Article

Facilitating “Omics” to Phenotype Classification Using a User-Friendly AI-Driven Platform: Application to Cancer Prognostics

2023-08-24

Abstract excerpt

Precision medicine approaches often relies on complex and integrative analysis of multiple biomarkers from “omics” data to generate insights that can help either diagnostics, prognostics or therapeutical decisions. Such insights are often made using Machine learning (ML) models that make sample classification for a particular phenotype (yes/no). Building such models is a challenge and time-consuming, requiring adv...

Topics

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

Identifiers and source

Literature Corpus work: 79b6b627-1232-54cc-8e93-655a2448b191
DOI: 10.20944/preprints202308.1712.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Facilitating “Omics” to Phenotype Classification Using a User-Friendly AI-Driven Platform: Application to Cancer PrognosticsDOI 10.20944/preprints202308.1712.v1Read

Integrating Omics Data and AI for Cancer Diagnosis and Prognosis: A Systematic ReviewSemantic proximity Read

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision OncologySemantic proximity Read

Multi-Omics Prognostic Marker Discovery and Survival Modeling: A Case Study on Pan-Cancer Survival Analyses in Women’s CancersSemantic proximity Read

Feature Importance in Predicting Clinical Outcome: Statistics vs. Explainable Artificial IntelligenceSemantic proximity Read

Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized MedicineSemantic proximity Read

AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosisSemantic proximity Read

Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized MedicineSemantic proximity Read

Precision Oncology in the Era of Genomics and Artificial IntelligenceSemantic proximity Read

AI‐Driven Multi‐Omics Integrated Applications Using Diverse Neural Networks for Breast Cancer Diagnostic Screening and Biomarker DiscoverySemantic proximity Read

Pan-cancer evaluation of gene expression and somatic alteration data for cancer prognosis predictionSemantic proximity Read

Machine Learning Approaches for Identification of Potential Biomarkers from Cancer Omics DataSemantic proximity Read

Epigenetic profile drives accurate survival prediction in breast cancer via a multi-omics machine learning modelSemantic proximity Read