Multimodal analysis methods in predictive biomedicine.

Autor:	Qoku A; German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, a partnership between DKFZ and UCT Frankfurt-Marburg, Germany, Frankfurt am Main, Germany.; German Cancer Research Center (DKFZ), Heidelberg, Germany.; Goethe University Frankfurt, Germany., Katsaouni N; Goethe University Frankfurt, Institute for Cardiovascular Regeneration, Germany.; Goethe University Frankfurt, Cardio-Pulmonary Institute, Germany.; German Centre for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany., Flinner N; Goethe University Frankfurt, Dr. Senckenberg Institute of Pathology, University Hospital, Frankfurt am Main, Germany.; Frankfurt Institute for Advanced Studies (FIAS), Frankfurt am Main, Germany.; Frankfurt Cancer Institute (FCI), Germany.; University Cancer Center (UCT) Frankfurt-Marburg, Frankfurt am Main, Germany., Buettner F; German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, a partnership between DKFZ and UCT Frankfurt-Marburg, Germany, Frankfurt am Main, Germany.; German Cancer Research Center (DKFZ), Heidelberg, Germany.; Frankfurt Cancer Institute (FCI), Germany.; Goethe University Frankfurt, Germany., Schulz MH; Goethe University Frankfurt, Institute for Cardiovascular Regeneration, Germany.; Goethe University Frankfurt, Cardio-Pulmonary Institute, Germany.; German Centre for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany.
Jazyk:	angličtina
Zdroj:	Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2023 Nov 20; Vol. 21, pp. 5829-5838. Date of Electronic Publication: 2023 Nov 20 (Print Publication: 2023).
DOI:	10.1016/j.csbj.2023.11.011
Abstrakt:	For medicine to fulfill its promise of personalized treatments based on a better understanding of disease biology, computational and statistical tools must exist to analyze the increasing amount of patient data that becomes available. A particular challenge is that several types of data are being measured to cope with the complexity of the underlying systems, enhance predictive modeling and enrich molecular understanding. Here we review a number of recent approaches that specialize in the analysis of multimodal data in the context of predictive biomedicine. We focus on methods that combine different OMIC measurements with image or genome variation data. Our overview shows the diversity of methods that address analysis challenges and reveals new avenues for novel developments. Competing Interests: Florian Buettner is employed by Siemens AG. He reports funding from 10.13039/100009945Merck KGaA and renumeration from Albireo. Arber Qoku, Nicoletta Katsaouni, Dr Nadine Flinner, and Prof. Dr Marcel H. Schulz do not report any conflicts of interest. (© 2023 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu