ArticleNature communications2021
Improving gene function predictions using independent transcriptional components.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 39 citations in OpenAlex.
- Exploring the transcriptional crosstalk between adipose tissue and locoregional recurrence in breast cancer using independent component analysis.Functional & integrative genomics · 2026Article
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- Deep learning-based semantic matching of cis-regulatory DNA sequences facilitates the prediction of gene function.Nature plants · 2026Article
- Association of copy number alterations with the immune transcriptomic landscape in cancer.NPJ systems biology and applications · 2026Article
- MKFGO: integrating multi-source knowledge fusion with pretrained language model for high-accuracy protein function prediction.Briefings in bioinformatics · 2025Article
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- DPFunc: accurately predicting protein function via deep learning with domain-guided structure information.Nature communications · 2025Article
- iModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendia.PLoS computational biology · 2024Article
- Neoadjuvant immune checkpoint blockade in women with mismatch repair deficient endometrial cancer: a phase I study.Nature communications · 2024Article
- Independent transcriptional patterns reveal biological processes associated with disease-free survival in early colorectal cancer.Communications medicine · 2024Article
- Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data.BMC bioinformatics · 2024Article
- A global genetic interaction network by single-cell imaging and machine learning.Cell systems · 2023Article
- Patterns of transcription factor binding and epigenome at promoters allow interpretable predictability of multiple functions of non-coding and coding genes.Computational and structural biotechnology journal · 2023Article
- Integrating unsupervised language model with triplet neural networks for protein gene ontology prediction.PLoS computational biology · 2022Article
- TripletGO: Integrating Transcript Expression Profiles with Protein Homology Inferences for Gene Function Prediction.Genomics, proteomics & bioinformatics · 2022Article
- Integrative analysis of expression profile indicates the ECM receptor and LTP dysfunction in the glioma-related epilepsy.BMC genomics · 2022Article
- Article
- Lysophosphatidic acid receptor 6 regulated by miR-27a-3p attenuates tumor proliferation in breast cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2022Article
- Article
- Integrated Analysis of Expression Profile and Potential Pathogenic Mechanism of Temporal Lobe Epilepsy With Hippocampal Sclerosis.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The interpretation of high throughput sequencing data is limited by our incomplete functional understanding of coding and non-coding transcripts. Reliably predicting the function of such transcripts can overcome this limitation. Here we report the use of a consensus independent component analysis and guilt-by-association approach to predict over 23,000 functional groups comprised of over 55,000 coding and non-coding transcripts using publicly available transcriptomic profiles. We show that, compared to using Principal Component Analysis, Independent Component Analysis-derived transcriptional components enable more confident functionality predictions, improve predictions when new members are added to the gene sets, and are less affected by gene multi-functionality. Predictions generated using human or mouse transcriptomic data are made available for exploration in a publicly available web portal.
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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.