Machine Learning Bio-molecular Interactions from Temporal Logic Properties

With the advent of formal languages for modeling bio-molecu\-lar interaction systems, the design of automated reasoning tools to assist the biologist becomes possible. The biochemical abstract machine BIOCHAM software environment offers a rule-based language to model bio-molecular interactions and an original temporal logic based language to formalize the biological properties of the system. Building on these two formal languages, machine learning techniques can be used to infer new molecular interaction rules from temporal properties. In this context, the aim is to semi-automatically correct or complete models from observed biological properties of the system. Machine learning from temporal logic formulae is quite new however, both from the machine learning perspective and from the Systems Biology perspective. In this paper we present an ad-hoc enumerative method for structural learning from temporal properties and report on the evaluation of this method on formal biological models of the literature

Conception d'un module d'annotation semi-automatique de génomes à l'aide d'une hiérarchie fonctionnelle

Afin de comprendre le mode de fonctionnement de certains organismes, les biologistes en étudient les protéines en leur attribuant entre autres des fonctions. Cette tâche, appelée annotation fonctionnelle est extrêmement longue. Il est donc indispensable de l'automatiser en partie. Nous utilisons une hiérarchie fonctionnelle dérivée de SubtiList pour annoter les protéines par notre système semi-automatique car les génomes bactériens, Lactobacillus bulgaricus et sakei, qui servent à notre étude ont été annotés à l'aide de cette hiérarchie. Afin de prédire les classes fonctionnelles de protéines pour les proposer aux experts biologistes de l'INRA, nous utilisons des algorithmes d'apprentissage sur des critères décrivant les protéines. Ceux-ci renseignent sur les relations de similarité entre protéines et sur leurs propriétés intrinsèques. Tilde, un système d'apprentissage au premier ordre (de PLI) est utilisé pour construire des arbres de décision qui sont ensuite transformés en règles. Un protocole d'expérimentation est mis en place afin de prédire les classes fonctionnelles d'une protéine aux différents niveaux de la hiérarchie. Nous ajoutons aux règles trouvées un indice de confiance calculé à partir des résultats obtenus sur les données de validation. Tous les résultats sont stockés dans une base de données consultable via des pages web. Nous recensons dans un premier temps les différents couples annotations/prédictions possibles en fonction de la hiérarchie puis nous proposons des nouvelles mesures hiérarchiques pour évaluer notre système. Nous comparons notre système à Clus-HMC qui est moins expressif. Nous donnons quelques règles et arbres en exemple.To understand how organisms work, biologists need to study proteins by assigning them some functions. This task is named functional annotation and is extremely time-consuming. There is thus a crucial need to automate functional annotation. So we need to automate some steps. In our semi-automatic system, we use a fontional hierarchy derived from SubtiList to annotate proteins. This choice was made because it is the hierarchy used at INRA to annotate the bacterial genoms we exploit: Lactobacillus bulgaricus and sakei. We use machine learning systems on criteria which give us information on similarity results with other proteins and intrinsic properties of each protein. We use TILDE, which is a first ordrer machine learning system (ILP), to generate decision trees. These trees are transformed into rules. To predict functional class of proteins on different levels of hierarchy, we build an experiment protocol. To each rule we add a confidence level calculated on results we obtain on validation data. All results are stored in a database, available on the web. To evaluate our system we list all the possible annotation/prediction pairs that can be obtained using the hierarchy, then we propose new hierarchical measures. We compare our system to the clus-HMC approach which is less expressive. We give some trees and rules as example.ORSAY-PARIS 11-BU Sciences (914712101) / SudocSudocFranceF

In situ characterization of irradiation-induced microstructural evolution in urania single crystals at 773 K

International audienceImplantations with low-energy ions (Xe, La) on UO2 single crystals at 773 K were performed to investigate the role played by both the radiation damage and the incorporation of foreign elements on the matrix destabilisation. The radiation damage was monitored by both in situ RBS-C and in situ TEM during ion irradiation experiments performed at 773 K. RBS-C data shows a similar regular increase of the radiation-induced disorder in crystals for both Xe and La ions followed by a saturation plateau at about 3–4 dpa. An unexpected difference of the value of the saturation plateaus is observed, with a higher value recorded for Xe-irradiated crystals. In situ TEM images show the apparition and evolution of several defects as a function of the ion dose up to 40 dpa, irrespective of the nature of the bombarding ion: ‘black dots’ defects, dislocation loops and lines, and finally a dislocation network at high dpa. Nanometre-sized gas bubbles were observed at 773 K for the Xe-implanted crystal for doses larger than 3 dpa. Neither precipitate nor cavity were observed on La-implanted crystals. The difference in the saturation plateau as seen by RBS-C can be ascribed to the formation of the Xe aggregates that lead to an increase of the dechannelling yield