Data-driven Gene Regulatory Network Inference based on Classification Algorithms

International audienceDifferent paradigms of gene regulatory network inference have been proposed so far in the literature. The data-driven family is an important inference paradigm, that aims at scoring potential regulatory links between transcription factors and target genes, analyzing gene expression datasets. Three major approaches have been proposed to score such links relying on correlation measures, mutual information metrics, and regression algorithms. In this paper we present a new family of data-driven inference approaches, inspired on the regression based family, and based on classification algorithms. This paper advocates for the use of this paradigm as a new promising approach to infer gene regulatory networks. Indeed, the implementation and test of five new inference methods based on well-known classification algorithms shows that such an approach exhibits good quality results when compared to well-established paradigms

CARRERO BLANCO EN LA ENTREGA DE LAS VIVIENDAS DE GARCÍA ESCÁMEZ "CUATRO CAÑONES" [Material gráfico]

Reportaje fotográfico de la visita de Carrero Blanco a Gran CanariaCopia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 201

Subspace Clustering for all Seasons

International audienceSubspace clustering is recognized as a more general and difficult task than standard clustering since it requires to identify not only objects sharing similar feature values but also the various subspaces where these similarities appear. Many approaches have been investigated for subspace clustering in the literature using various clustering paradigms. In this paper, we present Chameleoclust, an evolutionary subspace clustering algorithm that incorporates a genome having an evolvable structure. The genome is a coarse grained genome defined as a list of tuples (the "genes"),each tuple containing numbers. These tuples are mapped at the phenotype level to denote core point locations in different dimensions, which are then used to collectively build the subspace clusters, by grouping the data around the core points. The algorithm has been assessed using a reference framework for subspace clustering evaluation, and compared to state-of-the-art algorithms on both real and synthetic datasets. The results obtained with the Chameleoclust algorithm show that evolution of evolution, through an evolvable genome structure, is usefull to solve a difficult problem such as subspace clustering

EvoMove: Evolutionary-based living musical companion

International audienceThe EvoMove system is a motion-based musical companion that relies on a commensal computing scheme. The system relies on wireless sensors to detect dancer moves. The sensor information is sent to KymeroClust, an evolutionary algorithm that identifies and maintains a clustering model of the move categories. The system uses this information to play audio samples according to the detected categories. These categories are not predefined, but are built dynamically by clustering the stream of data coming from the motion sensors. The EvoMove system has been tested by different users and subjective promising experiences are reported

Evolutionary novelty in the apoptotic pathway of aphids

Apoptosis, a conserved form of programmed cell death, shows interspecies differences that may reflect evolutionary diversification and adaptation, a notion that remains largely untested. Among insects, the most speciose animal group, the apoptotic pathway has only been fully characterized in Drosophila melanogaster, and apoptosis-related proteins have been studied in a few other dipteran and lepidopteran species. Here, we studied the apoptotic pathway in the aphid Acyrthosiphon pisum, an insect pest belonging to the Hemiptera, an earlier-diverging and distantly related order. We combined phylogenetic analyses and conserved domain identification to annotate the apoptotic pathway in A. pisum and found low caspase diversity and a large expansion of its inhibitory part, with 28 inhibitors of apoptosis (IAPs). We analyzed the spatiotemporal expression of a selected set of pea aphid IAPs and showed that they are differentially expressed in different life stages and tissues, suggesting functional diversification. Five IAPs are specifically induced in bacteriocytes, the specialized cells housing symbiotic bacteria, during their cell death. We demonstrated the antiapoptotic role of these five IAPs using heterologous expression in a tractable in vivo model, the Drosophila melanogaster developing eye. Interestingly, IAPs with the strongest antiapoptotic potential contain two BIR and two RING domains, a domain association that has not been observed in any other species. We finally analyzed all available aphid genomes and found that they all show large IAP expansion, with new combinations of protein domains, suggestive of evolutionarily novel aphidspecific functions

Regroupement de sous-espaces sur des ensembles de données statiques et des flux de données dynamiques à l'aide d'algorithmes bioinspirés

An important task that has been investigated in the context of high dimensional data is subspace clustering. This data mining task is recognized as more general and complicated than standard clustering, since it aims to detect groups of similar objects called clusters, and at the same time to find the subspaces where these similarities appear. Furthermore, subspace clustering approaches as well as traditional clustering ones have recently been extended to deal with data streams by updating clustering models in an incremental way. The different algorithms that have been proposed in the literature, rely on very different algorithmic foundations. Among these approaches, evolutionary algorithms have been under-explored, even if these techniques have proven to be valuable addressing other NP-hard problems. The aim of this thesis was to take advantage of new knowledge from evolutionary biology in order to conceive evolutionary subspace clustering algorithms for static datasets and dynamic data streams. Chameleoclust, the first algorithm developed in this work, takes advantage of the large degree of freedom provided by bio-like features such as a variable genome length, the existence of functional and non-functional elements and mutation operators including chromosomal rearrangements. KymeroClust, our second algorithm, is a k-medians based approach that relies on the duplication and the divergence of genes, a cornerstone evolutionary mechanism. SubMorphoStream, the last one, tackles the subspace clustering task over dynamic data streams. It relies on two important mechanisms that favor fast adaptation of bacteria to changing environments, namely gene amplification and foreign genetic material uptake. All these algorithms were compared to the main state-of-the-art techniques, obtaining competitive results. Results suggest that these algorithms are useful complementary tools in the analyst toolbox. In addition, two applications called EvoWave and EvoMove have been developed to assess the capacity of these algorithms to address real world problems. EvoWave is an application that handles the analysis of Wi-Fi signals to detect different contexts. EvoMove, the second one, is a musical companion that produces sounds based on the clustering of dancer moves captured using motion sensors.Une tâche importante qui a été étudiée dans le contexte de données à forte dimensionnalité est la tâche connue sous le nom de subspace clustering. Le subspace clustering est généralement reconnu comme étant plus compliqué que le clustering standard, étant donné que cette tâche vise à détecter des groupes d’objets similaires entre eux (clusters), et qu’en même temps elle vise à trouver les sous-espaces où apparaissent ces similitudes. Le subspace clustering, ainsi que le clustering traditionnel ont été récemment étendus au traitement de flux de données en mettant à jour les modèles de clustering de façon incrémentale. Les différents algorithmes qui ont été proposés dans la littérature, reposent sur des bases algorithmiques très différentes. Parmi ces approches, les algorithmes évolutifs ont été sous-explorés, même si ces techniques se sont avérées très utiles pour traiter d’autres problèmes NP-difficiles. L’objectif de cette thèse a été de tirer parti des nouvelles connaissances issues de l’évolution afin de concevoir des algorithmes évolutifs qui traitent le problème du subspace clustering sur des jeux de données statiques ainsi que sur des flux de données dynamiques. Chameleoclust, le premier algorithme développé au cours de ce projet, tire partie du grand degré de liberté fourni par des éléments bio-inspirés tels qu’un génome de longueur variable, l’existence d’éléments fonctionnels et non fonctionnels et des opérateurs de mutation incluant des réarrangements chromosomiques. KymeroClust, le deuxième algorithme conçu dans cette thèse, est un algorithme de k-medianes qui repose sur un mécanisme évolutif important: la duplication et la divergence des gènes. SubMorphoStream, le dernier algorithme développé ici, aborde le problème du subspace clustering sur des flux de données dynamiques. Cet algorithme repose sur deux mécanismes qui jouent un rôle clef dans l’adaptation rapide des bactéries à des environnements changeants: l’amplification de gènes et l’absorption de matériel génétique externe. Ces algorithmes ont été comparés aux principales techniques de l’état de l’art, et ont obtenu des résultats compétitifs. En outre, deux applications appelées EvoWave et EvoMove ont été développés pour évaluer la capacité de ces algorithmes à résoudre des problèmes réels. EvoWave est une application d’analyse de signaux Wi-Fi pour détecter des contextes différents. EvoMove est un compagnon musical artificiel qui produit des sons basés sur le clustering des mouvements d’un danseur, décrits par des données provenant de capteurs de déplacements

What can Reveal 1,018 Speeches of Fidel Castro?

International audienc

Hacer frente a los riesgos naturales y ambientales en los espacios rurales y urbanos latinoamericanos: características y factores

El 7e congreso del CEISAL (Consejo Europeo de Investigaciones Sociales de América Latina)sucederá en Porto (Portugal), del 12 al 15 de junio de 2013. En el marco de este congreso organizamos un simposio sobre el tema: « Hacer frente a los riesgos naturales y ambientales en los espacios rurales y urbanos latinoamericanos: características y factores de mutación de las vulnerabilidades sociales ». Se tratará, entre otros, de comparar las características de las vulnerabilidades sociales en las zonas rurales y en las zonas urbanas. Se pondrá especial atención en las ponencias que traten la relación vulnerabilidad y agua

What can Reveal 1,018 Speeches of Fidel Castro?

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Analyzing Open Data to Support Democracy: a Study Case Inspecting Electoral Fraud in Bolivian General Elections

International audienceThe objective of this paper is to show how analyzing open data may help verify politicians' arguments. In this paper we considered the polem-ical 2019 Bolivian general elections as case study. We used open access electoral data and statistical tools, to assess two political arguments that aimed at explaining changes in the vote count trend, namely the arrival of rural votes, and electoral fraud. This study highlights and discusses the importance of open data access and the involvement of science in assessing political arguments