ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2017
Integrative network and transcriptomics-based approach predicts genotype- specific drug combinations for melanoma.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2.BMC medical informatics and decision making · 2021Article
- Machine learning in the prediction of cancer therapy.Computational and structural biotechnology journal · 2021Review
- Integrative network analysis identifies potential targets and drugs for ovarian cancer.BMC medical genomics · 2020Article
- Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Tumor cell sensitivity to vemurafenib can be predicted from protein expression in a BRAF-V600E basket trial setting.BMC cancer · 2019Article
- Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes.NPJ systems biology and applications · 2019Article
- An integrative machine learning approach for prediction of toxicity-related drug safety.Life science alliance · 2018Article
- Core signaling pathways in ovarian cancer stem cell revealed by integrative analysis of multi-marker genomics data.PloS one · 2018Article
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Authors and funding
3 authors.
Funding
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Abstract
Computational methods for drug combination predictions are needed to identify effective therapies that improve durability and prevent drug resistance in an efficient manner. In this paper, we present SynGeNet, a computational method that integrates transcriptomics data characterizing disease and drug z-score profiles with network mining algorithms in order to predict synergistic drug combinations. We compare SynGeNet to other available transcriptomics-based tools to predict drug combinations validated across melanoma cell lines in three genotype groups: BRAF-mutant, NRAS-mutant and combined. We showed that SynGeNet outperforms other available tools in predicting validated drug combinations and single agents tested as part of additional drug pairs. Interestingly, we observed that the performance of SynGeNet decreased when the network construction step was removed and improved when the proportion of matched-genotype validation cell lines increased. These results suggest that delineating functional information from transcriptomics data via network mining and genomic features can improve drug combination predictions.
Identifiers
28815138PMC5543336What 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.