ArticleBMC systems biology2018
GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization.
Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
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- A best-match approach for gene set analyses in embedding spaces.Genome research · 2024Article
- A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data.PLoS computational biology · 2024Article
- Cancer Mutations Converge on a Collection of Protein Assemblies to Predict Resistance to Replication Stress.Cancer discovery · 2024Article
- Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference.Genome biology · 2024Article
- SLC7A11, a potential immunotherapeutic target in lung adenocarcinoma.Scientific reports · 2023Article
- Vis-SPLIT: Interactive Hierarchical Modeling for mRNA Expression Classification.IEEE Visualization Conference : VIS. IEEE Conference on Visualization · 2023Article
- clusterMaker2: a major update to clusterMaker, a multi-algorithm clustering app for Cytoscape.BMC bioinformatics · 2023Article
- Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings, with drug-repurposing applications in COVID-19.Bioinformatics (Oxford, England) · 2023Article
- scCapsNet-mask: an updated version of scCapsNet with extended applicability in functional analysis related to scRNA-seq data.BMC bioinformatics · 2022Article
- Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities.Chemical research in toxicology · 2022Review
- Single-Cell Transcriptomic Census of Endothelial Changes Induced by Matrix Stiffness and the Association with Atherosclerosis.Advanced functional materials · 2022Article
- The ERα-NRF2 signalling axis promotes bicalutamide resistance in prostate cancer.Cell communication and signaling : CCS · 2022Article
- MOMA: a multi-task attention learning algorithm for multi-omics data interpretation and classification.Bioinformatics (Oxford, England) · 2022Article
- Deep learning using bulk RNA-seq data expands cell landscape identification in tumor microenvironment.Oncoimmunology · 2022Article
- A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases.International journal of molecular sciences · 2021Review
- CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training.BMC bioinformatics · 2021Article
- AutoEncoder-Based Computational Framework for Tumor Microenvironment Decomposition and Biomarker Identification in Metastatic Melanoma.Frontiers in genetics · 2021Article
- MultiCapsNet: A General Framework for Data Integration and Interpretable Classification.Frontiers in genetics · 2021Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundBioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter-gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets.
resultsIn this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets' ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets.
conclusionsUsing autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.
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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.