Avviso Seminario del Prof. Luis M. Rocha

Venerdì 7 Marzo alle ore 15:30 il prof. Luis M. Rocha, Thomas J. Watson College of Engineering and Applied Science, State University of New York at Binghamton, terrà un seminario online dal titolo "Towards Extraction of Multiscale Factors via Redundancy Removal from the Structure and Dynamics of Complex Networks".

Il seminario, della durata di 1h, si svolgerà tramite piattaforma Teams ed è aperto a tutti gli interessati utilizzando il seguente link.

SPEAKER: Prof. Luis M. Rocha, George J. Klir Professor of Systems Science, School of Systems Science and Industrial Engineering, Thomas J. Watson College of Engineering and Applied Science State University of New York at Binghamton, and Visiting Professor and Principal Investigator Católica Biomedical Research Center Universidade Católica Portuguesa

TITLE: 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 [1].

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 [2] and effective graphs [3] 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 [4] and patient-centered integration and analysis of heterogeneous data sources in epilepsy, ranging from social media data to electronic health records [5,6].

[1] De Domenico et al [2024]. Challenges and opportunities for digital twins in precision medicine: a complex systems perspective. npj Digital Medicine 8, 37. DOI: 10.1038/s41746-024-01402-3.

[2] T. Simas, R.B. Correia, and L.M. Rocha [2021]. The distance backbone of complex networks. Journal of Complex Networks, 9(6):cnab021, 2021.

[3] A.J. Gates, R.B. Correia, X. Wang, and L.M. Rocha. [2021] The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling. PNAS, 118(12), 2021.

[4] R.B. Correia et al [2024]. The conserved genetic program of male germ cells uncovers ancient regulators of human spermatogenesis. eLife, 13:RP95774.

[5] R.B. Correia et al [2025]. myAURA: A Personalized health library for epilepsy management via knowledge graph sparsification and visualization. Journal of the American Medical Informatics Association, ocaf012. DOI: 10.1093/jamia/ocaf012.

[6] J. Sanchez-Valle et al [2024]. Prevalence and differences in the co-administration of drugs known to interact: an analysis of three distinct and large populations. BMC medicine, 22(1):166.