Apprentissage interprétable de la criminalité en france (2012-2021)
In Actes de l’atelier gestion et analyse des données spatiales et temporelles
Abstract
L’activité criminelle en France a connu une évolution significative au cours des deux dernières décennies, marquée par la recrudescence des actes de malveillance, notamment liés aux mouvements sociaux et syndicaux, aux émeutes, ainsi qu’au terrorisme. Dans ce contexte difficile, l’utilisation de techniques issues de l’intelligence artificielle pourrait offrir de nombreuses perspectives pour renforcer la sûreté publique et privée en France. Un exemple de cette approche est l’analyse spatio-temporelle des données de criminalité, déjà couronnée de succès au Brésil (Da Silva et al., 2020), au Proche-Orient (Tolan et al., 2015), et dans d’autres pays. Dans le cadre de ce travail, nous explorons la possibilité d’appliquer cette approche au contexte français.
Navigating pharmacophore space to identify activity discontinuities: A case study with BCR-ABL
In Molecular Informatics
Abstract
Abstract 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.
Refinement of a ligand activity and representation of topological phamacophores in a colored network
In Proceedings of the 11èmes journées de la société française de chémoinformatique
Abstract
Structure-Activity Relationships is a critical aspect of drug design. It enables us to examine ligand interactions and performances towards specific targets, then to design effective drugs for treating diseases or improving existing medical therapies. In this context, we specifically study the activity of ligands towards kinases using the BCR-ABL dataset. The work is dedicated to introduce a refinement method for the activity of molecules. Instead of considering anity as a binary activity, a molecule being either active or inactive, the compounds were partitioned into 4 classes according to their activity: very active, moderately active, slightly active, inactive. This activity is later used to evaluate molecular descriptors called topological pharmacophores [1]. These pharmacophores provide essential information by representing the key structural features of a molecule. Their quality is determined by measuring their “growth-rate” which corresponds to the ratio of active molecules over inactive ones, among the molecules supported by the pharmacophore. In our work, the calculation of the growth-rate is based on the classes of activity that we have created. Consequently, we will obtain three measurements of the growth rate, each one being related to a class of activity. In addition, we proposed to convert the new information of the quality of the pharmacophores into a visual representation called “The Pharmacophore Network” [2]. The latter is a graph whose nodes represent the pharmacophores and edges represent a graph-edit distance that separates them. Our goal was to structure more finely the pharmacophore space and to be able to detect visually interesting areas that can be explored. For this purpose, we integrated colors in this Pharmacophore Network, where each color refers to a class of activity.
On the historical evolution of the performance versus cost ratio of Raspberry Pi computers
In Conférence francophone d’informatique en parallélisme, architecture et système (compas 2023)
Abstract
This article aims to analyze the historical evolution of the cost/performance ratio of the Raspberry Pi family of computers, given their representativeness in the field of single-board computers. While comparing the cost/performance ratio of different models of single-board computers is not a new idea, there are no studies focused on evaluating the performance evolution and associated costs of all generations of the Raspberry Pi B line. Our analysis considered all generations of Raspberry Pi B line available on the market until 2023, and we adjusted computer prices based on the 2012 dollar value, the year of the first Raspberry Pi’s launch. The results indicate a clear trend of increasing performance over time, accompanied by a tendency for the price paid for performance to decrease. This reduction becomes even more pronounced when considering the depreciation of the dollar compared to its value in 2012.
Clustering en chémoinformatique pour le raffinement de l’activité des molécules
In Proceedings of the second computer science UTM PhD symposium
Abstract
Dans le domaine de la conception des médicaments, la chémoinformatique utilise des méthodes informatiques et mathématiques pour analyser des données chimiques et biologiques et essayer de trouver très en amont des molécules intéressantes. Dans notre contexte, nous transformons les molécules pour ne conserver que leurs caractéristiques pharmacophoriques (partie active de la molécule). L’objectif de ce travail est de raffiner l’activité des molécules qui seront utilisées dans le processus de conception des médicaments en des classes d’activité. Cela permettra aux chimistes et pharmaciens une meilleure visualisation et compréhension de l’activité des molécules, et fournira des données plus fines pour le développement ultérieur d’un modèle de prédiction des molécules d’interêt therapeutique.