Researchers at the University of Sussex are using Artificial Intelligence (AI) technology to analyse different types of cancer cells to understand different gene dependencies, and to identify genes that are critical to a cell's survival. Sussex researchers have done this by developing a prediction algorithm that works out which genes are essential in the cell, by analysing the genetic changes in the tumour.

Scientists at University of California San Diego School of Medicine have developed an artificial intelligence (AI)-based strategy for discovering high-affinity antibody drugs.

In the study, published January 28, 2023 in Nature Communications, researchers used the approach to identify a new antibody that binds a major cancer target 17-fold tighter than an existing antibody drug.

New machine learning research led by Professor Farrokh Alemi and Professor Janusz Wojtusiak provides a way for patients and clinicians to better predict whether symptoms are due to COVID-19, influenza, or RSV. A more accurate diagnosis leads to better decisions on course of care to heal patients and prevent the disease from spreading.

Cancer has many faces - no wonder, then, that the range of cancer-causing mutations is huge as well. The totality of such genomic alterations in an individual is what experts call a "mutational landscape." These landscapes differ from one another depending on the type of cancer. And even people suffering from the same cancer often have different mutation patterns.

To help the estimated 1.45 million Americans living with type 1 diabetes better manage their blood sugar levels, Oregon Health & Science University is combining the power of an artificial intelligence-driven smartphone app with the support of human experts.

The Leona M. and Harry B. Helmsley Charitable Trust has awarded OHSU more than $4.3 million to support this work.

Eighty stakeholders from twenty major biomedical research institutions across the globe have agreed upon a list of 19 open science practices to be implemented and monitored. The study, led by Dr. Kelly Cobey, Scientist and Director of the Open Science and Meta Research Program at the University of Ottawa Heart Institute, Canada, forms the basis for the future development of institutional digital dashboards that will display that institution's compliance with open science practices.

A team of researchers at the University of Wisconsin­-Madison has successfully combined genomics with machine learning in the quest to develop accessible tests that allow earlier detection of cancer.

For many types of cancer, early detection can lead to better outcomes for patients. While scientists are developing new blood tests that analyze DNA to aid in earlier detection, these new technologies have limitations, including cost and sensitivity.