典型相关
判别式
相关性
表型
基因型
多元统计
单核苷酸多态性
逻辑回归
计算生物学
人工智能
计算机科学
模式识别(心理学)
生物
机器学习
遗传学
数学
基因
几何学
作者
Lei Du,Fang Liu,Kefei Liu,Xiaohui Yao,Shannon L. Risacher,Junwei Han,Lei Guo,Andrew J. Saykin,Li Shen
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2020-07-01
卷期号:36 (Supplement_1): i371-i379
被引量:27
标识
DOI:10.1093/bioinformatics/btaa434
摘要
Abstract Motivation Brain imaging genetics studies the complex associations between genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). The neurodegenerative disorders usually exhibit the diversity and heterogeneity, originating from which different diagnostic groups might carry distinct imaging QTs, SNPs and their interactions. Sparse canonical correlation analysis (SCCA) is widely used to identify bi-multivariate genotype–phenotype associations. However, most existing SCCA methods are unsupervised, leading to an inability to identify diagnosis-specific genotype–phenotype associations. Results In this article, we propose a new joint multitask learning method, named MT–SCCALR, which absorbs the merits of both SCCA and logistic regression. MT–SCCALR learns genotype–phenotype associations of multiple tasks jointly, with each task focusing on identifying one diagnosis-specific genotype–phenotype pattern. Meanwhile, MT–SCCALR cannot only select relevant SNPs and imaging QTs for each diagnostic group alone, but also allows the selection of those shared by multiple diagnostic groups. We derive an efficient optimization algorithm whose convergence to a local optimum is guaranteed. Compared with two state-of-the-art methods, MT–SCCALR yields better or similar canonical correlation coefficients and classification performances. In addition, it owns much better discriminative canonical weight patterns of great interest than competitors. This demonstrates the power and capability of MTSCCAR in identifying diagnostically heterogeneous genotype–phenotype patterns, which would be helpful to understand the pathophysiology of brain disorders. Availability and implementation The software is publicly available at https://github.com/dulei323/MTSCCALR. Supplementary information Supplementary data are available at Bioinformatics online.
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