情态动词
计算机科学
深度学习
人工智能
机器学习
特征(语言学)
临床表型
歧管(流体力学)
特征学习
非线性降维
模式识别(心理学)
表型
生物
降维
基因
工程类
哲学
化学
高分子化学
机械工程
生物化学
语言学
作者
Nam D. Nguyen,Jiawei Huang,Daifeng Wang
标识
DOI:10.1038/s43588-021-00185-x
摘要
The phenotypes of complex biological systems are fundamentally driven by various multi-scale mechanisms. Multi-modal data, such as single cell multi-omics data, enables a deeper understanding of underlying complex mechanisms across scales for phenotypes. We developed an interpretable regularized learning model, deepManReg, to predict phenotypes from multi-modal data. First, deepManReg employs deep neural networks to learn cross-modal manifolds and then to align multi-modal features onto a common latent space. Second, deepManReg uses cross-modal manifolds as a feature graph to regularize the classifiers for improving phenotype predictions and also for prioritizing the multi-modal features and cross-modal interactions for the phenotypes. We applied deepManReg to (1) an image dataset of handwritten digits with multi-features and (2) single cell multi-modal data (Patch-seq data) including transcriptomics and electrophysiology for neuronal cells in the mouse brain. We show that deepManReg improved phenotype prediction in both datasets, and also prioritized genes and electrophysiological features for the phenotypes of neuronal cells.
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