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GraphRepur: A Graph Neural Network Used to Repurpose Drugs for Breast Cancer

Graph neural networks are powerful computational tools that can predict relationships from graphs at the node, edge and graph level. […]

The IRIS Study Using Real-Word Breast Cancer Data

Researchers have conducted a multi-country Iberance Real World Insights (IRIS) study, which aims to provide country-specific real-world data that can […]

Murine Viruses in PDX Models: Why Some Cancer Drugs May be Ineffective

Researchers from the University of Texas have used data-driven approaches to identify a possible explanation for why some cancer drugs […]

Identifying the Bioactive Compounds in Ginger for Treatment in Colon Cancer

An article, published in BioData Mining, aimed to identify the bioactive compounds in ginger as a treatment for colon cancer […]

DRUML: Using ML to Rank Cancer Drugs in Order of Efficacy

Researchers at Queen Mary University of London have developed a machine learning algorithm – DRUML – that ranks drugs based […]

EMR implementation in cancer care

We summarise a recent study, published in BMC Health Services Research, that explored the factors influencing the adoption of an […]

Harnessing RWE for novel cancer therapy development

We summarise a recent article, published in Cell, that explored the benefits and challenges of using real-world evidence (RWE) in […]

Drug repurposing for triple-negative breast cancer

We summarise a recent review, published in J. Pers. Med., that explored relevant research aimed at repurposing pre-existing drugs to […]

ML framework predicts anti-cancer drug efficacy

Researchers have developed a machine-learning (ML) framework to identify robust drug biomarkers and thereby, predict anti-cancer drug efficacy. Biomarker identification […]

Cancer-on-a-chip developed to assess chemotherapy-associated toxicity

Researchers have developed a dual-organ system on a chip to measure cardiac toxicity arising from breast cancer chemotherapy. Breast cancer […]