Abstract
Using results from genome-wide association studies for understanding complex traits is a current challenge. Here we review how genotype data can be used with different machine learning (ML) methods to predict phenotype occurrence and severity from genotype data. We discuss common feature encoding schemes and how studies handle the often small number of samples compared to the huge number of variants. We compare which ML methods are being applied, including recent results using deep neural networks. Further, we review the application of methods for feature explanation and interpretation.
Funding source: DFG Cluster of Excellence Cardio Pulmonary Institute (CPI)
Award Identifier / Grant number: EXC 2026
Funding source: Alfons und Gertrud Kassel-Stiftung "Center for Data Science and AI"
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: This project is part of the "Center for Data Science and AI" funded by the Alfons und Gertrud Kassel-Stiftung. This work was supported by the DFG Cluster of Excellence Cardio Pulmonary Institute (CPI) [EXC 2026].
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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© 2021 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Highlight: Bioinformatics in Theory and Application
- Bioinformatics in theory and application – highlights of the 36th German Conference on Bioinformatics
- Machine learning based disease prediction from genotype data
- Placental mitochondrial function as a driver of angiogenesis and placental dysfunction
- Detecting myocardial scar using electrocardiogram data and deep neural networks
- Prediction and analysis of redox-sensitive cysteines using machine learning and statistical methods
- iMLP, a predictor for internal matrix targeting-like sequences in mitochondrial proteins
- Avoided motifs: short amino acid strings missing from protein datasets
- Functional genomics meta-analysis to identify gene set enrichment networks in cardiac hypertrophy
- Computational prediction of CRISPR-impaired non-coding regulatory regions
- Unification of functional annotation descriptions using text mining
- Detection of follicular regions in actin-stained whole slide images of the human lymph node by shock filter
- How to draw the line – Raman spectroscopy as a tool for the assessment of biomedicines
Artikel in diesem Heft
- Frontmatter
- Highlight: Bioinformatics in Theory and Application
- Bioinformatics in theory and application – highlights of the 36th German Conference on Bioinformatics
- Machine learning based disease prediction from genotype data
- Placental mitochondrial function as a driver of angiogenesis and placental dysfunction
- Detecting myocardial scar using electrocardiogram data and deep neural networks
- Prediction and analysis of redox-sensitive cysteines using machine learning and statistical methods
- iMLP, a predictor for internal matrix targeting-like sequences in mitochondrial proteins
- Avoided motifs: short amino acid strings missing from protein datasets
- Functional genomics meta-analysis to identify gene set enrichment networks in cardiac hypertrophy
- Computational prediction of CRISPR-impaired non-coding regulatory regions
- Unification of functional annotation descriptions using text mining
- Detection of follicular regions in actin-stained whole slide images of the human lymph node by shock filter
- How to draw the line – Raman spectroscopy as a tool for the assessment of biomedicines