Mohameth François Sy

Team Lead, Data and Knowledge Engineering, FAIR Data and Knowledge Graph, Search, AI

Welcome 👋

My name is Mohameth François Sy, hailing from Senegal, the westernmost country of the African continent mainland also known as Worth a visit .

I am an experienced data, knowledge engineer and team lead with many years of experience on building knowledge graphs products to support data-driven use cases and projects such as building biologically detailed digital reconstructions and simulations of the mouse brain at the Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), in Switzerland. I have built, led diverse and international teams of data and knowledge engineers as well as data stewardships and curators. Along the way, I collaborated with scientists, developers, Product and PMO teams to deliver.

You’ll find a selected list of projects and publications I contributed to as well as talks I gave in various event types such as meetups and workshops.

selected projects

Knowledge Graph Analytics for insights on COVID-19

knowledge graph generation, analysis and exploration from COVID-19 Open Research Dataset to perform literature review of the role of glucose metabolism deregulations in the progression of COVID-19.

Blue Brain Nexus

Blue Brain Nexus – Data and Knowledge Graph Management for Data-Driven Science

Knowledge Graph Forge

A python framework for building, validating and using a knowledge graph

selected publications

Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science

Mohameth François Sy, Bogdan Roman, Samuel Kerrien, Didac Montero Mendez, Henry Genet, Wojciech Wajerowicz, Michaël Dupont, and 16 more authors

Semantic Web, 2023

Publisher: IOS Press

Abstract Paper Code News Website

Modern data-driven science often consists of iterative cycles of data discovery, acquisition, preparation, analysis, model building and validation leading to knowledge discovery as well as dissemination at scale. The unique challenges of building and simulating the whole rodent brain in the Swiss EPFL Blue Brain Project (BBP) required a solution to managing large-scale highly heterogeneous data, and tracking their provenance to ensure quality, reproducibility and attribution throughout these iterative cycles. Here, we describe Blue Brain Nexus (BBN), an ecosystem of open source, domain agnostic, scalable, extensible data and knowledge graph management systems built by BBP to address these challenges. BBN builds on open standards and interoperable semantic web technologies to enable the creation and management of secure RDF-based knowledge graphs validated by W3C SHACL. BBN supports a spectrum of (meta)data modeling and representation formats including JSON and JSON-LD as well as more formally specified SHACL-based schemas enabling domain model-driven runtime API. With its streaming event-based architecture, BBN supports asynchronous building and maintenance of multiple extensible indices to ensure high performance search capabilities and enable analytics. We present four use cases and applications of BBN to large-scale data integration and dissemination challenges in computational modeling, neuroscience, psychiatry and open linked data.
KG_NLP

A Machine-Generated View of the Role of Blood Glucose Levels in the Severity of COVID-19

Emmanuelle Logette, Charlotte Lorin, Cyrille Favreau, Eugenia Oshurko, Jay S. Coggan, Francesco Casalegno, Mohameth François Sy, and 11 more authors

Frontiers in Public Health, 2021

Abstract Paper Code News

SARS-CoV-2 started spreading toward the end of 2019 causing COVID-19, a disease that reached pandemic proportions among the human population within months. The reasons for the spectrum of differences in the severity of the disease across the population, and in particular why the disease affects more severely the aging population and those with specific preconditions are unclear. We developed machine learning models to mine 240,000 scientific articles openly accessible in the CORD-19 database, and constructed knowledge graphs to synthesize the extracted information and navigate the collective knowledge in an attempt to search for a potential common underlying reason for disease severity. The machine-driven framework we developed repeatedly pointed to elevated blood glucose as a key facilitator in the progression of COVID-19. Indeed, when we systematically retraced the steps of the SARS-CoV-2 infection, we found evidence linking elevated glucose to each major step of the life-cycle of the virus, progression of the disease, and presentation of symptoms. Specifically, elevations of glucose provide ideal conditions for the virus to evade and weaken the first level of the immune defense system in the lungs, gain access to deep alveolar cells, bind to the ACE2 receptor and enter the pulmonary cells, accelerate replication of the virus within cells increasing cell death and inducing an pulmonary inflammatory response, which overwhelms an already weakened innate immune system to trigger an avalanche of systemic infections, inflammation and cell damage, a cytokine storm and thrombotic events. We tested the feasibility of the hypothesis by manually reviewing the literature referenced by the machine-generated synthesis, reconstructing atomistically the virus at the surface of the pulmonary airways, and performing quantitative computational modeling of the effects of glucose levels on the infection process. We conclude that elevation in glucose levels can facilitate the progression of the disease through multiple mechanisms and can explain much of the differences in disease severity seen across the population. The study provides diagnostic considerations, new areas of research and potential treatments, and cautions on treatment strategies and critical care conditions that induce elevations in blood glucose levels.