Using fuzzy methods for rule extraction in the discrimination of class C GPCR subtypes from their subsequences

G-Protein-Coupled receptors (GPCR) are cell membrane proteins that regulate many of the cell functions and transduce signals between the intracellular and extracellular domains. This makes them relevant in pharmacology as therapeutic targets. As members of this superfamily, class C GPCRs in particular regulate a number of important physiological functions. Proteins of the class must be studied from their primary sequences, as only one of their 3-D structures has been fully determined, earlier this year. Protein function investigation requires the identification of motifs, or functional subsequences. In this thesis, we will describe the discrimination of class C GPCR subtypes through interpretable rules from a specific alignment free transformation of the sequences, namely amino acid composition. The Fuzzy Inductive Reasoning methodology was used as the basis to extract these linguistic rules

Data Mining Techniques in Gene Expression Data Analysis

Ph.DDOCTOR OF PHILOSOPH

Fouille de graphes pour le suivi d’objets dans les vidéos

Detecting and following the main objects of a video is necessary to describe its content in order to, for example, allow for a relevant indexation of the multimedia content by the search engines. Current object tracking approaches either require the user to select the targets to follow, or rely on pre-trained classifiers to detect particular classes of objects such as pedestrians or car for example. Since those methods rely on user intervention or prior knowledge of the content to process, they cannot be applied automatically on amateur videos such as the ones found on YouTube. To solve this problem, we build upon the hypothesis that, in videos with a moving background, the main objects should appear more frequently than the background. Moreover, in a video, the topology of the visual elements composing an object is supposed consistent from one frame to another. We represent each image of the videos with plane graphs modeling their topology. Then, we search for substructures appearing frequently in the database of plane graphs thus created to represent each video. Our contributions cover both fields of graph mining and object tracking. In the first field, our first contribution is to present an efficient plane graph mining algorithm, named PLAGRAM. This algorithm exploits the planarity of the graphs and a new strategy to extend the patterns. The next contributions consist in the introduction of spatio-temporal constraints into the mining process to exploit the fact that, in a video, the motion of objects is small from on frame to another. Thus, we constrain the occurrences of a same pattern to be close in space and time by limiting the number of frames and the spatial distance separating them. We present two new algorithms, DYPLAGRAM which makes use of the temporal constraint to limit the number of extracted patterns, and DYPLAGRAM_ST which efficiently mines frequent spatio-temporal patterns from the datasets representing the videos. In the field of object tracking, our contributions consist in two approaches using the spatio-temporal patterns to track the main objects in videos. The first one is based on a search of the shortest path in a graph connecting the spatio-temporal patterns, while the second one uses a clustering approach to regroup them in order to follow the objects for a longer period of time. We also present two industrial applications of our methodDétecter et suivre les objets principaux d’une vidéo est une étape nécessaire en vue d’en décrire le contenu pour, par exemple, permettre une indexation judicieuse des données multimédia par les moteurs de recherche. Les techniques de suivi d’objets actuelles souffrent de défauts majeurs. En effet, soit elles nécessitent que l’utilisateur désigne la cible a suivre, soit il est nécessaire d’utiliser un classifieur pré-entraîné à reconnaitre une classe spécifique d’objets, comme des humains ou des voitures. Puisque ces méthodes requièrent l’intervention de l’utilisateur ou une connaissance a priori du contenu traité, elles ne sont pas suffisamment génériques pour être appliquées aux vidéos amateurs telles qu’on peut en trouver sur YouTube. Pour résoudre ce problème, nous partons de l’hypothèse que, dans le cas de vidéos dont l’arrière-plan n’est pas fixe, celui-ci apparait moins souvent que les objets intéressants. De plus, dans une vidéo, la topologie des différents éléments visuels composant un objet est supposée consistante d’une image a l’autre. Nous représentons chaque image par un graphe plan modélisant sa topologie. Ensuite, nous recherchons des motifs apparaissant fréquemment dans la base de données de graphes plans ainsi créée pour représenter chaque vidéo. Cette approche nous permet de détecter et suivre les objets principaux d’une vidéo de manière non supervisée en nous basant uniquement sur la fréquence des motifs. Nos contributions sont donc réparties entre les domaines de la fouille de graphes et du suivi d’objets. Dans le premier domaine, notre première contribution est de présenter un algorithme de fouille de graphes plans efficace, appelé PLAGRAM. Cet algorithme exploite la planarité des graphes et une nouvelle stratégie d’extension des motifs. Nous introduisons ensuite des contraintes spatio-temporelles au processus de fouille afin d’exploiter le fait que, dans une vidéo, les objets se déplacent peu d’une image a l’autre. Ainsi, nous contraignons les occurrences d’un même motif a être proches dans l’espace et dans le temps en limitant le nombre d’images et la distance spatiale les séparant. Nous présentons deux nouveaux algorithmes, DYPLAGRAM qui utilise la contrainte temporelle pour limiter le nombre de motifs extraits, et DYPLAGRAM_ST qui extrait efficacement des motifs spatio-temporels fréquents depuis les bases de données représentant les vidéos. Dans le domaine du suivi d’objets, nos contributions consistent en deux approches utilisant les motifs spatio-temporels pour suivre les objets principaux dans les vidéos. La première est basée sur une recherche du chemin de poids minimum dans un graphe connectant les motifs spatio-temporels tandis que l’autre est basée sur une méthode de clustering permettant de regrouper les motifs pour suivre les objets plus longtemps. Nous présentons aussi deux applications industrielles de notre méthod

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Learning Social Links and Communities from Interaction, Topical, and Spatio-Temporal Information

The immense popularity of today's social networks has lead to the availability and accessibility of vast amounts of data created by users on a daily basis. Various types of information can be extracted from such data, for example, interactions among users, topics of user postings, and geographic locations of users. While most of the existing works on social network analysis, in particular those focusing on social links and communities, rely on explicit and static link structures among users, extracting knowledge from exploiting more features embedded in user-generated data is another important direction that only recently has gained more attention. Initial studies employing this approach show good results in terms of a better understanding latent interactions among users. In the context of this dissertation, multiple features embedded in user-generated data are investigated to develop new models and algorithms for (1) revealing hidden social links between users and (2) extracting and analyzing dynamic feature-based communities in social networks. We introduce two approaches for extracting and measuring interpretable and meaningful social links between users. One is based on the participation of users in threads of discussions. The other one relies on the social characteristics of users as reflected in their postings. A novel probabilistic model called rLinkTopic is developed to address the problem of extracting a new type of feature-based community called regional LinkTopic: a community of users that are geographically close to each other over time, have common interests indicated by the topical similarity of their postings, and are contextually linked to each other. Based on the rLinkTopic model, a comprehensive framework called ErLinkTopic is developed that allows to extract and capture complex changes in the features describing regional LinkTopic communities, for example, the community membership of users and topics of communities. Our framework provides a novel basis for important studies such as exploring social characteristics of users in geographic regions and predicting the evolution of user communities. For each approach developed in this dissertation, extensive comparative experiments are conducted using data from real-world social networks to validate the proposed models and algorithms in terms of effectiveness and efficiency. The experimental results are further discussed in detail to show improvements over existing approaches and the applicability and advantages of our models in terms of learning social links and communities from user-generated data

2011-2012 Louisiana Tech University Catalog

The Louisiana Tech University Catalog includes announcements and course descriptions for courses offered at Louisiana Tech University for the academic year of 2011-2012.https://digitalcommons.latech.edu/university-catalogs/1004/thumbnail.jp