TY  - JOUR
A1  - Qoku, Arber
A1  - Katsaouni, Nikoletta
A1  - Flinner, Nadine
A1  - Büttner, Florian
A1  - Schulz, Marcel Holger
T1  - Multimodal analysis methods in predictive biomedicine
T2  - Computational and structural biotechnology journal
N2  - 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.
KW  - Multimodal Modeling
KW  - Predictive Modeling
KW  - Multi-Omics
KW  - Machine Learning
KW  - Personalized Medicine
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/79696
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-796969
SN  - 2001-0370
VL  - 2023
IS  - In Press, Journal Pre-proof
ER  -