Dataset Information

Deep Learning for Integrated Analysis of Insulin Resistance with Multi-Omics Data.

ABSTRACT: Technological advances in next-generation sequencing (NGS) have made it possible to uncover extensive and dynamic alterations in diverse molecular components and biological pathways across healthy and diseased conditions. Large amounts of multi-omics data originating from emerging NGS experiments require feature engineering, which is a crucial step in the process of predictive modeling. The underlying relationship among multi-omics features in terms of insulin resistance is not well understood. In this study, using the multi-omics data of type II diabetes from the Integrative Human Microbiome Project, from 10,783 features, we conducted a data analytic approach to elucidate the relationship between insulin resistance and multi-omics features, including microbiome data. To better explain the impact of microbiome features on insulin classification, we used a developed deep neural network interpretation algorithm for each microbiome feature's contribution to the discriminative model output in the samples.

SUBMITTER: Huang E

PROVIDER: S-EPMC7918166 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Deep Learning for Integrated Analysis of Insulin Resistance with Multi-Omics Data.

Huang Eunchong E Kim Sarah S Ahn TaeJin T

Journal of personalized medicine 20210215 2

Technological advances in next-generation sequencing (NGS) have made it possible to uncover extensive and dynamic alterations in diverse molecular components and biological pathways across healthy and diseased conditions. Large amounts of multi-omics data originating from emerging NGS experiments require feature engineering, which is a crucial step in the process of predictive modeling. The underlying relationship among multi-omics features in terms of insulin resistance is not well understood. ...[more]

PMID: 33671853

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Project description:BackgroundBreast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival lengths, indicating a need to identify prognostic biomarkers for personalized diagnosis and treatment. With the development of new technologies such as next-generation sequencing, multi-omics information are becoming available for a more thorough evaluation of a patient's condition. In this study, we aim to improve breast cancer overall survival prediction by integrating multi-omics data (e.g., gene expression, DNA methylation, miRNA expression, and copy number variations (CNVs)).MethodsMotivated by multi-view learning, we propose a novel strategy to integrate multi-omics data for breast cancer survival prediction by applying complementary and consensus principles. The complementary principle assumes each -omics data contains modality-unique information. To preserve such information, we develop a concatenation autoencoder (ConcatAE) that concatenates the hidden features learned from each modality for integration. The consensus principle assumes that the disagreements among modalities upper bound the model errors. To get rid of the noises or discrepancies among modalities, we develop a cross-modality autoencoder (CrossAE) to maximize the agreement among modalities to achieve a modality-invariant representation. We first validate the effectiveness of our proposed models on the MNIST simulated data. We then apply these models to the TCCA breast cancer multi-omics data for overall survival prediction.ResultsFor breast cancer overall survival prediction, the integration of DNA methylation and miRNA expression achieves the best overall performance of 0.641?±?0.031 with ConcatAE, and 0.63?±?0.081 with CrossAE. Both strategies outperform baseline single-modality models using only DNA methylation (0.583?±?0.058) or miRNA expression (0.616?±?0.057).ConclusionsIn conclusion, we achieve improved overall survival prediction performance by utilizing either the complementary or consensus information among multi-omics data. The proposed ConcatAE and CrossAE models can inspire future deep representation-based multi-omics integration techniques. We believe these novel multi-omics integration models can benefit the personalized diagnosis and treatment of breast cancer patients.

Dataset Information

Deep Learning for Integrated Analysis of Insulin Resistance with Multi-Omics Data.

Publications

Deep Learning for Integrated Analysis of Insulin Resistance with Multi-Omics Data.

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