Integration of Multimodal Data from Disparate Sources for Identifying Disease Subtypes

• Published in: Biology (Basel, Switzerland) Vol. 11; no. 3; p. 360
• Main Authors: Zhou, Kaiyue, Kottoori, Bhagya Shree, Munj, Seeya Awadhut, Zhang, Zhewei, Draghici, Sorin, Arslanturk, Suzan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.02.2022; MDPI
• Subjects: Acute myeloid leukemia; Adenocarcinoma; Cancer; cancer progression; Datasets; deep learning; DNA methylation; Etiology; Gene expression; Glioblastoma; Glioblastoma multiforme; imputation; Learning; miRNA; mRNA; multimodal data fusion; Neural networks; Pancreatic cancer; Patients; Sensory integration; deep learning; cancer progression; imputation; multimodal data fusion
• ISSN: 2079-7737; 2079-7737
• DOI: 10.3390/biology11030360

Studies over the past decade have generated a wealth of molecular data that can be leveraged to better understand cancer risk, progression, and outcomes. However, understanding the progression risk and differentiating long- and short-term survivors cannot be achieved by analyzing data from a single modality due to the heterogeneity of disease. Using a scientifically developed and tested deep-learning approach that leverages aggregate information collected from multiple repositories with multiple modalities (e.g., mRNA, DNA Methylation, miRNA) could lead to a more accurate and robust prediction of disease progression. Here, we propose an autoencoder based multimodal data fusion system, in which a fusion encoder flexibly integrates collective information available through multiple studies with partially coupled data. Our results on a fully controlled simulation-based study have shown that inferring the missing data through the proposed data fusion pipeline allows a predictor that is superior to other baseline predictors with missing modalities. Results have further shown that short- and long-term survivors of glioblastoma multiforme, acute myeloid leukemia, and pancreatic adenocarcinoma can be successfully differentiated with an AUC of 0.94, 0.75, and 0.96, respectively.