Using The Cancer Genome Atlas from cBioPortal to Develop Genomic Datasets for Machine Learning Assisted Cancer Treatment

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 22, 2025

Abstract Predicting the impact of genetic mutations is crucial for understanding diseases like cancer. Polymorphism Phenotyping (PolyPhen) and Sorting Intolerant From Tolerant (SIFT) are key tools assessing how amino acid substitutions affect protein function mutation pathogenicity. To our knowledge, no ready-to-use genomic dataset exists prediction models to identify potentially harmful mutations, which could support research clinical decisions. This study develops non-genomic datasets using The Cancer Genome Atlas (TCGA) from cBioPortal applies machine learning predict PolyPhen SIFT scores. We explore three classification models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), an ensemble RF-XGBoost model. Experimental results show that data yields more accurate predictions than data. model performs best on data, achieving average accuracies 88.43% 95.13% SIFT, highlighting potential artificial intelligence in analysis disease treatment.

Language: Английский

Exploring Therapeutic Strategies for Androgen-Independent Prostate Cancer Using a Magnetic Coculture Platform

Biomedical Engineering Advances, Journal Year: 2025, Volume and Issue: unknown, P. 100144 - 100144

Published: Jan. 1, 2025

Language: Английский

Lung Cancer Detection Using Deep Neural Networks and Radiological Imaging

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 209 - 220

Published: Jan. 1, 2025

Language: Английский