Both chordal and weakly chordal graphs have been topics of research in graph theory for many years. Upon reading their definitions it is clear that the weakly chordal class of graphs is a relaxation of the chordal condition for graphs. The question is then asked could we possibly find and study the properties if we, in turn, relaxed the weakly chordal condition for graphs? We start by providing the definitions and basic results needed later on. In the second chapter, we discuss perfect graphs, some of their properties, and some subclasses that were researched. The third chapter is focused on a new class of graphs, the definition of which relaxes the restrictions for chordal and weakly chordal graphs, and extends certain results from weakly chordal graphs to this class.

In 1990, Fröberg presented a combinatorial classification of the quadratic square-free monomial ideals with linear resolutions. He showed that the edge ideal of a graph has a linear resolution if and only if the complement of the graph is chordal. Since then, a generalization of Fröberg's theorem to higher dimensions has been sought in order to classify all square-free monomial ideals with linear resolutions. Such a characterization would also give a description of all square-free monomial ideals which are Cohen-Macaulay. In this thesis we explore one method of extending Fröberg's result. We generalize the idea of a chordal graph to simplicial complexes and use simplicial homology as a bridge between this combinatorial notion and the algebraic concept of a linear resolution. We are able to give a generalization of one direction of Fröberg's theorem and, in investigating the converse direction, find a necessary and sufficient combinatorial condition for a square-free monomial ideal to have a linear resolution over fields of characteristic 2.

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Nous avons utilisé la décomposition par séparateurs minimaux complets. Pour décomposer un graphe G, il est nécessaire de trouver les séparateurs minimaux dans le graphe triangulé H correspondant. Dans ce contexte, nos premiers efforts se sont tournés vers la détection de séparateurs minimaux dans un graphe triangulé. Nous avons défini une structure, que nous avons nommée 'atom tree'. Cette dernière est inspirée du 'clique tree' et permet d'obtenir et de représenter les atomes qui sont les produits de la décomposition. Lors de la manipulation de données à l'aide de treillis de Galois, nous avons remarqué que la décomposition par séparateurs minimaux permettait une approche de type `Diviser pour régner' pour les treillis de Galois. La détection des gènes fusionnés, qui est une étape importante pour la compréhension de l'évolution des espèces, nous a permis d'appliquer nos algorithmes de détection de séparateurs minimaux complets, qui nous a permis de détecter et regrouper de manière efficace les gènes fusionnés. Une autre application biologique fut la détection de familles de gènes d'intérêts à partir de données de niveaux d'expression de gènes. La structure de `l'atom tree' nous a permis d'avoir un bon outils de visualisation et de gérer des volumes de données importantes. / We worked on clique minimal separator decomposition. In order to compute this decomposition on a graph G we need to compute the minimal separators of its triangulation H. In this context, the first efforts were on finding a clique minimal separators in a chordal graph. We defined a structure called atom tree inspired from the clique tree to compute and represent the final products of the decomposition, called atoms. The purpose of this thesis was to apply this technique on biological data. While we were manipulating this data using Galois lattices, we noticed that the clique minimal separator decomposition allows a divide and conquer approach on Galois lattices. One biological application of this thesis was the detection of fused genes which are important evolutionary events. Using algorithms we produced in the course of along our work we implemented a program called MosaicFinder that allows an efficient detection of this fusion event and their pooling. Another biological application was the extraction of genes of interest using expression level data. The atom tree structure allowed us to have a good visualization of the data and to be able to compute large datasets.