Navigating pharmacophore space to identify activity discontinuities: A case study with BCR-ABL Maroua Lejmi, Damien Geslin, Ronan Bureau, Bertrand Cuissart, Ilef Ben Slima, Nida Meddouri, Amel Borgi, Jean‐Luc Lamotte, Alban Lepailleur Molecular Informatics, 2024 The exploration of chemical space is a fundamental aspect of chemoinformatics, particularly when one explores a large compound data set to relate chemical structures with molecular properties. In this study, we extend our previous work on chemical space visualization at the pharmacophoric level. Instead of using conventional binary classification of affinity (active vs inactive), we introduce a refined approach that categorizes compounds into four distinct classes based on their activity levels: super active, very active, active, and inactive. This classification enriches the color scheme applied to pharmacophore space, where the color representation of a pharmacophore hypothesis is driven by the associated compounds. Using the BCR‐ABL tyrosine kinase as a case study, we identified intriguing regions corresponding to pharmacophore activity discontinuities, providing valuable insights for structure‐activity relationships analysis.
COVID-19 pandemic's effect on the mental health among the Tunisian general population: Associated factors mining via machine learning Ilef Ben Slima, Sourour Ammar, Mariem Turki, Wiem Bouattour, Jihene Aloulou Scientific African, 2023 The emergence of COVID-19 pandemic has caused a brutal change in the lifestyle of citizens all around the world and greatly affected the mental health of individuals. In Tunisia, many psychological problems have been triggered during the first peak of the pandemic like anxiety, depression, sleep disturbances, and suicide risk. To overcome such disorders, it is crucial to identify the main factors leading to the mental disorders and then develop preventive strategies if a novel form of pandemic or a traumatic event appears. This paper proposes a novel association rules-based approach to characterize the profiles of citizens highly vulnerable to psychological disorders when confronted to traumatic events. The aim of this work is to use machine learning techniques in order to identify the major factors as well as the clusters of features leading to several psychiatric disorders during the COVID-19 pandemic in Tunisia. Many stressors were found to be associated with some psychiatric disorders. The stronger associations were found between doctor consultation and anxiety, COVID test and depression, quarantine and insomnia, and direct contact with a suspected case and peritraumatic distress and dissociation. In addition, it has been found that some factors, like female gender and regular worker, are not leading to mental disorders when they are treated alone, however, they present a high influence on the mental health when they are associated with other factors. For instance, this work discovered that women who have psychiatric history and who always drink coffee are exposed to depression during the pandemic. Other profile of citizens who are highly vulnerable to peritraumatic dissociation concerns students who are confined and who have recent symptoms. The characterization of such vulnerable profiles can provide considerable decision support for medical staff.
Attributes regrouping by genetic algorithm in fuzzy inference systems Maroua Lejmi, Ilef Ben Slima, Amel Borgi Procedia Computer Science, 2022 Fuzzy Rule-Based Classification Systems (FRBCS) are a very popular and useful tool due to their interpretable classification models based on linguistic variables. However, a high number of attributes leads to an explosion of the number of classification rules. In this paper, we are interested in ensemble methods applied to FRBCS. An attributes regrouping method based on association rules and called SIFRA was proposed to solve this problem. It determines the relevant groups of related attributes that will be treated separately by FRBCSs. We propose a Genetic Algorithm-based method to select final groups of related attributes determined by SIFRA. In our approach, the genetic algorithm is used to filter the groups of related attributes and conserve the best ones, while maintaining a good classification rate and a reduced number of rules. We lead experiments to compare our contribution with previous methods in the same context; the experimental results are satisfactory.
PCMCR: A Novel Conflict Resolution Strategy based on Possibility Theory for Human Activity Recognition Ilef Ben Slima, Amina Jarraya, Sourour Ammar, Amel Borgi Procedia Computer Science, 2022 DCR is a Distributed Collaborative Reasoning multi-agent model that aims to recognize human activities in smart homes from distributed, heterogeneous and dynamic sensor data. In this model, distributed agents with diverse classifiers, detect sensor stream data, make local predictions, communicate and collaborate to identify current activities. Conflict resolution strategies are applied to generate one final predicted activity when the local predicted activity of an agent is different from received predicted activities of other agents. In this paper, a possibilistic conflict resolution strategy, PCMCR, is proposed. Possibility theory is particularly efficient in combining multiple agents predictions that can be incomplete, uncertain, and conflicting. The PCMCR strategy deals with uncertainty factor which can be presented in the predictions of poor agents. It can take advantage of the complementary information given by each agent, even the weak ones. We experimentally test this strategy by performing an evaluation study on Aruba dataset. The obtained results indicate an enhancement in terms of accuracy, F-measure and G-mean metrics compared to the existing conflict resolution method max-trust of DCR, to the centralized model and to an existing distributed system.
Features’ associations in fuzzy ensemble classifiers Ilef Ben Slima, Amel Borgi Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2018
Regrouping attributes by association rules in fuzzy inference systems Revue Des Nouvelles Technologies De L Information, 2015
RECENT SCHOLAR PUBLICATIONS
Generating Local Rules in Fuzzy Rule-Based Classification Systems M Lejmi, B Cuissart, IB Slima, N Meddouri, JL Lamotte, A Borgi International Conference on Computational Science and Its Applications, 150-165 , 2025 2025.0
CNN-Trans: A Two-Branch CNN Transformer Model for Multivariate Time Series Classification. S Hassine, S Ammar, IB Slima ICAART (2), 418-428 , 2025 2025.0 Citations: 4
Clustering and Association Rules Mining for Coral Reef Fish Distribution: A Data-Driven Approach in the Mediterranean Sea I Ben Slima, A Borgi, F Sellem International Conference on Management of Digital, 426-435 , 2024 2024.0
Navigating pharmacophore space to identify activity discontinuities: A case study with BCR‐ABL M Lejmi, D Geslin, R Bureau, B Cuissart, I Ben Slima, N Meddouri, A Borgi, ... Molecular Informatics 43 (8), e202400050 , 2024 2024.0 Citations: 3
P17-Refinement of a ligand activity and representation of topological pharmacophores in a colored network M Lejmi, D Geslin, B Cuissart, IB Slima, N Meddouri, R Bureau, ... 11emes Journées de la Société Française de Chémoinformatique, 65 , 2023 2023.0
COVID-19 pandemic’s effect on the mental health among the Tunisian general population: Associated factors mining via machine learning IB Slima, S Ammar, M Turki, W Bouattour, J Aloulou Scientific African 21, e01804 , 2023 2023.0 Citations: 2
Clustering en chémoinformatique pour le raffinement de l'activité des molécules M Lejmi, IB Slima, B Cuissart, N Meddouri, R Bureau, A Lepailleur, ... Proceedings of the second Computer Science UTM PhD Symposium, 51-55 , 2023 2023.0
Possibilistic rank-level fusion method for person re-identification I Ben Slima, S Ammar, M Ghorbel Neural Computing and Applications 34 (17), 14151-14168 , 2022 2022.0 Citations: 5
PCMCR: A novel conflict resolution strategy based on possibility theory for human activity recognition IB Slima, A Jarraya, S Ammar, A Borgi Procedia Computer Science 207, 926-935 , 2022 2022.0 Citations: 3
Attributes regrouping by genetic algorithm in fuzzy inference systems M Lejmi, IB Slima, A Borgi Procedia Computer Science 207, 1037-1046 , 2022 2022.0 Citations: 2
Scientific African A Raimi, A Roopnarain, R Adeleke 2021.0
Possibilistic classifier combination for person re-identification I Ben Slima, S Ammar, M Ghorbel, Y Kessentini Mediterranean Conference on Pattern Recognition and Artificial Intelligence … , 2020 2020.0 Citations: 2
Apprentissage par Regroupement d'Attributs dans les Systèmes d'Inférence Floue IB Slima Université de Tunis El Manar , 2019 2019.0 Citations: 1
Apprentissage par Regroupement d'Attributs dans les Systèmes d'Inférence Floue.(Regrouping Attributes in Fuzzy Inference Systems). IB Slima Tunis El Manar University, Tunisia , 2019 2019.0
Supervised methods for regrouping attributes in fuzzy rule-based classification systems: I. Ben Slima, A. Borgi I Ben Slima, A Borgi Applied Intelligence 48 (12), 4577-4593 , 2018 2018.0 Citations: 9
Features’ associations in fuzzy ensemble classifiers I Ben Slima, A Borgi International conference on database and expert systems applications, 369-377 , 2018 2018.0 Citations: 3
Attributes regrouping by association rules in SUCRAGE R Zaatour, A Borgi, IB Slima 2016 11th International Conference on Intelligent Systems: Theories and … , 2016 2016.0
Attributes regrouping by association rules in the fuzzy inference systems AB Ilef Ben Slima EGC 2015 Luxembourg, 317-328 , 2015 2015.0 Citations: 4
Regroupement d'attributs par règles d'association dans les systèmes d'inférence floue. IB Slima, A Borgi, T LR11ES14 LIPAH EGC, 317-328 , 2015 2015.0 Citations: 2
Scientific African IB Slima, S Ammar, M Turki, W Bouattour, J Aloulou
MOST CITED SCHOLAR PUBLICATIONS
Supervised methods for regrouping attributes in fuzzy rule-based classification systems: I. Ben Slima, A. Borgi I Ben Slima, A Borgi Applied Intelligence 48 (12), 4577-4593 , 2018 2018.0 Citations: 9
Possibilistic rank-level fusion method for person re-identification I Ben Slima, S Ammar, M Ghorbel Neural Computing and Applications 34 (17), 14151-14168 , 2022 2022.0 Citations: 5
CNN-Trans: A Two-Branch CNN Transformer Model for Multivariate Time Series Classification. S Hassine, S Ammar, IB Slima ICAART (2), 418-428 , 2025 2025.0 Citations: 4
Attributes regrouping by association rules in the fuzzy inference systems AB Ilef Ben Slima EGC 2015 Luxembourg, 317-328 , 2015 2015.0 Citations: 4
Navigating pharmacophore space to identify activity discontinuities: A case study with BCR‐ABL M Lejmi, D Geslin, R Bureau, B Cuissart, I Ben Slima, N Meddouri, A Borgi, ... Molecular Informatics 43 (8), e202400050 , 2024 2024.0 Citations: 3
PCMCR: A novel conflict resolution strategy based on possibility theory for human activity recognition IB Slima, A Jarraya, S Ammar, A Borgi Procedia Computer Science 207, 926-935 , 2022 2022.0 Citations: 3
Features’ associations in fuzzy ensemble classifiers I Ben Slima, A Borgi International conference on database and expert systems applications, 369-377 , 2018 2018.0 Citations: 3
COVID-19 pandemic’s effect on the mental health among the Tunisian general population: Associated factors mining via machine learning IB Slima, S Ammar, M Turki, W Bouattour, J Aloulou Scientific African 21, e01804 , 2023 2023.0 Citations: 2
Attributes regrouping by genetic algorithm in fuzzy inference systems M Lejmi, IB Slima, A Borgi Procedia Computer Science 207, 1037-1046 , 2022 2022.0 Citations: 2
Possibilistic classifier combination for person re-identification I Ben Slima, S Ammar, M Ghorbel, Y Kessentini Mediterranean Conference on Pattern Recognition and Artificial Intelligence … , 2020 2020.0 Citations: 2
Regroupement d'attributs par règles d'association dans les systèmes d'inférence floue. IB Slima, A Borgi, T LR11ES14 LIPAH EGC, 317-328 , 2015 2015.0 Citations: 2
Apprentissage par Regroupement d'Attributs dans les Systèmes d'Inférence Floue IB Slima Université de Tunis El Manar , 2019 2019.0 Citations: 1
Generating Local Rules in Fuzzy Rule-Based Classification Systems M Lejmi, B Cuissart, IB Slima, N Meddouri, JL Lamotte, A Borgi International Conference on Computational Science and Its Applications, 150-165 , 2025 2025.0
Clustering and Association Rules Mining for Coral Reef Fish Distribution: A Data-Driven Approach in the Mediterranean Sea I Ben Slima, A Borgi, F Sellem International Conference on Management of Digital, 426-435 , 2024 2024.0
P17-Refinement of a ligand activity and representation of topological pharmacophores in a colored network M Lejmi, D Geslin, B Cuissart, IB Slima, N Meddouri, R Bureau, ... 11emes Journées de la Société Française de Chémoinformatique, 65 , 2023 2023.0
Clustering en chémoinformatique pour le raffinement de l'activité des molécules M Lejmi, IB Slima, B Cuissart, N Meddouri, R Bureau, A Lepailleur, ... Proceedings of the second Computer Science UTM PhD Symposium, 51-55 , 2023 2023.0
Scientific African A Raimi, A Roopnarain, R Adeleke 2021.0
Apprentissage par Regroupement d'Attributs dans les Systèmes d'Inférence Floue.(Regrouping Attributes in Fuzzy Inference Systems). IB Slima Tunis El Manar University, Tunisia , 2019 2019.0
Attributes regrouping by association rules in SUCRAGE R Zaatour, A Borgi, IB Slima 2016 11th International Conference on Intelligent Systems: Theories and … , 2016 2016.0
Scientific African IB Slima, S Ammar, M Turki, W Bouattour, J Aloulou
