Prediction of sotorasib response duration and identification of clinically actionable resistance mechanisms using an RNA-based patient classifier
KRAS mutations are among the most prevalent oncogenic drivers [...]
Genialis™ Supermodel: Predicting monotherapy and combination therapy responses in KRAS-mutated cancers using a large molecular model of cancer biology
KRAS mutations drive approximately 25% of human cancers, resulting [...]
Biology-driven, machine learning-based development of a biomarker to predict response to WEE1 inhibitor Debio 0123
The WEE1 kinase is a central regulator of G2/M [...]
Molecular biomarker testing in Slovenian non-small cell lung cancer patients in early and late stages of the disease
Molecular biomarkers in non-small cell lung cancer (NSCLC), primarily [...]
Genialis™ krasID: Biology-driven Machine Learning to Personalize KRAS Inhibitor Therapy
KRAS inhibitors (KRASi) have the potential to transform treatment [...]
Genialis™ krasID: Biology-Driven Machine Learning to Personalize KRAS Inhibitor Therapy
Genialis krasID analyzes foundational aspects of KRAS biology using RNA-sequencing [...]
Prevalence of genomic alterations in Xerna tumor microenvironment subtypes in triple negative breast cancer patients
Abstract:This study investigates the prevalence of genomic alterations in [...]
Prevalence of genomic alterations in Xerna tumor microenvironment subtypes in colorectal cancer patients
This study explores the distribution of genomic alterations in [...]
Profiling microsatellite instability using RNA sequencing data
This study introduces a machine learning model that uses [...]
ResponderID™ KRAS: Biology-Driven Machine Learning to Personalize KRAS Inhibitor Therapeutics
Traditional biomarkers often fail to capture the dynamic nature [...]
