The Systems Biomedicine team devises computational strategies to transform the deluge of multimodal biomedical data into knowledge for genetic diseases.

The advances in high-throughput technologies are providing unprecedented opportunities to better understand human diseases. Recent years have in this context witnessed the accumulation of omics approaches and datasets. Novel technologies, such as single-cell or spatial omics, are constantly arising. Biomedicine is further transitioning from multiomics to multimodal datasets: data are not only available at the molecular omics level, as we now have access to signals and images, but also to various datasets related to disease phenotypes, health databases, or drug chemical similarities. The bottleneck now lies in the analysis and integration of these complex, large-scale and heterogeneous datasets. The Systems Biomedicine team bridges the gaps by harnessing digital expertise and developing novel computational approaches.

The Systems Biomedicine team is hosting the research group of Paul Villoutreix, laureate of an INSERM Chaire de Professeur Junior.

The Systems Biomedicine team works in close collaboration with the MABIOS team from the Marseille Mathematics Institute.


  • Systems Biomedicine / Baudot

Systems Biology, which consider biological systems as networks of interactions between biological components, offer a new approach to link the genotypes (e.g., the genes and the proteins, as the components of the systems) to the phenotypes (the merging properties of the systems), and in particular the diseased phenotypes. Our group is active in the fields of systems biology with both the development of mathematical and computational tools grounded on network theory and their application to biological questions, with the ultimate goal of untangling the genotype-phenotype relationships of human disorders. More info ...


  • Learning meaningful representations of Life / Villoutreix

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The Systems Biomedicine team is involved in various local, national and international projects. For instance, in Aix*Marseille University, we are active in the Marmara, Laënnec and Centuri institutes. At the national level, we are involved in the PEPR Santé Numérique, in particular in the M4DI and ai4scmed projects. At the international level, we are involved in the EJP-RD.

Team preprints

Lambert, J.  et al. 2023

Tracking clusters of patients over time enables extracting information from medico-administrative databases

Objective: We propose here cluster-tracking approaches to identify clusters of patients from truncated longitudinal data contained in medico-administrative databases. Material and Methods: We first...
Journal of Biomedical Informatics - issue: - volume: 139 - pages: 104309.

Ozisik, O.  et al. 2022

orsum: a Python package for filtering and comparing enrichment analyses using a simple principle

Background:  Enrichment analyses are widely applied to investigate lists of genes of interest. However, such analyses often result in long lists of annotation terms with high redundancy, making the...
BMC Bioinformatics - issue: 1 - volume: 23 - pages: 293.

Baptista, A.  et al. 2022

Universal multilayer network exploration by random walk with restart

The amount and variety of data have been increasing drastically for several years. These data are often represented as networks and explored with approaches arising from network theory. Recent years...
Communications Physics - issue: 1 - volume: 5 - pages: 170.

Cantini, L.  et al. 2021

Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer

High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated.
Nature Comm - issue: 1 - volume: 12 - pages: .

Pio-Lopez, L.  et al. 2021

MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

Abstract Network embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks...
Sci Rep - issue: 1 - volume: 11 - pages: 8794.

Novoa-del-Toro, EM.  et al. 2021

A multi-objective genetic algorithm to find active modules in multiplex biological networks

The identification of subnetworks of interest—or active modules—by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular...
PLoS Comput Biol - issue: 8 - volume: 17 - pages: e1009263.

Sánchez-Valle, J.  et al. 2020

Interpreting molecular similarity between patients as a determinant of disease comorbidity relationships

Comorbidity is a medical condition attracting increasing attention in healthcare and biomedical research. Little is known about the involvement of potential molecular factors leading to the emergence...
Nature Comm - issue: 1 - volume: 11 - pages: 2854.

Katsogiannou, M.  et al. 2019

Integrative proteomic and phosphoproteomic profiling of prostate cell lines

Background Prostate cancer is a major public health issue, mainly because patients relapse after androgen deprivation therapy. Proteomic strategies, aiming to reflect the functional activity of cells,...
PLoS ONE - issue: 11 - volume: 14 - pages: 25.

The DREAM Module Identification Challenge Consortium, .  et al. 2019

Assessment of network module identification across complex diseases

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms...
Nature Methods - issue: 9 - volume: 16 - pages: 843-852.

Valdeolivas, A.  et al. 2018

Random Walk with Restart on Multiplex and Heterogeneous Biological Networks

Motivation: Recentyears have witnessed anexponentialgrowthin thenumberof identified interactions between biological molecules. These interactions are usually represented as large and complex networks,...
Bioinformatics - issue: - volume: - pages: .

Didier, G.  et al. 2018

Identifying communities from multiplex biological networks by randomized optimization of modularity

The identification of communities, or modules, is a common operation in the analysis of large biological networks. The Disease Module Identification DREAM challenge established a framework to evaluate...
F1000Research - issue: - volume: 7 - pages: 1042.

Sánchez-Valle, J.  et al. 2017

A molecular hypothesis to explain direct and inverse co-morbidities between Alzheimer's Disease, Glioblastoma and Lung cancer

Epidemiological studies indicate that patients suffering from Alzheimer's disease have a lower risk of developing lung cancer, and suggest a higher risk of developing glioblastoma. Here we explore the...
Sci Rep - issue: 1 - volume: 7 - pages: 4474.

Tabarés-Seisdedos, R.  et al. 2016

Editorial: Direct and Inverse Comorbidities Between Complex Disorders

Front Physiol - issue: - volume: 7 - pages: 117.

Didier, G.  et al. 2015

Identifying communities from multiplex biological networks

Various biological networks can be constructed, each featuring gene/protein relationships of different meanings (e.g., protein interactions or gene co-expression). However, this diversity is...
- issue: - volume: 3 - pages: e1525.

Flobak, A.  et al. 2015

Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling

Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug...
PLoS Comput. Biol. - issue: 8 - volume: 11 - pages: e1004426.

Tripathi, S.  et al. 2015

The gastrin and cholecystokinin receptors mediated signaling network: a scaffold for data analysis and new hypotheses on regulatory mechanisms

BACKGROUND: The gastrointestinal peptide hormones cholecystokinin and gastrin exert their biological functions via cholecystokinin receptors CCK1R and CCK2R respectively. Gastrin, a central regulator...
BMC Syst Biol - issue: - volume: 9 - pages: 40.

Ibáñez, K.  et al. 2014

Molecular evidence for the inverse comorbidity between central nervous system disorders and cancers detected by transcriptomic meta-analyses

There is epidemiological evidence that patients with certain Central Nervous System (CNS) disorders have a lower than expected probability of developing some types of Cancer. We tested here the...
PLoS Genet. - issue: 2 - volume: 10 - pages: e1004173.