Article

Evaluation of single-sample network inference methods for precision oncology

2023-07-12

Abstract excerpt

A major challenge in precision oncology is to identify targetable cancer vulnerabilities in individual patients. Modelling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning and predicting gene interactions for a group of patients. However, to...

Topics

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

Identifiers and source

Literature Corpus work: fb2a164b-5d1f-52de-aa3e-6617eac41b1e
DOI: 10.1101/2023.07.11.548508

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Evaluation of single-sample network inference methods for precision oncologyDOI 10.1101/2023.07.11.548508Read

Patient-specific gene networks reveal novel subtypes and predictive biomarkers in lung cancerSemantic proximity Read

Patient-specific gene co-expression networks reveal novel subtypes and predictive biomarkers in lung adenocarcinomaSemantic proximity Read

scHumanNet: a single-cell network analysis platform for the study of cell-type specificity of disease genesSemantic proximity Read

DysRegNet: Patient-specific and confounder-aware dysregulated network inferenceSemantic proximity Read

An individualized causal framework for learning intercellular communication networks that define microenvironments of individual tumorsSemantic proximity Read

Network-based stratification of tumor mutations.Semantic proximity Read

Integrating genetic and gene expression data in network-based stratification analysis of cancersSemantic proximity Read

Principles and strategies for developing network models in cancer.Semantic proximity Read

Single-cell network biology for resolving cellular heterogeneity in human diseases.Semantic proximity Read

SCING: Single Cell INtegrative Gene regulatory network inference elucidates robust, interpretable gene regulatory networksSemantic proximity Read

Personalized Network Modeling of the Pan-Cancer Patient and Cell Line InteractomeSemantic proximity Read

NetSig: network-based discovery from cancer genomes.Semantic proximity Read