Dual-Extraction Modeling: A multimodal deep learning architecture for phenotypic prediction and functional gene mining of complex traits.

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Abstract

Despite considerable advancements in extracting crucial insights from bio-omics data to unravel the intricate mechanisms underlying complex traits, the absence of a universal multimodal computational tool with robust interpretability for accurate phenotype prediction and identification of trait-associated genes remains a challenge. This study introduces the Dual-Extraction Modeling (DEM) approach, a multimodal deep learning architecture designed to extract representative features from heterogeneous omics datasets, enabling the prediction of complex trait phenotypes. Through comprehensive benchmarking experiments, we demonstrate DEM’s efficacy in classification and regression prediction of complex traits. DEM consistently exhibits superior accuracy, robustness, generalizability, and flexibility. Notably, we establish its effectiveness in predicting pleiotropic genes influencing both flowering time and rosette leaf number, underscoring its commendable interpretability. Additionally, user-friendly software has been developed to facilitate the seamless utilization of DEM’s functions. In summary, this study presents a state-of-the-art approach with the capability to effectively predict qualitative and quantitative traits, as well as identify functional genes, affirming its potential as a valuable tool in exploring the genetic basis of complex traits. Source code and software of DEM are available at https://github.com/cma2015/DEM/.Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.