A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling

Supreeta Vijayakumar, Giuseppe Magazzu, Pradip Moon, Annalisa Occhipinti, Claudio Angione

Research output: Chapter in Book/Report/Conference proceedingChapter

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Abstract

t Complex, distributed and dynamic sets of clinical biomedical data are
collectively referred to as multimodal clinical data. In order to accommodate the
volume and heterogeneity of such diverse data types and aid in their interpretation
when they are combined with a multi-scale predictive model, machine learning is
a useful tool that can be wielded to deconstruct biological complexity and extract
relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one
of the main frameworks striving to bridge the gap between genotype and phenotype
by incorporating prior biological knowledge into mechanistic models. Consequently,
the utilization of GSMMs as a foundation for the integration of multi-omic data
originating from different domains is a valuable pursuit towards refining predictions. In this chapter, we show how cancer multi-omic data can be analyzed via multimodal
machine learning and metabolic modeling. Firstly, we focus on the merits of adopting
an integrative systems-biology led approach to biomedical data mining. Following
this, we propose how constraint-based metabolic models can provide a stable yet
adaptable foundation for the integration of multimodal data with machine learning.
Finally, we provide a step-by-step tutorial for the combination of machine learning
and GSMMs, which includes: (i) tissue-specific constraint-based modeling; (ii)
survival analysis using time-to-event prediction for cancer; and (iii) classification and
regression approaches for multimodal machine learning
Original languageEnglish
Title of host publicationComputational Systems Biology in Medicine and Biotechnology
EditorsSonia Cortassa, Miguel A. Aon
PublisherSpringer
Pages87-122
Volume2399
ISBN (Electronic)9781071618318
DOIs
Publication statusPublished - 24 May 2022

Bibliographical note

Funding Information:
We would like to acknowledge the support from UKRI Research England’s THYME project, from the Children’s Liver Disease Foundation, and from Earlier.org.

Publisher Copyright:
© 2022, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.

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