With better methods needed for matching patients with depression with appropriate treatments, researchers at Stanford University, reporting today in Nature Medicine, say they have identified six biological subtypes (biotypes) of depression. The team also identified existing treatments that could be more likely, or less likely, to be effective against three…

A machine learning pipeline that combines multiple omics datasets was used to derive a new transcriptomic footprint correlated with positive outcomes in patients with advanced kidney cancer who underwent immunotherapy. The approach identified the molecular characteristics of an immune signaling hub distinguished by a human leukocyte antigen (HLA) repertoire with…

SAN DIEGO—With the exhibition floor about to close on the third day of AACR 2024, most presenters had deserted their posters. But in one of the dozens of rows, several clusters of conversations were happening all around one poster. Several members from Genialis were explaining krasID—an RNA-based biomarker that uses…

A new study suggests that predicting clinical outcomes based on machine learning models can be only slightly better than chance when extended beyond the trials they are developed from. Researchers found that a machine learning model designed to predict which patients with schizophrenia would benefit from a particular antipsychotic medicine…

A combination of only 11 proteins can be used to predict long-term disability outcomes in multiple sclerosis (MS), according to researchers from Linköping University, the Karolinska Institute, and the University of Skövde. This set of proteins could be used, they suggest, to tailor treatments for individuals based on expected severity…

By Michael Fehlings, PhD   Sponsored content brought to you by Therapeutics for solid tumors remain in a nascent corner of cancer research despite representing 90 percent of all cancers in adults. Thirty years after the first Chimeric Antigen Receptor (CAR) was reported, CAR-T cell therapies have seen significant breakthroughs…

A $2.5 million grant from the National Institutes of Health (NIH) to researchers at Binghamton University, State University of New York will be used to develop machine learning models to assess and predict cardiometabolic risks in adolescents and young adults. The grant will help fill a gap in current research…

A machine-learning model can accurately predict the risk of death after hip fracture using information gleaned from basic blood and lab tests and standard demographic data, research indicates. LightGBM by Microsoft was most accurate of the models assessed, according to the findings published in the Journal of Orthopaedic Research. The…

A new artificial intelligence tool predicts esophageal adenocarcinoma (EAC) and gastric cardia adenocarcinoma (GCA), a form of stomach cancer, at least three years prior to a diagnosis. Both cancers are highly fatal, and rates have risen sharply over the past five decades. Researchers from the Lieutenant Colonel Charles S. Kettles…

A machine-learning study has found the best drug combinations to prevent COVID-19 recurrence, which affects as many as one-third of patients. The researchers found these drug combos are not the same for every patient. Individual characteristics, including age, weight, and additional illness determine which combinations most effectively reduce recurrence. Using…

Research carried out by the University of Surrey and the University of Groningen shows combining machine learning and metabolite analysis can help predict a person’s individual circadian body clock and assess what healthy sleep patterns are for that person. “The circadian system in humans influences many behavioral, physiological, and molecular…

Biomedical engineering researchers at the Washington University McKelvey School of Engineering, St. Louis have applied a variety of imaging method to diagnose ovarian caaner more accurately. Now, the team from the lab of Quing Zhu, a professor of biomedical engineering, have used machine learning to leverage ultrasound features of ovarian…

A machine-learning algorithm detected potential signs of colorectal cancer (CRC) in patients identified as high-risk who had missed a routine colonoscopy, according to a new study led by Geisinger and Medial EarlySign. The findings, published this month in NEJM Catalyst Innovations in Care Delivery, present a noninvasive method to increase screening…

Scientists at the Mayo Clinic Cancer Center in Florida say a recent study is validating the use of genomic sequencing to predict the likelihood that patients with gastric cancer will derive benefit from chemotherapy or from immunotherapy. Their paper (“Development and validation of a prognostic and predictive 32-gene signature for…

A group based at Charité Medical University in Berlin has developed a machine learning based tool that can predict who will be most ill as a result of COVID-19 based on the results of a blood test. The tool can predict patient survival based on levels of a range of…