ArticlePLoS computational biology2017
Systematic, network-based characterization of therapeutic target inhibitors.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed, 29 citations in OpenAlex.
- CANDiT: A machine learning framework for differentiation therapy in colorectal cancer.Cell reports. Medicine · 2025Article
- DeepTarget predicts anti-cancer mechanisms of action of small molecules by integrating drug and genetic screens.NPJ precision oncology · 2025Article
- NaRnEA: An Information Theoretic Framework for Gene Set Analysis.Entropy (Basel, Switzerland) · 2023Article
- Validation of a non-oncogene encoded vulnerability to exportin 1 inhibition in pediatric renal tumors.Med (New York, N.Y.) · 2022Article
- A model for network-based identification and pharmacological targeting of aberrant, replication-permissive transcriptional programs induced by viral infection.Communications biology · 2022Article
- Single-cell transcriptional pharmacodynamics of trifluridine in a tumor-immune model.Scientific reports · 2022Article
- A Model for Network-Based Identification and Pharmacological Targeting of Aberrant, Replication-Permissive Transcriptional Programs Induced by Viral Infection.Research square · 2022Article
- A community challenge for a pancancer drug mechanism of action inference from perturbational profile data.Cell reports. Medicine · 2022Article
- Artificial intelligence guided discovery of a barrier-protective therapy in inflammatory bowel disease.Nature communications · 2021Article
- Network-guided search for genetic heterogeneity between gene pairs.Bioinformatics (Oxford, England) · 2021Article
- Finding disease modules for cancer and COVID-19 in gene co-expression networks with the Core&Peel method.Scientific reports · 2020Article
- Precision Targeting with EZH2 and HDAC Inhibitors in Epigenetically Dysregulated Lymphomas.Clinical cancer research : an official journal of the American Association for Cancer Research · 2019Article
- Network approaches and applications in biology.PLoS computational 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
9 authors at 4 institutions in 1 country.
Funding
Abstract
A large fraction of the proteins that are being identified as key tumor dependencies represent poor pharmacological targets or lack clinically-relevant small-molecule inhibitors. Availability of fully generalizable approaches for the systematic and efficient prioritization of tumor-context specific protein activity inhibitors would thus have significant translational value. Unfortunately, inhibitor effects on protein activity cannot be directly measured in systematic and proteome-wide fashion by conventional biochemical assays. We introduce OncoLead, a novel network based approach for the systematic prioritization of candidate inhibitors for arbitrary targets of therapeutic interest. In vitro and in vivo validation confirmed that OncoLead analysis can recapitulate known inhibitors as well as prioritize novel, context-specific inhibitors of difficult targets, such as MYC and STAT3. We used OncoLead to generate the first unbiased drug/regulator interaction map, representing compounds modulating the activity of cancer-relevant transcription factors, with potential in precision medicine.
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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.