ArticleNature computational science2022
A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data.
Article in Nature computational science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
22 citing papers in PubMed.
- TMO-Net+: An Enhanced Tumor Multi-Omics Pre-Trained Network for Multi-Task Learning in Oncology.Genes · 2026Article
- Predicting natural variation in the yeast phenotypic landscape with machine learning.Molecular systems biology · 2025Article
- MAMVCL: Multi-Atlas Guided Multi-View Contrast Learning for Autism Spectrum Disorder Classification.Brain sciences · 2025Article
- scSDNE: A semi-supervised method for inferring cell-cell interactions based on graph embedding.PLoS computational biology · 2025Article
- Integrating Deep Learning Models with Genome-Wide Association Study-Based Identification Enhanced Phenotype Predictions in Group AJournal of microbiology and biotechnology · 2025Article
- Lorentz-regularized interpretable VAE for multi-scale single-cell transcriptomic and epigenomic embeddings.Frontiers in genetics · 2025Article
- DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data.Translational psychiatry · 2024Article
- Analyzing Wav2Vec 1.0 Embeddings for Cross-Database Parkinson's Disease Detection and Speech Features Extraction.Sensors (Basel, Switzerland) · 2024Article
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology.Genome biology · 2024Article
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.Genome medicine · 2023Article
- Predictive scale-bridging simulations through active learning.Scientific reports · 2023Article
- Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks.Nucleic acids research · 2023Article
- Joint variational autoencoders for multimodal imputation and embedding.Nature machine intelligence · 2023Article
- scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs.Cell systems · 2023Article
- Applications of deep learning in understanding gene regulation.Cell reports methods · 2023Review
- Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment.The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023). Joint International Conference on Deep Learning, Big Data and Blockchain (4th : 2023 : Marrakech, Morocco ; Online) · 2023Article
- Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.