PXD019591 is an
original dataset announced via ProteomeXchange.
Dataset Summary
Title | Machine learning from large-scale proteomics accurately ranks anti-cancer drugs based on efficacy |
Description | We present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of > 400 drugs based on their effectiveness in decreasing cancer cell proliferation. We trained and validated DRUML using in-house proteomics and phosphoproteomics data from a panel of 26 AML, 10 esophageal and 12 hepatocellular carcinoma cell lines in triplicate (three independent cultures per cell line) by LC-MS/MS |
HostingRepository | PRIDE |
AnnounceDate | 2021-02-22 |
AnnouncementXML | Submission_2021-02-22_06:22:16.329.xml |
DigitalObjectIdentifier | https://dx.doi.org/10.6019/PXD019591 |
ReviewLevel | Peer-reviewed dataset |
DatasetOrigin | Original dataset |
RepositorySupport | Supported dataset by repository |
PrimarySubmitter | Maruan Hijazi |
SpeciesList | scientific name: Homo sapiens (Human); NCBI TaxID: 9606; |
ModificationList | Phospho; Oxidation; Carbamidomethyl; Gln->pyro-Glu |
Instrument | Q Exactive Plus |
Dataset History
Revision | Datetime | Status | ChangeLog Entry |
0 | 2020-06-04 07:17:54 | ID requested | |
⏵ 1 | 2021-02-22 06:22:16 | announced | |
2 | 2021-04-22 22:40:00 | announced | 2021-04-23: Updated publication reference for PubMed record(s): 33767176. |
Publication List
Dataset with its publication pending |
Keyword List
submitter keyword: machine learning, phosphoproteomics, proteomics, drug efficacy prediction, DRUML, drug ranking, cancer |
Contact List
Pedro Cutillas |
contact affiliation | Barts Cancer Institute. Queen Mary University of London |
contact email | p.cutillas@qmul.ac.uk |
lab head | |
Maruan Hijazi |
contact affiliation | BARTS CANCER INSTITUTE. QUEEN MARY UNIVERSITY OF LONDON |
contact email | m.hijazivega@qmul.ac.uk |
dataset submitter | |
Full Dataset Link List
Dataset FTP location
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PRIDE project URI |
Repository Record List
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- PRIDE
- PXD019591
- Label: PRIDE project
- Name: Machine learning from large-scale proteomics accurately ranks anti-cancer drugs based on efficacy