ArticleBioinformatics (Oxford, England)2019
Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Trial
- Mechanism-based prediction of drug synergy via network controllability analysis of therapeutic pathways in intractable diseases.iScience · 2026Article
- Tensor decomposition of multi-dimensional splicing events across multiple tissues to identify splicing-mediated risk genes associated with complex traits.PLoS computational biology · 2025Article
- Drug Search and Design Considering Cell Specificity of Chemically Induced Gene Expression Profiles for Disease-Associated Tissues.Molecular informatics · 2025Article
- Therapeutic target prediction for orphan diseases integrating genome-wide and transcriptome-wide association studies.Nature communications · 2025Article
- SSL-VQ: vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases.Bioinformatics (Oxford, England) · 2025Article
- Integrating pharmacogenomics and cheminformatics with diverse disease phenotypes for cell type-guided drug discovery.Genome medicine · 2025Article
- Translational Informatics Driven Drug Repositioning for Neurodegenerative Disease.Current neuropharmacology · 2025Review
- Comparative analysis between 2D and 3D colorectal cancer culture models for insights into cellular morphological and transcriptomic variations.Scientific reports · 2023Article
- Article
- Multi-Omics Approaches in Colorectal Cancer Screening and Diagnosis, Recent Updates and Future Perspectives.Cancers · 2022Review
- From drug repositioning to target repositioning: prediction of therapeutic targets using genetically perturbed transcriptomic signatures.Bioinformatics (Oxford, England) · 2022Article
- Simple, fast, and flexible framework for matrix completion with infinite width neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing.Patterns (New York, N.Y.) · 2022Article
- RAID: Regression Analysis-Based Inductive DNA Microarray for Precise Read-Across.Frontiers in pharmacology · 2022Article
- Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing.Nature machine intelligence · 2021Article
- Current Achievements and Applications of Transcriptomics in Personalized Cancer Medicine.International journal of molecular sciences · 2021Review
- A deep learning framework for high-throughput mechanism-driven phenotype compound screening.bioRxiv : the preprint server for biology · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationGenome-wide identification of the transcriptomic responses of human cell lines to drug treatments is a challenging issue in medical and pharmaceutical research. However, drug-induced gene expression profiles are largely unknown and unobserved for all combinations of drugs and human cell lines, which is a serious obstacle in practical applications.
resultsHere, we developed a novel computational method to predict unknown parts of drug-induced gene expression profiles for various human cell lines and predict new drug therapeutic indications for a wide range of diseases. We proposed a tensor-train weighted optimization (TT-WOPT) algorithm to predict the potential values for unknown parts in tensor-structured gene expression data. Our results revealed that the proposed TT-WOPT algorithm can accurately reconstruct drug-induced gene expression data for a range of human cell lines in the Library of Integrated Network-based Cellular Signatures. The results also revealed that in comparison with the use of original gene expression profiles, the use of imputed gene expression profiles improved the accuracy of drug repositioning. We also performed a comprehensive prediction of drug indications for diseases with gene expression profiles, which suggested many potential drug indications that were not predicted by previous approaches. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.