ICAN I/O – Data sciences

ICAN I/O – Data sciencesOur ICAN I/O platform – Data sciencesIn today’s scientific and medical fields, the generation of vast amounts of data, or “Big Data”, necessitates the use of mathematical modeling, statistics, and artificial intelligence. These tools are essential for efficiently managing and analyzing health data, revolutionizing approaches to biology.

• ICAN I/O is a platform of expertise focused on multi-omics data analysis and integration, biomarker discovery and clinical research. Our aim is to ensure secure data management throughout its lifecycle, in compliance with RGPD requirements.
• The platform goes hand in hand with the evolution of modern technologies and the use of robust algorithms to achieve research objectives through a collaborative and holistic approach integrating multi-omics technologies and statistical analysis.
• The team includes data managers and a data scientist who all have extensive experience gained from working on national and international projects, collaborating with eminent scientists, doctors and researchers on many scientific topics.
• With this level of expertise, IHU ICAN is recognized as a major international entity capable of generating significant healthcare data, driving advancements in the field.

View charterCurrent major projects

MAESTRIA : Machine Learning Artificial Intelligence Early Detection Stroke Atrial Fibrillation

• Consortium of 18 partners from Europe, the United States and Canada responding to an H2020 call for projects on digital diagnostics.
• Ultra-innovative project to better detect atrial cardiomyopathy, responsible for the occurrence of atrial fibrillation and vascular embolic accidents, with the use of machine learning and AI (Artificial Intelligence).
• The ICAN I/O Data Science team plays a central role in integrating multimodal data (imaging, clinical, omics) to improve diagnostic accuracy and identify new therapeutic targets, thereby increasing treatment efficacy and preventing complications.
• The ICAN I/O team is also leading the development of the MAESTRIA demonstrator, an innovative digital platform designed to guide atrial fibrillation risk assessment and management, improve diagnostics and support treatment decisions through a scalable, user-centric approach.

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ARTEMIs : AcceleRating the Translation of virtual twins towards a pErsonalised Management of fatty lIver patients

• The ARTEMIs project is developing “virtual twins” using computer models and machine learning, integrated into a Clinical Decision Support System (CDSS) for the management of metabolic fatty liver disease (MFLD).
• Targeting a disease that affects 25% of the population, ARTEMIs also focuses on early intervention for the cardiovascular complications associated with MAFLD.
• This system aims to personalize treatment by predicting disease progression, assessing treatment efficacy and encouraging a healthier lifestyle.
• The project concludes with a proof-of-concept study to evaluate the effectiveness of this integrated approach in a clinical setting.

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MEDITWIN: using digital twins to develop tomorrow’s personalized medicine

• Consortium comprising 7 University Hospital Institutes (IHUs), including IHU ICAN, Nantes University Hospital, Inria, associated startups and Dassault Systèmes.
• Project France 2030 announced on Monday December 11, 2023 in the presence of French President Emmanuel Macron.
• Using customized virtual twins of organs, metabolism and cancer tumors
• 5-year term, from 2024 to 2029
• Objectives: propose a diagnosis of cardiovascular disease risk for high-risk patients and help specialist physicians choose treatments for effective patient follow-up.

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RESIST-PP: the link between diet, innate immunity and risk of infection

• European Union-funded project to explore how high-fat diets induce postprandial inflammation and atherogenic dyslipidemia via metabolic endotoxemia influenced by gut microbiota.
• The project aims to study the impact of bacterial-derived metabolites on the susceptibility of obese and diabetic patients to infections, to analyze interactions between immune cells and blood microbiota, and to carry out studies on mouse models.
• The ICAN I/O platform contributes to this project by integrating multimodal data, improving our understanding of diet-induced infection risks, and developing new treatment and prevention strategies for metabolic and infectious diseases.

Find out moreDatabase management and curationCRF paper

• Protocol reading
• Paper CRF review
• Paper CRF Validation

eCRF creation

• List of variables
• Implementation in REDCap
• FRC test
• Validation of CRF
• Creation of user accounts
• Release

Edit Checks

• Creation and validation of the Data-Validation Plan
• Development / Test / Validation – Selection of required fields

Extraction

• Data extraction / Creation of datasets

Documentation

• REDCap User Guide
• Study entry guide
• Data-Management Plan of the study

External Data

• Link to external data

eCRF Maintenance and Data Quality Control

• Fixing problems
• New version / Amendment
• User Management
• Creation of queries

Monitoring

• Provision to the monitor of a fraction of data to be monitored

Multi-modal Integration

• The ICAN I/O platform specializes in multimodal integration and actively participates in numerous national and European projects focusing on omics studies, working closely with the ICAN Omics Lipidomics and ICAN Omics Metabolomics platforms.
• ICAN I/O uses a range of analyses from classical statistical analysis to advanced machine learning algorithms to achieve the study objectives.
• The ICAN I/O team is continually developing customized visualization tools to enhance the presentation and interpretation of results.
• The platform’s solid experience is backed up by a number of major scientific publications demonstrating its expertise.

OmicsClinical research

• The ICAN I/O platform leverages a comprehensive blend of statistical and machine learning techniques to excel in clinical research.
• It crafts Statistical Analysis Plans and performs analyses across Descriptive, Multivariable, and Longitudinal Time-series disciplines using various statistical models The platform’s expertise extends to risk prediction, discriminant analysis, model calibration and validation, as well as advanced classification methods such as the clustering and ROC analysis. It also manages propensity score matching, competitive risk analysis, survival analysis with Kaplan-Meier methods and multiple imputation followed by a sensitivity analysis.
• In machine learning, ICAN I/O employs Ensemble Methods, XG Boost, Random Forest, SVM, and PLS-DA, customized to fit specific study requirements and data types It focuses on technologies like Explainable AI, Causal Inference, and Deep Phenotyping, and utilizes Automated ML and Incremental Learning for enhanced data handling.
• The platform has developed web applications based on XG Boost for clinical use, discovered prognostic markers and biomarkers, and developed clinical scores, securing its leadership with two international biomarker patents.
• His innovative approach has been highlighted by multiple high-impact publications in journals such as The Lancet, European Heart Journal, Nature Cell Biology and others.

Scientific Committee

• Dr. Antonio GALLO, MD, Ph.D (APHP)
• Dr. Wilfried Le Goff, Ph.D (UMR 1166 ICAN)
• Prof. Matthieu Schmidt, MD, Ph.D (APHP)
• Dr. Xavier Fresquet, Ph.D (SCAI)

ContactMaharajah PONNAIAH, Ph.DResponsiblem.ponnaiah @ihuican.org
(33) 6 51 71 93 08View charterPublications12/03/2024Perturbation and resilience of the gut microbiome up to 3 months after β-lactams exposure in healthy volunteers suggest an important role of microbial β-lactamases

 13/02/2024Non-alcoholic fatty liver disease biomarkers estimate cardiovascular risk based on coronary artery calcium score in type 2 diabetes: a cross-sectional study with two independent cohorts01/02/2024Left atrial strain: A memory of the severity of atrial myocardial stress in atrial fibrillation

 01/11/2023Use of the SINBAD score as a predicting tool for major adverse foot events in patients with diabetic foot ulcer: A French multicentre study17/09/2023Impaired metabolism predicts coronary artery calcification in women with systemic lupus erythematosus28/08/2023Identification of the specific molecular and functional signatures of pre-beta-HDL: relevance to cardiovascular disease19/12/2023In vitro analysis of carotid lesions using a preliminary microwave sensor to detect vulnerable plaques: Correlation with histology, Duplex ultrasound examination, and computed tomography scanner: The Imaging and Microwave Phenotyping Assessment of Carotid stenosis Threat (IMPACT) study15/12/2023Acute myocardial infarction preferentially alters low-abundant, long-chain unsaturated phospholipid and sphingolipid species in plasma high-density lipoprotein subpopulations06/11/2023Fulminant myocarditis proven by early biopsy and outcomes

 27/07/2023Integrated omics approach for the identification of HDL structure-function relationships in PCSK9-related familial hypercholesterolemia28/03/2023Lipidomic analysis of adipose-derived extracellular vesicles reveals specific EV lipid sorting informative of the obesity metabolic state07/03/2023Association Between Shock Etiology and 5-Year Outcomes After Venoarterial Extracorporeal Membrane Oxygenation09/12/2022Impacts of a high-fat diet on the metabolic profile and the phenotype of atrial myocardium in mice01/12/2022A diagnostic proposal for sarcopenic obesity in adults based on body composition phenotypes01/08/2022Persistence of severe liver fibrosis despite substantial weight loss with bariatric surgery11/12/2021Predicting 90-day survival of patients with COVID-19: Survival of Severely Ill COVID (SOSIC) scores01/09/2021Tolerogenic Dendritic Cells Shape a Transmissible Gut Microbiota That Protects From Metabolic Diseases02/06/2021Tolerogenic Dendritic Cells Shape a Transmissible Gut Microbiota That Protects From Metabolic Diseases19/04/2021Extracorporeal membrane oxygenation network organization and clinical outcomes during the COVID-19 pandemic in Greater Paris, France: a multicentre cohort study01/03/2021Perceptions of the environment moderate the effects of objectively-measured built environment attributes on active transport. An ACTI-Cités study17/12/2020Large-Scale Assessment of Health-Related Physical Fitness in French Older Adults: Feasibility and Validity27/10/2020Free cholesterol transfer to high-density lipoprotein (HDL) upon triglyceride lipolysis underlies the U-shape relationship between HDL-cholesterol and cardiovascular disease13/08/2020Loss of G protein pathway suppressor 2 in human adipocytes triggers lipid remodeling by upregulating ATP binding cassette subfamily G member 122/06/2018Differential Associations of Walking and Cycling with Body Weight, Body Fat and Fat Distribution – the ACTI-Cités Project26/09/2017Cross-Sectional and longitudinal associations of objectively-measured physical activity on blood pressure: evaluation in 37 countries14/09/2017Patent Foramen Ovale Closure or Anticoagulation vs. Antiplatelets after Stroke03/04/2017Accelerometer assessed moderate-to-vigorous physical activity and successful ageing: results from the Whitehall II study