M
Malika Smaïl-Tabbone
Researcher at University of Lorraine
Publications - 82
Citations - 1262
Malika Smaïl-Tabbone is an academic researcher from University of Lorraine. The author has contributed to research in topics: Knowledge extraction & Domain knowledge. The author has an hindex of 16, co-authored 82 publications receiving 1000 citations. Previous affiliations of Malika Smaïl-Tabbone include Nancy-Université & French Institute for Research in Computer Science and Automation.
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Journal ArticleDOI
Application of Artificial Intelligence to Gastroenterology and Hepatology
Catherine Le Berre,William J. Sandborn,Sabeur Aridhi,Marie-Dominique Devignes,Laure Fournier,Malika Smaïl-Tabbone,Silvio Danese,Laurent Peyrin-Biroulet +7 more
TL;DR: The ways in which AI may help physicians make a diagnosis or establish a prognosis are reviewed and its limitations are discussed, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.
Journal ArticleDOI
IntelliGO: a new vector-based semantic similarity measure including annotation origin
Sidahmed Benabderrahmane,Malika Smaïl-Tabbone,Olivier Poch,Amedeo Napoli,Marie-Dominique Devignes +4 more
TL;DR: The IntelliGO similarity measure provides a customizable and comprehensive method for quantifying gene similarity based on GO annotations and displays a robust set-discriminating power which suggests it will be useful for functional clustering.
Journal ArticleDOI
Protein docking using case-based reasoning.
TL;DR: This work has developed a case‐based reasoning approach called KBDOCK which systematically identifies and reuses domain family binding sites from the authors' database of nonredundant DDIs and provides a near‐perfect way to model single‐domain protein complexes when full‐homology templates are available.
Proceedings Article
Many-Valued Concept Lattices for Conceptual Clustering and Information Retrieval
TL;DR: An extension of the Galois connection to deal with many-valued formal contexts with respect to similarity between attribute values in a many- valued context is presented.
Journal ArticleDOI
Integrative relational machine-learning for understanding drug side-effect profiles
Emmanuel Bresso,Emmanuel Bresso,Renaud Grisoni,Gino Marchetti,Gino Marchetti,Arnaud Sinan Karaboga,Michel Souchet,Marie-Dominique Devignes,Marie-Dominique Devignes,Malika Smaïl-Tabbone,Malika Smaïl-Tabbone +10 more
TL;DR: Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules.