Alban Gaignard

I obtained a Ph.D. in Computer Science from the University of Nice-Sophia Antipolis. My research interests cover knowledge graphs and distributed systems (workflows, provenance) in the context of life sciences.

I’m involved in several national and international projects in the context of biomedical data integration and sharing such as the PEPR Santé-numérique (ShareFAIR and Neurovasc programmes), the PPR ABRomics, and the ANR MeKaNo.

I co-lead the IFB “interoperability” and “health” working groups and represents the french activities in these domains for the European ELIXIR infrastructure.

news

Oct 20, 2025	Invited speaker at CMLS 2025 to present “Beyond FAIR assessment black boxes: ontology-based evaluations and the challenge of comparing FAIR scores”. [slides]
Oct 14, 2025	Alexandrina Bodrug presented her activities on FAIRifying genomic data at PEPR Santé Numérique days. [slides, poster]
Oct 1, 2025	Paper published in Transactions on Large-Scale Data- and Knowledge-Centered Systems LVIII.
Sep 24, 2025	Invited talk at ISGC 2025 - IA group premeeting session to present “Intracranial aneurysm data integration and sharing in the Neurovasc project”. [slides]
Sep 8, 2025	Invited talk at BC2 workshop T7 to present “Biomedical provenance metadata for intracranial aneurysm studies”. [slides]
Jul 10, 2025	Ulysse Le Clanche presented a first contribution of his PhD thesis at JOBIM 2025. [paper]
Jun 17, 2025	Invited talk to present LLM-based approaches for annotating web pages and performing hybrid neuro-symbolic search at French Bioinformatics Institute (IFB). [slides]
Jun 4, 2025	Paper published in the Semantic Web Journal on LLM-based annotation of web pages with Schema.org. [paper]
Jun 2, 2025	Paper accepted at ESWC 2025 on the prediction of clinical outcomes from patient care pathways represented as knowledge graphs. [paper]
Jun 1, 2025	Demo paper accepted at ESWC 2025 on Retrieval-Augmented SPARQL Query Processing (best demo nominee). [paper] [poster]

more news ...

selected publications

FAIR-Checker: supporting digital resource findability and reuse with Knowledge Graphs and Semantic Web standards

Alban Gaignard, Thomas Rosnet, Frédéric Lamotte, and 2 more authors

Journal of Biomedical Semantics, Dec 2023

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Developing and reusing bioinformatics data analysis pipelines using scientific workflow systems

Marine Djaffardjy, George Marchment, Clémence Sebe, and 5 more authors

Computational and Structural Biotechnology Journal, Dec 2023

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Data analysis pipelines are now established as an effective means for specifying and executing bioinformatics data analysis and experiments. While scripting languages, particularly Python, R and notebooks, are popular and sufficient for developing small-scale pipelines that are often intended for a single user, it is now widely recognized that they are by no means enough to support the development of large-scale, shareable, maintainable and reusable pipelines capable of handling large volumes of data and running on high performance computing clusters. This review outlines the key requirements for building large-scale data pipelines and provides a mapping of existing solutions that fulfill them. We then highlight the benefits of using scientific workflow systems to get modular, reproducible and reusable bioinformatics data analysis pipelines. We finally discuss current workflow reuse practices based on an empirical study we performed on a large collection of workflows.
Large-scale regulatory and signaling network assembly through linked open data

Marie Lefebvre, Alban Gaignard, Maxime Folschette, and 2 more authors

Database: The Journal of Biological Databases and Curation, Dec 2021
Findable and reusable workflow data products: A genomic workflow case study

Alban Gaignard, Hala Skaf-Molli, and Khalid Belhajjame

Semantic Web, Dec 2020
Location of intracranial aneurysms is the main factor associated with rupture in the ICAN population

Olivia Rousseau, Matilde Karakachoff, Alban Gaignard, and 15 more authors

Journal of Neurology, Neurosurgery, and Psychiatry, Dec 2020
Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities

Sarah Cohen-Boulakia, Khalid Belhajjame, Olivier Collin, and 11 more authors

Future Generation Computer Systems, Dec 2017

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With the development of new experimental technologies, biologists are faced with an avalanche of data to be computationally analyzed for scientific advancements and discoveries to emerge. Faced with the complexity of analysis pipelines, the large number of computational tools, and the enormous amount of data to manage, there is compelling evidence that many if not most scientific discoveries will not stand the test of time: increasing the reproducibility of computed results is of paramount importance. The objective we set out in this paper is to place scientific workflows in the context of reproducibility. To do so, we define several kinds of reproducibility that can be reached when scientific workflows are used to perform experiments. We characterize and define the criteria that need to be catered for by reproducibility-friendly scientific workflow systems, and use such criteria to place several representative and widely used workflow systems and companion tools within such a framework. We also discuss the remaining challenges posed by reproducible scientific workflows in the life sciences. Our study was guided by three use cases from the life science domain involving in silico experiments.