In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 7 ( 2022-7-15), p. e1010328-
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010328
DOI:
10.1371/journal.pcbi.1010328.g001
DOI:
10.1371/journal.pcbi.1010328.g002
DOI:
10.1371/journal.pcbi.1010328.g003
DOI:
10.1371/journal.pcbi.1010328.g004
DOI:
10.1371/journal.pcbi.1010328.g005
DOI:
10.1371/journal.pcbi.1010328.g006
DOI:
10.1371/journal.pcbi.1010328.g007
DOI:
10.1371/journal.pcbi.1010328.t001
DOI:
10.1371/journal.pcbi.1010328.s001
DOI:
10.1371/journal.pcbi.1010328.s002
DOI:
10.1371/journal.pcbi.1010328.s003
DOI:
10.1371/journal.pcbi.1010328.s004
DOI:
10.1371/journal.pcbi.1010328.s005
DOI:
10.1371/journal.pcbi.1010328.s006
DOI:
10.1371/journal.pcbi.1010328.s007
DOI:
10.1371/journal.pcbi.1010328.s008
DOI:
10.1371/journal.pcbi.1010328.s009
DOI:
10.1371/journal.pcbi.1010328.s010
DOI:
10.1371/journal.pcbi.1010328.s011
DOI:
10.1371/journal.pcbi.1010328.s012
DOI:
10.1371/journal.pcbi.1010328.s013
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6
Permalink