ArticleBMC genomics2019
ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell Data.Biology · 2025Article
- Protocol for comparative gene expression data analysis between brains and organoids using a cloud-based web app.STAR protocols · 2024Article
- scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network.Cell reports. Medicine · 2024Article
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- Article
- Gene communities in co-expression networks across different tissues.PLoS computational biology · 2023Article
- CMOT: Cross-Modality Optimal Transport for multimodal inference.Genome biology · 2023Article
- Joint variational autoencoders for multimodal imputation and embedding.Nature machine intelligence · 2023Article
- BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids.Cell reports methods · 2023Article
- Applications of deep learning in understanding gene regulation.Cell reports methods · 2023Review
- scTenifoldKnk: An efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation.Patterns (New York, N.Y.) · 2022Article
- A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data.Nature computational science · 2022Article
- Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics.Communications biology · 2021Article
- Ten Years of Collaborative Progress in the Quest for Orthologs.Molecular biology and evolution · 2021Article
- Co-expression networks in Chlamydomonas reveal significant rhythmicity in batch cultures and empower gene function discovery.The Plant cell · 2021Article
- Juxtapose: a gene-embedding approach for comparing co-expression networks.BMC bioinformatics · 2021Article
- Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution.Frontiers in genetics · 2021Review
- scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data.Patterns (New York, N.Y.) · 2020Article
- Multiview learning for understanding functional multiomics.PLoS computational biology · 2020Review
- The International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019): conference summary and innovations in genomics.BMC genomics · 2019Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
backgroundThe coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links.
resultsWe demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10
conclusionsManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster.
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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.