledge Graphs from Knowledge Bases, Registries, and Multiomic Data.

As large clinical and multiomics datasets and knowledge resources accumulate, they need to be transformed into computable and actionable information to support automated reasoning. These datasets range from laboratory experiment results to electronic health records (EHRs). Barriers to accessibility and sharing of such datasets include diversity of content, size and privacy.

Knowledge graphs facilitate prediction of drug response for acute myeloid leukemia.

Acute myeloid leukemia (AML) is a highly aggressive and heterogeneous disease, underscoring the need for improved therapeutic options and methods to optimally predict responses. With the wealth of available data resources, including clinical features, multiomics analysis, and drug screening from AML patients, development of drug response prediction models has become feasible. Knowledge graphs (KGs) embed the relationships between different entities or features, allowing for explanation of a wide breadth of drug sensitivity and resistance mechanisms.