AI Medical Compendium Journal:
Cancer discovery

Showing 1 to 10 of 17 articles

Artificial Intelligence Predicts Drug Response.

Cancer discovery
A new artificial intelligence-based predictive modeling framework called DrugCell could accurately predict effective drugs and treatment combinations based on tumor genotype, according to a proof-of-concept analysis.

Machine-Learning and Chemicogenomics Approach Defines and Predicts Cross-Talk of Hippo and MAPK Pathways.

Cancer discovery
Hippo pathway dysregulation occurs in multiple cancers through genetic and nongenetic alterations, resulting in translocation of YAP to the nucleus and activation of the TEAD family of transcription factors. Unlike other oncogenic pathways such as RA...

Deep Learning to Estimate RECIST in Patients with NSCLC Treated with PD-1 Blockade.

Cancer discovery
Real-world evidence (RWE), conclusions derived from analysis of patients not treated in clinical trials, is increasingly recognized as an opportunity for discovery, to reduce disparities, and to contribute to regulatory approval. Maximal value of RWE...

MANIFEST: Multiomic Platform for Cancer Immunotherapy.

Cancer discovery
Immunotherapy has revolutionized survival outcomes for many patients diagnosed with cancer. However, biomarkers that can reliably distinguish treatment responders from nonresponders, predict potential life-threatening and life-changing drug-induced t...

The Hallmarks of Predictive Oncology.

Cancer discovery
As the field of artificial intelligence evolves rapidly, these hallmarks are intended to capture fundamental, complementary concepts necessary for the progress and timely adoption of predictive modeling in precision oncology. Through these hallmarks,...

Advancing Cancer Prevention through an AI-Based Integration of Traditional and Western Medicine.

Cancer discovery
Traditional Chinese medicine has accumulated a wealth of experiences in individualized cancer prevention and serves as a complement to Western medicine. We propose an artificial intelligence-based integration of traditional and Western medicine as a ...

Gene-Specific Machine Learning Models to Classify Driver Mutations in Clonal Hematopoiesis.

Cancer discovery
There is no general consensus on the set of mutations capable of driving the age-related clonal expansions in hematopoietic stem cells known as clonal hematopoiesis, and current variant classifications typically rely on rules derived from expert know...

Identification of Clonal Hematopoiesis Driver Mutations through In Silico Saturation Mutagenesis.

Cancer discovery
Clonal hematopoiesis (CH) is a phenomenon of clonal expansion of hematopoietic stem cells driven by somatic mutations affecting certain genes. Recently, CH has been linked to the development of hematologic malignancies, cardiovascular diseases, and o...

A Deep Learning Model for Cancer Type Prediction Sets a New Standard.

Cancer discovery
Classifying tumor types using machine learning approaches is not always trivial, particularly for challenging cases such as cancers of unknown primary. In this issue of Cancer Discovery, Darmofal and colleagues describe a new tool that uses informati...

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.

Cancer discovery
UNLABELLED: Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explore...