Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data

• Published in: PLoS computational biology 2022-07, Vol.18 (7), p.e1010328-e1010328
• Main Authors: Liu, Long, Meng, Qingyu, Weng, Cherry, Lu, Qing, Wang, Tong, Wen, Yalu
• Format: Article
• Language: English
• Subjects: Alzheimer's disease; Alzheimers disease; Artificial neural networks; Biology and Life Sciences; Computer and Information Sciences; Data analysis; Deep learning; Electronic data processing; Forecasts and trends; Genes; Genetic diversity; Genetic variance; Genomes; Genomic analysis; Genomics; Genotype & phenotype; Health risks; Machine learning; Medicine and Health Sciences; Methods; Neural networks; Noise prediction; Physical Sciences; Precision medicine; Prediction models; Research and Analysis Methods; Risk; Risk factors (Health); Transfer learning
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1010328

Building an accurate disease risk prediction model is an essential step in the modern quest for precision medicine. While high-dimensional genomic data provides valuable data resources for the investigations of disease risk, their huge amount of noise and complex relationships between predictors and outcomes have brought tremendous analytical challenges. Deep learning model is the state-of-the-art methods for many prediction tasks, and it is a promising framework for the analysis of genomic data. However, deep learning models generally suffer from the curse of dimensionality and the lack of biological interpretability, both of which have greatly limited their applications. In this work, we have developed a deep neural network (DNN) based prediction modeling framework. We first proposed a group-wise feature importance score for feature selection, where genes harboring genetic variants with both linear and non-linear effects are efficiently detected. We then designed an explainable transfer-learning based DNN method, which can directly incorporate information from feature selection and accurately capture complex predictive effects. The proposed DNN-framework is biologically interpretable, as it is built based on the selected predictive genes. It is also computationally efficient and can be applied to genome-wide data. Through extensive simulations and real data analyses, we have demonstrated that our proposed method can not only efficiently detect predictive features, but also accurately predict disease risk, as compared to many existing methods.