ArticlePeerJ2018
Ensemble learning for detecting gene-gene interactions in colorectal cancer.
Article in PeerJ, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 31 citations in OpenAlex.
- Comparative performance of multiple ensemble learning models for preoperative prediction of tumor deposits in rectal cancer based on MR imaging.Scientific reports · 2025Article
- Association of NID2 SNPs with Glioma Risk and Prognosis in the Chinese Population.Neuromolecular medicine · 2024Article
- Revealing third-order interactions through the integration of machine learning and entropy methods in genomic studies.BioData mining · 2024Article
- Breast cancer prediction using different machine learning methods applying multi factors.Journal of cancer research and clinical oncology · 2023Article
- Genome-wide association study as a powerful tool for dissecting competitive traits in legumes.Frontiers in plant science · 2023Review
- NSPA: characterizing the disease association of multiple genetic interactions at single-subject resolution.Bioinformatics advances · 2023Article
- Article
- Genetic interactions effects for cancer disease identification using computational models: a review.Medical & biological engineering & computing · 2021Review
- Investigation of gene-gene interactions in cardiac traits and serum fatty acid levels in the LURIC Health Study.PloS one · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) has a high incident rate in both men and women and is affecting millions of people every year. Genome-wide association studies (GWAS) on CRC have successfully revealed common single-nucleotide polymorphisms (SNPs) associated with CRC risk. However, they can only explain a very limited fraction of the disease heritability. One reason may be the common uni-variable analyses in GWAS where genetic variants are examined one at a time. Given the complexity of cancers, the non-additive interaction effects among multiple genetic variants have a potential of explaining the missing heritability. In this study, we employed two powerful ensemble learning algorithms, random forests and gradient boosting machine (GBM), to search for SNPs that contribute to the disease risk through non-additive gene-gene interactions. We were able to find 44 possible susceptibility SNPs that were ranked most significant by both algorithms. Out of those 44 SNPs, 29 are in coding regions. The 29 genes include
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.