ArticlePloS one2016
Integrating Domain Specific Knowledge and Network Analysis to Predict Drug Sensitivity of Cancer Cell Lines.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- DNA Repair Genes as Drug Candidates for Early Breast Cancer Onset in Latin America: A Systematic Review.International journal of molecular sciences · 2021Pooled it
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- The involvement of homeobox-C 4 in predicting prognosis and unraveling immune landscape across multiple cancersFrontiers in genetics · 2022Article
- Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles.Biomedicines · 2021Article
- Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization.Molecular therapy. Nucleic acids · 2019Article
- Paclitaxel Response Can Be Predicted With Interpretable Multi-Variate Classifiers Exploiting DNA-Methylation and miRNA Data.Frontiers in genetics · 2019Article
- Anti-cancer Drug Response Prediction Using Neighbor-Based Collaborative Filtering with Global Effect Removal.Molecular therapy. Nucleic acids · 2018Article
- A Hybrid Interpolation Weighted Collaborative Filtering Method for Anti-cancer Drug Response Prediction.Frontiers in pharmacology · 2018Article
- Precision and recall oncology: combining multiple gene mutations for improved identification of drug-sensitive tumours.Oncotarget · 2017Article
- Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data.F1000Research · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
One of fundamental challenges in cancer studies is that varying molecular characteristics of different tumor types may lead to resistance to certain drugs. As a result, the same drug can lead to significantly different results in different types of cancer thus emphasizing the need for individualized medicine. Individual prediction of drug response has great potential to aid in improving the clinical outcome and reduce the financial costs associated with prescribing chemotherapy drugs to which the patient's tumor might be resistant. In this paper we develop a network based classifier (NBC) method for predicting sensitivity of cell lines to anticancer drugs from transcriptome data. In the literature, this strategy has been used for predicting cancer types. Here, we extend it to estimate sensitivity of cells from different tumor types to various anticancer drugs. Furthermore, we incorporate domain specific knowledge such as the use of apoptotic gene list and clinical dose information in our method to impart biological significance to the prediction. Our experimental results suggest that our network based classifier (NBC) method outperforms existing classifiers in estimating sensitivity of cell lines for different drugs.
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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.