Towards Extraction of Multiscale Factors via Redundancy Removal from the Structure and Dynamics of Complex Networks

Venerdì 7 Marzo alle 15:30, online su MS Teams, si tiene il seminario di Luis M. Rocha (Thomas J. Watson College of Engineering and Applied Science, State University of New York at Binghamton) dal titolo "Towards Extraction of Multiscale Factors via Redundancy Removal from the Structure and Dynamics of Complex Networks".

Abstract

Due to the widespread digitization of biomedical and behavioral data, there has been a breakthrough in our ability to characterize often overlooked exposome factors in disease, such as social interactions, psychological states, and behavioral patterns such as medical treatments, drug use, drinking habits and diet. This is particularly important to study chronic health conditions which unfold as a complex interplay among biological, psychological, linguistic, and societal multiscale factors that change over time and which traditional organism models cannot capture. The recent availability of heterogeneous multiomics and unconventional data from electronic health records, social media, and digital cohorts, as well as computational and theoretical advances in characterizing multivariate, multilayer complex systems, raise the prospect of “digital twins” in precision medicine, whereby the behavior of a cell, sub-system, organ or a whole organism can be accurately simulated to predict disease and intervention outcomes.

Towards that goal, we summarize our multilayer network reduction methodology used to uncover multiscale factors in disease, though the methodology is general and can be applied beyond biomedicine. In particular, we show that using distance backbones and effective graphs to remove redundant edges or interactions from network models obtained from biochemical and social data, reveals optimal information transmission and regulatory pathways. This greatly facilitates explainable inference, which is essential in biomedical settings. Indeed, by removing large proportions of redundant associations and interactions, it is feasible to use the remaining ones to directly backtrack to empirical evidence, i.e. the data items used to characterize correlational strength or information about causal dynamics when available. Finally, we demonstrate that our network reduction approach naturally extends to multilayer networks. This is exemplified with recent studies of multi-organism male infertility from protein interaction networks and patient-centered integration and analysis of heterogeneous data sources in epilepsy, ranging from social media data to electronic health records. 

(07 marzo 2025)