Article

Combining explainable machine learning, demographic and multi-omic data to identify precision medicine strategies for inflammatory bowel disease

2021-03-05

Abstract excerpt

Inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohn’s disease, affect several million individuals worldwide. These diseases are heterogeneous at the clinical, immunological and genetic levels and result from a complex interaction between the host and environmental factors. Investigating drug efficacy in cultured human fresh IBD tissues can improve our understanding of the reasons why certain...

Topics

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

Identifiers and source

Literature Corpus work: fb71dc50-dab8-5ced-a346-378bc49439c5
DOI: 10.1101/2021.03.03.21252821

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Combining explainable machine learning, demographic and multi-omic data to identify precision medicine strategies for inflammatory bowel diseaseDOI 10.1101/2021.03.03.21252821Read

Multi-omics feature engineering driven by biomedical foundation models improves drug response prediction for inflammatory bowel disease patients.Semantic proximity Read

DrDimont: Explainable drug response prediction from differential analysis of multi-omics networksSemantic proximity Read

EVA: a Foundation Model Advancing Translational Drug Development in Immuno-InflammationSemantic proximity Read

Expanding large-scale mechanistic models with machine learned associations and big datasetsSemantic proximity Read

Predicted efficacy of a pharmacogenetic passport for inflammatory bowel disease.Semantic proximity Read

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseasesSemantic proximity Read

Exploring inflammatory bowel disease therapy targets through druggability genes: a Mendelian randomization study.Semantic proximity Read

CellPhenoX: An eXplainable Cell-specific machine learning method to predict clinical Phenotypes using single-cell multi-omicsSemantic proximity Read

Multi-omics data integration identifies novel biomarkers and patient subgroups in inflammatory bowel diseaseSemantic proximity Read

The Clinical Response of Upadacitinib and Risankizumab is Associated with Reduced Inflammatory Bowel Disease Anti-TNFα Inadequate Response MechanismsSemantic proximity Read

Personalizing therapy selection in inflammatory bowel disease.Semantic proximity Read

Machine learning to phenotype pain and predict response to pain interventions among young adults with irritable bowel syndromeSemantic proximity Read