ArticleCell chemical biology2019
Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets.
Article in Cell chemical biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 44 citations in OpenAlex.
- The translational chasm in machine learning for triple-negative breast cancer: a quantitative landscape assessment.Frontiers in oncology · 2026Pooled it
- Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor.Molecular systems biology · 2021Pooled it
- AGC kinase homology requires and enables co-targeting for CNS regeneration.bioRxiv : the preprint server for biology · 2026Article
- Recent advances in targeted therapies and immunotherapies for triple-negative breast cancer.Frontiers in oncology · 2026Review
- Extracellular vesicles in triple-negative breast cancer: current updates, challenges and future prospects.Frontiers in molecular biosciences · 2025Review
- Advancements in triple-negative breast cancer sub-typing, diagnosis and treatment with assistance of artificial intelligence : a focused review.Journal of cancer research and clinical oncology · 2024Review
- Therapies targeting triple-negative breast cancer: a perspective on anti-FGFR.Frontiers in oncology · 2024Review
- Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine.Briefings in bioinformatics · 2023Review
- Systems Biology in Cancer Diagnosis Integrating Omics Technologies and Artificial Intelligence to Support Physician Decision Making.Journal of personalized medicine · 2023Review
- Chemogenomic library design strategies for precision oncology, applied to phenotypic profiling of glioblastoma patient cells.iScience · 2023Article
- Phenotypic drug discovery: recent successes, lessons learned and new directions.Nature reviews. Drug discovery · 2022Review
- Compounds co-targeting kinases in axon regulatory pathways promote regeneration and behavioral recovery after spinal cord injury in mice.Experimental neurology · 2022Article
- Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease.Journal of clinical medicine · 2022Review
- Phenotypic Screening Following Transcriptomic Deconvolution to Identify Transcription Factors Mediating Axon Growth Induced by a Kinase Inhibitor.SLAS discovery : advancing life sciences R & D · 2021Article
- Review
- The Kinase Chemogenomic Set (KCGS): An Open Science Resource for Kinase Vulnerability Identification.International journal of molecular sciences · 2021Article
- Machine and deep learning approaches for cancer drug repurposing.Seminars in cancer biology · 2021Review
- Turning liabilities into opportunities: Off-target based drug repurposing in cancer.Seminars in cancer biology · 2021Review
- A review on machine learning approaches and trends in drug discovery.Computational and structural biotechnology journal · 2021Review
- Inhibition of GCK-IV kinases dissociates cell death and axon regeneration in CNS neurons.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
The lack of functional understanding of most mutations in cancer, combined with the non-druggability of most proteins, challenge genomics-based identification of oncology drug targets. We implemented a machine-learning-based approach (idTRAX), which relates cell-based screening of small-molecule compounds to their kinase inhibition data, to directly identify effective and readily druggable targets. We applied idTRAX to triple-negative breast cancer cell lines and efficiently identified cancer-selective targets. For example, we found that inhibiting AKT selectively kills MFM-223 and CAL148 cells, while inhibiting FGFR2 only kills MFM-223. Since the effects of catalytically inhibiting a protein can diverge from those of reducing its levels, targets identified by idTRAX frequently differ from those identified through gene knockout/knockdown methods. This is critical if the purpose is to identify targets specifically for small-molecule drug development, whereby idTRAX may produce fewer false-positives. The rapid nature of the approach suggests that it may be applicable in personalizing therapy.
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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.