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Principal investigator :
Jean-Daniel Zucker, Ph.D.; Edi Prifti, Ph.D.

Integromics is the Bioinformatics department of ICAN focused on integrating patient’s clinical, environmental and OMICS data to support the ICAN institute’s translational research mission. A strong emphasis is placed on the analysis of large-scale biological datasets generated through the use of high-throughput technologies, including microarrays, lipidomics, metabolomics, next-generation sequencing (genomics, transcriptomics, methylomics, metagenomics) and radiomics. The department is also devoted to developing innovative algorithms that address key challenges in the field of omics data processing, visualization, analyses and integration. Integromics contributes to capitalizing and freely distributing these methods and software to ICAN members and worldwide researchers through its IO::Portal. Created in September 2015, Integromics gathers together bioinformaticians, computational biologists, mathematicians and biologists to rationalize the bioinformatics initiatives in ICAN and face up with the massive amount of data to be integrated and to support the institutes precision medicine mission. Integromics research activities are structured around the five following axes all applied to the field of cardiometabolic diseases and related complications:


This axis aims at building a data warehouse to support the storage, security, and curation of existing and future data, which will be integrated into different pipelines. Integromics has already started an important work in standardizing project metadata and we will create a general and integrated database scheme. Another objective is to build an integrated tool for automated querying, searching and interpretation of all the bioclinical data including omics (raw, preprocessed and post-processed). This biomedical data warehouse will be integrated with the ICAN Portal.


Build high-performance computing capabilities that rely both on the founder’s available resources (interfaced with hospital and UPMC IT systems) and ICAN’s internal dedicated resources. Another objective is to build a portal and platform to enable access for ICAN partners. This will also serve as a foundation for pipelines, tools and methods using a modular architecture for increased efficiency and valorization. In the big data context, specifically for omics data, it is also important to adapt the archiving capacity and network for optimal data transfer, storage and labeling/indexing.


Integromics is developing high performance and distributed pipelines (based on Spark and GPU) to rationalize ICAN’s bioinformatics data processing and the statistical analyses for the different types of omics data that are produced by ICAN platforms as well as external ones. Particular effort is put into multi-type integrative approaches based on network reconstruction and deep learning.


Integromics is building sustainable innovative methods for precision medicine using different omics data and based on Artificial Intelligence and Statistical methods. Some of these approaches support predictive analytics based on deep learning and statistical learning to automate prediction tasks, integrated score (combining different data) linked to clinician and researcher needs. Others are focused on inference of large heterogeneous causality networks to enable visualization and inference of integrated data. Finally, systems biology modeling is also used to help identifying targets that could then be validated by ICAN teams and partners.


One of the main objectives of Integromics is to provide ICAN clinicians with dedicated computational tools to support diagnosis and provide patients with additional tools supporting community networking and treatment follow-up. In cardiometabolic clinical practice, the primary goal is to optimize patient outcomes through personalized predictions, risk scores, earlier and better-quality treatment, and improved decision support for clinicians in cyclical processes.



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INTEGROMICS is the Bioinformatics Department of ICAN institute focused on integrating OMICS, Clinical and environmental data about patients to support the institute’s translational research mission.


Prof. Zucker’s research focus is AI, Machine Learning, Multi-Scale Agent-based modelling of Complex Systems from Omics data integration to Environmental Decision system. Prof. Zucker is a Former Engineer (Sup’Aéro,1985), and received his Ph.D. in Machine Learning from Paris 6 University in 1996, where he became an associate professor the same year. In 2002 Dr Zucker became a full professor at Paris 13 University. In 2008 he also became a Senior Researcher at IRD. Prof. Zucker is the director of the UMMISCO Lab. on Math. and Computational Modeling of Complex Systems