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Towards Heterogeneous Data Integration: The NeuroLang Approach

Abstract : A major goal of neuroscience research is to understand the circuits and patterns of neural activity that give rise to mental processes and behavior. But a task of this complexity often requires the integration of datasets, which are usually presented under a variety of topologies –ontologies, images, text–. This makes those analyses prone to human error and time-consuming. We believe that a tool with the ability to integrate heterogeneous datasets in a comprehensive and unified manner would alleviate the workload required for such a task. To this end, we will demonstrate how we can combine different datasets to perform an analysis that allows us to use histological knowledge of the brain and relate it to a variety of cognitive processes. Our main proposal is that having a probabilistic language based on first-order logic able to combine data under different topologies, solve complex queries and deal with uncertainty in a single framework is a great fit for this type of analysis.
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https://hal.inria.fr/hal-03786717
Contributor : Gaston Zanitti Connect in order to contact the contributor
Submitted on : Friday, September 23, 2022 - 4:10:26 PM
Last modification on : Thursday, November 3, 2022 - 2:06:08 PM

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  • HAL Id : hal-03786717, version 1

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Gastón E Zanitti, Majd Abdallah, Demian Wassermann. Towards Heterogeneous Data Integration: The NeuroLang Approach. OHBM 2022 - Organization for Human Brain Mapping, Jun 2022, Glasgow, United Kingdom. ⟨hal-03786717⟩

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