Machine learning identifies a core gene set predictive of acquired resistance to EGFR tyrosine kinase inhibitor.

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Machine learning identifies a core gene set predictive of acquired resistance to EGFR tyrosine kinase inhibitor. is …
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P356DOI10.1007/S00432-018-2676-7
P698PubMed publication ID29802456

P50authorSung Young KimQ95965259
P2093author name stringYoung Rae Kim
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Mechanisms of resistance to EGFR tyrosine kinase inhibitorsQ36238664
Acquired resistance to EGFR tyrosine kinase inhibitors in cancer cells is mediated by loss of IGF-binding proteinsQ36726112
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Acquired Resistance to the Mutant-Selective EGFR Inhibitor AZD9291 Is Associated with Increased Dependence on RAS Signaling in Preclinical ModelsQ38887681
Axl mediates acquired resistance of head and neck cancer cells to the epidermal growth factor receptor inhibitor erlotinib.Q39097045
Evaluating the molecule-based prediction of clinical drug responses in cancerQ39646838
Novel mechanism of lapatinib resistance in HER2-positive breast tumor cells: activation of AXL.Q39813764
P433issue8
P921main subjectmachine learningQ2539
P304page(s)1435-1444
P577publication date2018-05-25
P1433published inJournal of Cancer Research and Clinical OncologyQ2081599
P1476titleMachine learning identifies a core gene set predictive of acquired resistance to EGFR tyrosine kinase inhibitor.
P478volume144

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Q92661860MOB (Mps one Binder) Proteins in the Hippo Pathway and Cancercites workP2860

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