Multimodal analysis methods in predictive biomedicine

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.

Metadaten
Author:	Arber Qoku ORCiD, Nikoletta Katsaouni ORCiD, Nadine Flinner ORCiD GND, Florian Büttner ORCiD, Marcel Holger Schulz ORCiD GND
URN:	urn:nbn:de:hebis:30:3-796969
DOI:	https://doi.org/10.1016/j.csbj.2023.11.011
ISSN:	2001-0370
Parent Title (English):	Computational and structural biotechnology journal
Document Type:	Article
Language:	English
Date of Publication (online):	2023/11/20
Date of first Publication:	2023/11/20
Publishing Institution:	Universitätsbibliothek Johann Christian Senckenberg
Release Date:	2023/11/22
Tag:	Machine Learning; Multi-Omics; Multimodal Modeling; Personalized Medicine; Predictive Modeling
Volume:	2023
Issue:	In Press, Journal Pre-proof
Page Number:	41
HeBIS-PPN:	516371495
Institutes:	Medizin
	Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS)
Dewey Decimal Classification:	0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
	6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:	Universitätspublikationen
Licence (German):	Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International

Open Access