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Using ML to identify responders vs non-responders

We summarise a recent study, published as preprint in medrxiv, that explored the hypothesis that a data-driven analysis of a […]

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 […]

Predicting near-term COVID-19 mortality using an ML-based approach

A team of researchers have developed a machine learning (ML)-based algorithm to analyse electronic health record (EHR) data and reliably […]

The role of EMR and ML in drug development

Here, we summarise a chapter in Artificial Intelligence in Oncology Drug Discovery and Development, which explored the role of electronic […]

Multimodality MRI and ML techniques in bipolar disorder identification

Researchers have combined structural and functional magnetic resonance imaging (MRI) with machine learning (ML) techniques to aid in accurately identifying […]

Integration of ML in the diagnosis and treatment of neurodegenerative diseases

A recent Nature review, describes the latest developments in the use of machine learning approaches to interrogate datasets in neurodegenerative […]

ML-based approach for drug synergy predictions and stratification

Particularly in oncology, combination drug treatments can offer higher patient survival rates and a lower risk of drug resistance than […]

First end-to-end autoML pipeline for time-series data

The van der Schaar lab has announced the release of their alpha version of clairvoyance, an autoML complete package for […]

Immunai secures $20M in seed funding to map immune system using ML

Immunai, a biotech start-up founded by MIT, Harvard and Stanford researchers, has emerged from stealth mode with $20 million in […]

Lessons from building an end-to-end ML platform

Caleb Kaiser, writing for Towards Data Science, shares his experience of trying – and failing – to build an end-to-end […]