Journal Article

DKFZ-2026-00872

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A pipeline of machine learning-driven multi-modal data fusion methods for prognostic risk analysis in bevacizumab-treated metastatic colorectal cancer.

Thomas, V. ; Nyamundanda, G. ; Lärkeryd, A. ; Hari, P. S. ; Miller, I. S. ; Venken, T. ; Smeets, D. ; Boeckx, B. ; Betge, J.DKFZ* ; Ebert, M. P. A. ; Gaiser, T. ; Murphy, V. ; Kay, E. ; Verheul, H. M. ; O'Farrell, A. C. ; Cremolini, C. ; Marmorino, F. ; Gallagher, W. M. ; Barat, A. ; Klinger, R. ; Fender, B. ; Ylstra, B. ; van Grieken, N. ; McNamara, D. A. ; Hennessy, B. T. ; Das, S. ; Moran, B. ; O'Connor, D. P. ; Dienstmann, R. ; Lambrechts, D. ; Prehn, J. H. M. ; Sadanandam, A. ; Byrne, A. T.

2026
Springer Nature [London]

Keyword(s): Biomarkers ; Machine learning ; Metastatic colorectal cancer ; Multi-modal data fusion ; Personalised medicine ; PhenMap ; Prognostic risk analysis

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Contributing Institute(s):

1. NWG-KKE Translationale Gastrointestinale Onkologie und präklinische Modelle (B440)

Research Program(s):

1. 312 - Funktionelle und strukturelle Genomforschung (POF4-312) (POF4-312)

Appears in the scientific report 2026

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; ; Article Processing Charges ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; Fees ; IF < 5 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection ; Zoological Record

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