{"title":"Deep IDA: A Deep Learning Approach for Integrative Discriminant Analysis of Multi-omics Data with Feature Ranking- An Application to COVID-19","authors":"Jiuzhou Wang, S. Safo","doi":"10.1093/bioadv/vbae060","DOIUrl":null,"url":null,"abstract":"\n \n \n Many diseases are complex heterogeneous conditions that affect multiple organs in the body and depend on the interplay between several factors that include molecular and environmental factors, requiring a holistic approach to better understand disease pathobiology. Most existing methods for integrating data from multiple sources and classifying individuals into one of multiple classes or disease groups have mainly focused on linear relationships despite the complexity of these relationships. On the other hand, methods for nonlinear association and classification studies are limited in their ability to identify variables to aid in our understanding of the complexity of the disease or can be applied to only two data types.\n \n \n \n We propose Deep IDA (Integrative Discriminant Analysis), a deep learning method to learn complex nonlinear transformations of two or more views such that resulting projections have maximum association and maximum separation. Further, we propose a feature ranking approach based on ensemble learning for interpretatble results. We test Deep IDA on both simulated data and two large real-world datasets, including RNA sequencing, metabolomics, and proteomics data pertaining to COVID-19 severity. We identified signatures that better discriminated COVID-19 patient groups, and related to neurological conditions, cancer, and metabolic diseases, corroborating current research findings and heightening the need to study the post sequelae effects of COVID-19 to devise effective treatments and to improve patient care.\n \n \n \n Our algorithms are implemented in PyTorch and available at: https://github.com/JiuzhouW/DeepIDA.\n \n \n \n Supplementary materials are available at Bioinformatics Advances online\n","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":"28 19","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioadv/vbae060","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0
Abstract
Many diseases are complex heterogeneous conditions that affect multiple organs in the body and depend on the interplay between several factors that include molecular and environmental factors, requiring a holistic approach to better understand disease pathobiology. Most existing methods for integrating data from multiple sources and classifying individuals into one of multiple classes or disease groups have mainly focused on linear relationships despite the complexity of these relationships. On the other hand, methods for nonlinear association and classification studies are limited in their ability to identify variables to aid in our understanding of the complexity of the disease or can be applied to only two data types.
We propose Deep IDA (Integrative Discriminant Analysis), a deep learning method to learn complex nonlinear transformations of two or more views such that resulting projections have maximum association and maximum separation. Further, we propose a feature ranking approach based on ensemble learning for interpretatble results. We test Deep IDA on both simulated data and two large real-world datasets, including RNA sequencing, metabolomics, and proteomics data pertaining to COVID-19 severity. We identified signatures that better discriminated COVID-19 patient groups, and related to neurological conditions, cancer, and metabolic diseases, corroborating current research findings and heightening the need to study the post sequelae effects of COVID-19 to devise effective treatments and to improve patient care.
Our algorithms are implemented in PyTorch and available at: https://github.com/JiuzhouW/DeepIDA.
Supplementary materials are available at Bioinformatics Advances online
期刊介绍:
ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications.
The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.