ReviewBritish journal of cancer2012
Genetic and proteomic approaches to identify cancer drug targets.
Review in British journal of cancer, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Comprehensive Pan-cancer Analysis and Experimental Verification of EGLN Family: Potential Biomarkers in Cervical Cancer.Current cancer drug targets · 2026Article
- Genome-wide CRISPR Screening to Identify Drivers of TGF-β-Induced Liver Fibrosis in Human Hepatic Stellate Cells.ACS chemical biology · 2022Article
- Strategies to Overcome Resistance Mechanisms in T-Cell Acute Lymphoblastic Leukemia.International journal of molecular sciences · 2019Review
- Therapeutic Targeting of Notch Signaling Pathway in Hematological Malignancies.Mediterranean journal of hematology and infectious diseases · 2019Review
- Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery.International journal of molecular sciences · 2018Review
- Natural Compounds as Anticancer Agents Targeting DNA Topoisomerases.Current genomics · 2017Review
- Complementary Approaches to Existing Target Based Drug Discovery for Identifying Novel Drug Targets.Biomedicines · 2016Review
- Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines.Nature biotechnology · 2016Article
- 7B7: a novel antibody directed against the Ku70/Ku80 heterodimer blocks invasion in pancreatic and lung cancer cells.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2014Article
- Venus Flytrap (Dionaea muscipula Solander ex Ellis) Contains Powerful Compounds that Prevent and Cure Cancer.Frontiers in oncology · 2013Article
- Use of big data in drug development for precision medicine.Expert review of precision medicine and drug developmentArticle
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.
Funding
Abstract
While target-based small-molecule discovery has taken centre-stage in the pharmaceutical industry, there are many cancer-promoting proteins not easily addressed with a traditional target-based screening approach. In order to address this problem, as well as to identify modulators of biological states in the absence of knowing the protein target of the state switch, alternative phenotypic screening approaches, such as gene expression-based and high-content imaging, have been developed. With this renewed interest in phenotypic screening, however, comes the challenge of identifying the binding protein target(s) of small-molecule hits. Emerging technologies have the potential to improve the process of target identification. In this review, we discuss the application of genomic (gene expression-based), genetic (short hairpin RNA and open reading frame screening), and proteomic approaches to protein target identification.
Indexed as
Identifiers
What 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.