ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2018
Cell-specific prediction and application of drug-induced gene expression profiles.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2018. 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.
- Decoding GPCR signaling in living cells to advance early therapeutic discovery for neurological disorders.Frontiers in molecular neuroscience · 2026Review
- Drug Search and Design Considering Cell Specificity of Chemically Induced Gene Expression Profiles for Disease-Associated Tissues.Molecular informatics · 2025Article
- AnchorDrug: A system for drug-induced gene expression prediction in new contexts through active learning.Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining · 2025Article
- Transfer Learning with Kernel Methods.Nature communications · 2023Article
- Identification of candidate biomarkers and mechanisms in foam cell formation from heterogeneous cellular origins via integrated transcriptome analysis.Annals of translational medicine · 2023Article
- Article
- 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
- Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq data.Genome medicine · 2021Article
- Reconciling multiple connectivity scores for drug repurposing.Briefings in bioinformatics · 2021Article
- DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment.PLoS computational biology · 2021Article
- A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing.Nature machine intelligence · 2021Article
- Using common genetic variants to find drugs for common epilepsies.Brain communications · 2021Article
- Cross-modal representation alignment of molecular structure and perturbation-induced transcriptional profiles.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2021Article
- A deep learning framework for high-throughput mechanism-driven phenotype compound screening.bioRxiv : the preprint server for biology · 2020Article
- Genetic Signatures of Acute Asthma Exacerbation Related With Ineffective Response to Corticosteroid.Allergy, asthma & immunology research · 2020Article
- Universal Nature of Drug Treatment Responses in Drug-Tissue-Wide Model-Animal Experiments Using Tensor Decomposition-Based Unsupervised Feature Extraction.Frontiers in genetics · 2020Article
- Signatures of cell death and proliferation in perturbation transcriptomics data-from confounding factor to effective prediction.Nucleic acids research · 2019Article
- MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease.Cell systems · 2019Article
- Deep Learning Benchmarks on L1000 Gene Expression Data.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.
Indexed as
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29218867PMC5753597What OpenQuestion holds
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.