ArticleFrontiers in genetics2020
DSPLMF: A Method for Cancer Drug Sensitivity Prediction Using a Novel Regularization Approach in Logistic Matrix Factorization.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Gene dependency-informed inference of response to targeted cancer therapies.Nature communications · 2026Article
- Translational Informatics Driven Drug Repositioning for Neurodegenerative Disease.Current neuropharmacology · 2025Review
- An Innovative Multi-Omics Model Integrating Latent Alignment and Attention Mechanism for Drug Response Prediction.Journal of personalized medicine · 2024Article
- Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery.Nature communications · 2024Article
- StringFix: an annotation-guided transcriptome assembler improves the recovery of amino acid sequences from RNA-Seq reads.Genes & genomics · 2023Article
- SeEn: Sequential enriched datasets for sequence-aware recommendations.Scientific data · 2022Article
- Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer's disease drug repurposing.PLoS computational biology · 2022Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model.BMC bioinformatics · 2021Article
- Drug-induced cell viability prediction from LINCS-L1000 through WRFEN-XGBoost algorithm.BMC bioinformatics · 2021Article
- Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors.Cancer informatics · 2021Article
- A computational method for drug sensitivity prediction of cancer cell lines based on various molecular information.PloS one · 2021Article
- Article
- ADRML: anticancer drug response prediction using manifold learning.Scientific reports · 2020Article
- DRIM: A Web-Based System for Investigating Drug Response at the Molecular Level by Condition-Specific Multi-Omics Data Integration.Frontiers in genetics · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ability to predict the drug response for cancer disease based on genomics information is an essential problem in modern oncology, leading to personalized treatment. By predicting accurate anticancer responses, oncologists achieve a complete understanding of the effective treatment for each patient. In this paper, we present DSPLMF (
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