ArticleBMC genetics2016
Machine learning and data mining in complex genomic data--a review on the lessons learned in Genetic Analysis Workshop 19.
Article in BMC genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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Who cites it
7 citing papers in PubMed.
- Feature Selection Strategies for Deep Learning-Based Classification in Ultra-High-Dimensional Genomic Data.International journal of molecular sciences · 2025Article
- Acute Myeloid Leukemia Genome Characterization Study and Subtype Classification Employing Feature Selection and Bayesian Networks.Biomedicines · 2025Article
- A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction.Frontiers in bioinformatics · 2022Review
- Presidential address: Six open questions to genetic epidemiologists.Genetic epidemiology · 2019Article
- Article
- Genetic Analysis Workshop 19: methods and strategies for analyzing human sequence and gene expression data in extended families and unrelated individuals.BMC proceedings · 2016Article
- Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19.BMC proceedings · 2016Article
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Authors and funding
9 authors.
Funding
Abstract
In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data.In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets.
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Registered trials
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