Article

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

2025-02-22

Abstract excerpt

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 for assessing how amino acid substitutions affect protein function and mutation pathogenicity. To our knowledge, no ready-to-use genomic dataset exists for prediction models to identify potentially harmful mutations, which could...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 499dcfe1-d90e-5559-adf9-5b6668e74e5a
DOI: 10.1101/2025.02.17.638660

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Using The Cancer Genome Atlas from cBioPortal to Develop Genomic Datasets for Machine Learning Assisted Cancer TreatmentDOI 10.1101/2025.02.17.638660Read

Predictive Modeling of Novel Somatic Mutation Impacts on Cancer Prognosis: A Machine Learning Approach Using the COSMIC DatabaseSemantic proximity Read

PolyBoost: An enhanced genomic variant classifier using extreme gradient boosting.Semantic proximity Read

A machine learning-based analysis of nationwide cancer comprehensive genomic profiling data across cancer types to identify features associated with recommendation of genome-matched therapySemantic proximity Read

CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network.Semantic proximity Read

Evaluation of in silico tools and Chat-GPT in identifying the impact of missense variants of immune-related genes associated with immunotherapy outcomes for solid tumorsSemantic proximity Read

MARiO: predicting cancer variant pathogenicity by integrating <i>in silico</i> evaluation and patient-level mutational contextsSemantic proximity Read

CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer.Semantic proximity Read

Evaluating the Predictability of Cancer Types from 536 Somatic Mutations: A New Dataset.Semantic proximity Read

From Genomics to AI: Revolutionizing Precision Medicine in OncologySemantic proximity Read

<i>CanDrivR-CS</i> : A Cancer-Specific Machine Learning Framework for Distinguishing Recurrent and Rare VariantsSemantic proximity Read

TGPred: a tumor gene prediction webserver for analyzing structural and functional impacts of variants.Semantic proximity Read

FINDEL: A Deep Learning Approach to Efficient Artifact Removal From Cancer GenomesSemantic proximity Read