ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2020
Using Transcriptional Signatures to Find Cancer Drivers with LURE.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Best holdout assessment is sufficient for cancer transcriptomic model selection.Patterns (New York, N.Y.) · 2024Article
- Optimizer's dilemma: optimization strongly influences model selection in transcriptomic prediction.Bioinformatics advances · 2024Article
- Widespread redundancy in -omics profiles of cancer mutation states.Genome biology · 2022Article
- eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants.PLoS computational biology · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
Cancer genome projects have produced multidimensional datasets on thousands of samples. Yet, depending on the tumor type, 5-50% of samples have no known driving event. We introduce a semi-supervised method called Learning UnRealized Events (LURE) that uses a progressive label learning framework and minimum spanning analysis to predict cancer drivers based on their altered samples sharing a gene expression signature with the samples of a known event. We demonstrate the utility of the method on the TCGA Pan-Cancer Atlas dataset for which it produced a high-confidence result relating 59 new connections to 18 known mutation events including alterations in the same gene, family, and pathway. We give examples of predicted drivers involved in TP53, telomere maintenance, and MAPK/RTK signaling pathways. LURE identifies connections between genes with no known prior relationship, some of which may offer clues for targeting specific forms of cancer. Code and Supplemental Material are available on the LURE website: https://sysbiowiki.soe.ucsc.edu/lure.
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Identifiers
31797609PMC6924983What OpenQuestion holds
Registered trials
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