Empowering individual trait prediction using interactions for precision medicine

• Main Authors: Gola, D. (Author), König, I.R (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: algorithm; Algorithms; Bioinformatics; Classification; Coronary artery disease; Decision trees; Diabetes Mellitus, Type 2; Dimensionality reduction; Diseases; Feature extraction; Forecasting; Generalized linear model; Genetic epidemiologies; genetics; human; Humans; Individual prediction; Interactions; Learning systems; machine learning; Machine learning; Machine Learning; Machine learning methods; multifactor dimensionality reduction; Multifactor Dimensionality Reduction; Multifactor dimensionality reductions; non insulin dependent diabetes mellitus; personalized medicine; Personalized medicine; Power, Psychological; Precision Medicine; Prediction; Prediction performance; Predictive analytics; State-of-the-art algorithms
• Online Access: View Fulltext in Publisher

 Background: One component of precision medicine is to construct prediction models with their predicitve ability as high as possible, e.g. to enable individual risk prediction. In genetic epidemiology, complex diseases like coronary artery disease, rheumatoid arthritis, and type 2 diabetes, have a polygenic basis and a common assumption is that biological and genetic features affect the outcome under consideration via interactions. In the case of omics data, the use of standard approaches such as generalized linear models may be suboptimal and machine learning methods are appealing to make individual predictions. However, most of these algorithms focus mostly on main or marginal effects of the single features in a dataset. On the other hand, the detection of interacting features is an active area of research in the realm of genetic epidemiology. One big class of algorithms to detect interacting features is based on the multifactor dimensionality reduction (MDR). Here, we further develop the model-based MDR (MB-MDR), a powerful extension of the original MDR algorithm, to enable interaction empowered individual prediction. Results: Using a comprehensive simulation study we show that our new algorithm (median AUC: 0.66) can use information hidden in interactions and outperforms two other state-of-the-art algorithms, namely the Random Forest (median AUC: 0.54) and Elastic Net (median AUC: 0.50), if interactions are present in a scenario of two pairs of two features having small effects. The performance of these algorithms is comparable if no interactions are present. Further, we show that our new algorithm is applicable to real data by comparing the performance of the three algorithms on a dataset of rheumatoid arthritis cases and healthy controls. As our new algorithm is not only applicable to biological/genetic data but to all datasets with discrete features, it may have practical implications in other research fields where interactions between features have to be considered as well, and we made our method available as an R package (https://github.com/imbs-hl/MBMDRClassifieR). Conclusions: The explicit use of interactions between features can improve the prediction performance and thus should be included in further attempts to move precision medicine forward. © 2021, The Author(s).