ArticlePloS one2015
Influence of Feature Encoding and Choice of Classifier on Disease Risk Prediction in Genome-Wide Association Studies.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Evaluating genetic-based disease prediction approaches through simulation.Human genetics · 2026Article
- PROVGEN: A Privacy-Preserving Approach for Outcome Validation in Genomic Research.Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium · 2026Article
- Using genomic context informed genotype data and within-model ancestry adjustment to classify type 2 diabetes.Communications medicine · 2025Article
- Integration of Genetic Information to Improve Brain Age Gap Estimation Models in the UK Biobank.Human brain mapping · 2025Article
- Genetic variations as predictors of dispositional and dyadic empathy-a couple study.Scientific reports · 2024Article
- Genome-wide association studies of ischemic stroke based on interpretable machine learning.PeerJ. Computer science · 2024Article
- Genetic risk assessment based on association and prediction studies.Scientific reports · 2023Article
- Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.Sensors (Basel, Switzerland) · 2023Article
- Privacy-preserving cancer type prediction with homomorphic encryption.Scientific reports · 2023Article
- Deep polygenic neural network for predicting and identifying yield-associated genes in Indonesian rice accessions.Scientific reports · 2022Article
- Predicting breast cancer risk using interacting genetic and demographic factors and machine learning.Scientific reports · 2020Article
- Pipeline design to identify key features and classify the chemotherapy response on lung cancer patients using large-scale genetic data.BMC systems biology · 2018Article
- Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls.Scientific reports · 2018Article
- PoCos: Population Covering Locus Sets for Risk Assessment in Complex Diseases.PLoS computational biology · 2016Article
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Authors and funding
3 authors.
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Abstract
Various attempts have been made to predict the individual disease risk based on genotype data from genome-wide association studies (GWAS). However, most studies only investigated one or two classification algorithms and feature encoding schemes. In this study, we applied seven different classification algorithms on GWAS case-control data sets for seven different diseases to create models for disease risk prediction. Further, we used three different encoding schemes for the genotypes of single nucleotide polymorphisms (SNPs) and investigated their influence on the predictive performance of these models. Our study suggests that an additive encoding of the SNP data should be the preferred encoding scheme, as it proved to yield the best predictive performances for all algorithms and data sets. Furthermore, our results showed that the differences between most state-of-the-art classification algorithms are not statistically significant. Consequently, we recommend to prefer algorithms with simple models like the linear support vector machine (SVM) as they allow for better subsequent interpretation without significant loss of accuracy.
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