ArticleBMC systems biology2017
A link prediction approach to cancer drug sensitivity prediction.
Article in BMC systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 62 citations in OpenAlex.
- ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction.PLoS computational biology · 2025Article
- A Survey on Hyperlink Prediction.IEEE transactions on neural networks and learning systems · 2024Article
- NeuPD-A Neural Network-Based Approach to Predict Antineoplastic Drug Response.Diagnostics (Basel, Switzerland) · 2023Article
- Early Pharmacological Profiling of Antiproliferative Compounds by Live Cell Imaging.Molecules (Basel, Switzerland) · 2022Article
- Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer.Journal of personalized medicine · 2022Article
- Extracting functional insights from loss-of-function screens using deep link prediction.Cell reports methods · 2022Article
- In Silico Inference of Synthetic Cytotoxic Interactions from Paclitaxel Responses.International journal of molecular sciences · 2021Article
- Applied Research on the Combination of Weighted Network and Supervised Learning in Acupoints Compatibility.Journal of healthcare engineering · 2021Article
- Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles.Frontiers in oncology · 2021Article
- Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments.BMC medical genomics · 2020Article
- Gene regulatory network analysis with drug sensitivity reveals synergistic effects of combinatory chemotherapy in gastric cancer.Scientific reports · 2020Article
- Ensembled machine learning framework for drug sensitivity prediction.IET systems biology · 2020Article
- Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology.International journal of molecular sciences · 2020Article
- DLS: A Link Prediction Method Based on Network Local Structure for Predicting Drug-Protein Interactions.Frontiers in bioengineering and biotechnology · 2020Article
- Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization.Molecular therapy. Nucleic acids · 2019Article
- Prediction of drug synergy score using ensemble based differential evolution.IET systems biology · 2019Article
- PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- The International Conference on Intelligent Biology and Medicine (ICIBM) 2016: putting systems biology to work.BMC systems biology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine.
resultsIn this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant.
conclusionsWe propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.
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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.