Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Systematic identification of combinatorial drivers and targets in cancer cell lines.

PloS one - 1 Jan 2013

Tabchy Adel, Eltonsy Nevine, Housman David E, Mills Gordon B

Abstract excerpt

There is an urgent need to elicit and validate highly efficacious targets for combinatorial intervention from large scale ongoing molecular characterization efforts of tumors. We established an in silico bioinformatic platform in concert with a high throughput screening platform evaluating 37 novel targeted agents in 669 extensively characterized cancer cell lines reflecting the genomic and tissue-type diversity...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Biomarkers, Tumor
Share to Cell Lineage
Share to Cluster Analysis

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Computational Biology

Share to Drug Resistance, Neoplasm

Share to Drug Screening Assays, Antitumor

Share to Genomics

Share to Molecular Targeted Therapy

Source

Open in PubMed Open full text

Identifiers

PMID: 23577104
DOI: 10.1371/journal.pone.0060339
PMCID: PMC3618473

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Systematic identification of combinatorial drivers and targets in cancer cell lines.PMID 23577104Read ↗

Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence PatternsSemantic proximity Read ↗

Cancer Treatment in the Genomic EraSemantic proximity Read ↗

A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization.Semantic proximity Read ↗

Cell line modeling for systems medicine in cancers (Review)Semantic proximity Read ↗

Data Mining Approaches for Genomic Biomarker Development: Applications Using Drug Screening Data from the Cancer Genome Project and the Cancer Cell Line Encyclopedia.Semantic proximity Read ↗

Special report: multiple molecular testing of cancers to identify targeted therapies.Semantic proximity Read ↗

Perturbational Gene-Expression Signatures for Combinatorial Drug DiscoverySemantic proximity Read ↗

Pan-cancer and multiomics: advanced strategies for diagnosis, prognosis, and therapy in the complex genetic and molecular universe of cancerSemantic proximity Read ↗

The evolving role of cancer cell line-based screens to define the impact of cancer genomes on drug responseSemantic proximity Read ↗

A statistical framework for defining synergistic anticancer drug interactionsSemantic proximity Read ↗

Integrative omics analyses broaden treatment targets in human cancer.Semantic proximity Read ↗

Exploiting genetic complexity in cancer to improve therapeutic strategies.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.