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Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial.

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Title: Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial.
Authors: Rakaee, M.1,2,3 (AUTHOR) mehrdad.rakaee@uit.no, Andersen, S.3,4 (AUTHOR), Giannikou, K.1,5 (AUTHOR), Paulsen, E.-E.3,6 (AUTHOR), Kilvaer, T.K.3,4 (AUTHOR), Busund, L.-T.R.2,7 (AUTHOR), Berg, T.2,7 (AUTHOR), Richardsen, E.2,7 (AUTHOR), Lombardi, A.P.7 (AUTHOR), Adib, E.1,8 (AUTHOR), Pedersen, M.I.3 (AUTHOR), Tafavvoghi, M.9 (AUTHOR), Wahl, S.G.F.10,11 (AUTHOR), Petersen, R.H.12,13 (AUTHOR), Bondgaard, A.L.14 (AUTHOR), Yde, C.W.15 (AUTHOR), Baudet, C.15 (AUTHOR), Licht, P.16 (AUTHOR), Lund-Iversen, M.17 (AUTHOR), Grønberg, B.H.10,11 (AUTHOR)
Source: Annals of Oncology. Jul2023, Vol. 34 Issue 7, p578-588. 11p.
Database: Academic Search Ultimate
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An: 164417892
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PubType: Academic Journal
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  Data: Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial.
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  Data: <searchLink fieldCode="JN" term="%22Annals+of+Oncology%22">Annals of Oncology</searchLink>. Jul2023, Vol. 34 Issue 7, p578-588. 11p.
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              Text: Jul2023
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