Graph theoretical approach to study eQTL: a case study of Plasmodium falciparum

Motivation: Analysis of expression quantitative trait loci (eQTL) significantly contributes to the determination of gene regulation programs. However, the discovery and analysis of associations of gene expression levels and their underlying sequence polymorphisms continue to pose many challenges. Methods are limited in their ability to illuminate the full structure of the eQTL data. Most rely on an exhaustive, genome scale search that considers all possible locus–gene pairs and tests the linkage between each locus and gene

Conceptual Based Hidden Data Analytics and Reduction Method for System Interface Enhancement Through Handheld devices

With the increasing demand placed on online systems by users, many organizations and companies are seeking to enhance their online interfaces to facilitate the search process on their hidden databases. Usually, users issue queries to a hidden database by using the search template provided by the system. In this thesis, a new approach based mainly on hidden database reduction preserving functional dependencies is developed for enhancing the online system interface through a small screen device. The developed approach is applied to online market systems like eBay. Offline hidden data analysis is used to discover attributes and their domains and different functional dependencies. In this thesis, a comparative study between several methods for mining functional dependencies shows the advantage of conceptual methods for data reduction. In addition, by using online consecutive reductions on search results, we adopted a method of displaying results in order of decreasing relevance. The validation of the proposed designed and developed methods prove their generality and suitability for system interfacing through continuous data reductions.NPRP-07-794-1-145 grant from the Qatar National Research Fund (a member of Qatar foundation

Outlier detection using flexible categorisation and interrogative agendas

Categorization is one of the basic tasks in machine learning and data analysis. Building on formal concept analysis (FCA), the starting point of the present work is that different ways to categorize a given set of objects exist, which depend on the choice of the sets of features used to classify them, and different such sets of features may yield better or worse categorizations, relative to the task at hand. In their turn, the (a priori) choice of a particular set of features over another might be subjective and express a certain epistemic stance (e.g. interests, relevance, preferences) of an agent or a group of agents, namely, their interrogative agenda. In the present paper, we represent interrogative agendas as sets of features, and explore and compare different ways to categorize objects w.r.t. different sets of features (agendas). We first develop a simple unsupervised FCA-based algorithm for outlier detection which uses categorizations arising from different agendas. We then present a supervised meta-learning algorithm to learn suitable (fuzzy) agendas for categorization as sets of features with different weights or masses. We combine this meta-learning algorithm with the unsupervised outlier detection algorithm to obtain a supervised outlier detection algorithm. We show that these algorithms perform at par with commonly used algorithms for outlier detection on commonly used datasets in outlier detection. These algorithms provide both local and global explanations of their results

Extraction de taxonomie par regroupement hiérarchique de plongements vectoriels de graphes de connaissances

RÉSUMÉ: Les graphes de connaissances jouent aujourd’hui un rôle important pour représenter et stocker des données, bien au-delà du Web sémantique ; beaucoup d’entre eux sont obtenus de manière automatique ou collaborative, et agrègent des données issues de sources diverses. Dans ces conditions, la création et la mise à jour automatique d’une taxonomie qui reflète le contenu d’un graphe est un enjeu crucial.Or, la plupart des méthodes d’extraction taxonomique adaptées aux graphes de grande taille se contentent de hiérarchiser des classes pré-existantes, et sont incapables d’identifier de nouvelles classes à partir des données. Dans ce mémoire, nous proposons une méthode d’extraction de taxonomie expressive applicable à grande échelle, grâce à l’utilisation de plongements vectoriels. Les modèles de plongement vectoriel de graphe fournissent une représentation vectorielle dense des éléments d’un graphe, qui intègre sous forme géométrique les régularités des données : ainsi, deux éléments sémantiquement proches dans le graphe auront des plongements vectoriels géométriquement proches.Notre but est de démontrer le potentiel du regroupement hiérarchique non-supervisé appliqué aux plongements vectoriels sur la tâche d’extraction de taxonomie. Pour cela, nous procédons en deux étapes : nous montrons d’abord qu’un tel regroupement est capable d’extraire une taxonomie sur les classes existantes, puis qu’il permet de surcroît d’identifier de nouvelles classes et de les organiser hiérarchiquement, c’est-à-dire d’extraire une taxonomie expressive.----------ABSTRACT: Knowledge graphs are the backbone of the Semantic Web, and have been succesfully applied to a wide range of areas. Many of these graphs are built automatically or collaboratively,and aggregate data from various sources. In these conditions, automatically creating and updating a taxonomy that accurately reflects the content of a graph is an important issue. However, among scalable taxonomy extraction approaches, most of them can only extract a hierarchy on existing classes, and are unable to identify new classes from the data. In this thesis, we propose a novel taxonomy extraction method based on knowledge graph embeddings that is both scalable and expressive. A knowledge graph embedding model provides a dense, low-dimensional vector representation of the entities of a graph, such that similar entities in the graph are embedded close to each other in the embedding space.Our goal is to show how these graph embeddings can be combined with unsupervised hierarchical clustering to extract a taxonomy from a graph. We first show that unsupervised clustering is able to extract a taxonomy on existing classes. Then, we show that it can also be used to identify new classes and organize them hierarchically, thus creating an expressive taxonom