Interactive knowledge discovery and data mining on genomic expression data with numeric formal concept analysis

Background: Gene Expression Data (GED) analysis poses a great challenge to the scientific community that can be framed into the Knowledge Discovery in Databases (KDD) and Data Mining (DM) paradigm. Biclustering has emerged as the machine learning method of choice to solve this task, but its unsupervised nature makes result assessment problematic. This is often addressed by means of Gene Set Enrichment Analysis (GSEA). Results: We put forward a framework in which GED analysis is understood as an Exploratory Data Analysis (EDA) process where we provide support for continuous human interaction with data aiming at improving the step of hypothesis abduction and assessment. We focus on the adaptation to human cognition of data interpretation and visualization of the output of EDA. First, we give a proper theoretical background to bi-clustering using Lattice Theory and provide a set of analysis tools revolving around K-Formal Concept Analysis (K-FCA), a lattice-theoretic unsupervised learning technique for real-valued matrices. By using different kinds of cost structures to quantify expression we obtain different sequences of hierarchical bi-clusterings for gene under- and over-expression using thresholds. Consequently, we provide a method with interleaved analysis steps and visualization devices so that the sequences of lattices for a particular experiment summarize the researcher’s vision of the data. This also allows us to define measures of persistence and robustness of biclusters to assess them. Second, the resulting biclusters are used to index external omics databases—for instance, Gene Ontology (GO)—thus offering a new way of accessing publicly available resources. This provides different flavors of gene set enrichment against which to assess the biclusters, by obtaining their p-values according to the terminology of those resources. We illustrate the exploration procedure on a real data example confirming results previously published. Conclusions: The GED analysis problem gets transformed into the exploration of a sequence of lattices enabling the visualization of the hierarchical structure of the biclusters with a certain degree of granularity. The ability of FCA-based bi-clustering methods to index external databases such as GO allows us to obtain a quality measure of the biclusters, to observe the evolution of a gene throughout the different biclusters it appears in, to look for relevant biclusters—by observing their genes and what their persistence is—to infer, for instance, hypotheses on their function

Formal Concept Analysis Applications in Bioinformatics

Bioinformatics is an important field that seeks to solve biological problems with the help of computation. One specific field in bioinformatics is that of genomics, the study of genes and their functions. Genomics can provide valuable analysis as to the interaction between how genes interact with their environment. One such way to measure the interaction is through gene expression data, which determines whether (and how much) a certain gene activates in a situation. Analyzing this data can be critical for predicting diseases or other biological reactions. One method used for analysis is Formal Concept Analysis (FCA), a computing technique based in partial orders that allows the user to examine the structural properties of binary data based on which subsets of the data set depend on each other. This thesis surveys, in breadth and depth, the current literature related to the use of FCA for bioinformatics, with particular focus on gene expression data. This includes descriptions of current data management techniques specific to FCA, such as lattice reduction, discretization, and variations of FCA to account for different data types. Advantages and shortcomings of using FCA for genomic investigations, as well as the feasibility of using FCA for this application are addressed. Finally, several areas for future doctoral research are proposed. Adviser: Jitender S. Deogu

Fouille de données complexes et biclustering avec l'analyse formelle de concepts

Knowledge discovery in database (KDD) is a process which is applied to possibly large volumes of data for discovering patterns which can be significant and useful. In this thesis, we are interested in data transformation and data mining in knowledge discovery applied to complex data, and we present several experiments related to different approaches and different data types.The first part of this thesis focuses on the task of biclustering using formal concept analysis (FCA) and pattern structures. FCA is naturally related to biclustering, where the objective is to simultaneously group rows and columns which verify some regularities. Related to FCA, pattern structures are its generalizations which work on more complex data. Partition pattern structures were proposed to discover constant-column biclustering, while interval pattern structures were studied in similar-column biclustering. Here we extend these approaches to enumerate other types of biclusters: additive, multiplicative, order-preserving, and coherent-sign-changes.The second part of this thesis focuses on two experiments in mining complex data. First, we present a contribution related to the CrossCult project, where we analyze a dataset of visitor trajectories in a museum. We apply sequence clustering and FCA-based sequential pattern mining to discover patterns in the dataset and to classify these trajectories. This analysis can be used within CrossCult project to build recommendation systems for future visitors. Second, we present our work related to the task of antibacterial drug discovery. The dataset for this task is generally a numerical matrix with molecules as rows and features/attributes as columns. The huge number of features makes it more complex for any classifier to perform molecule classification. Here we study a feature selection approach based on log-linear analysis which discovers associations among features.As a synthesis, this thesis presents a series of different experiments in the mining of complex real-world data.L'extraction de connaissances dans les bases de données (ECBD) est un processus qui s'applique à de (potentiellement larges) volumes de données pour découvrir des motifs qui peuvent être signifiants et utiles. Dans cette thèse, on s'intéresse à deux étapes du processus d'ECBD, la transformation et la fouille, que nous appliquons à des données complexes. Nous présentons de nombreuses expérimentations s'appuyant sur des approches et des types de données variés.La première partie de cette thèse s'intéresse à la tâche de biclustering en s'appuyant sur l'analyse formelle de concepts (FCA) et aux pattern structures. FCA est naturellement liées au biclustering, dont l'objectif consiste à grouper simultanément un ensemble de lignes et de colonnes qui vérifient certaines régularités. Les pattern structures sont une généralisation de la FCA qui permet de travailler avec des données plus complexes. Les "partition pattern structures'' ont été proposées pour du biclustering à colonnes constantes tandis que les "interval pattern structures'' ont été étudiées pour du biclustering à colonnes similaires. Nous proposons ici d'étendre ces approches afin d'énumérer d'autres types de biclusters : additif, multiplicatif, préservant l'ordre, et changement de signes cohérents.Dans la seconde partie, nous nous intéressons à deux expériences de fouille de données complexes. Premièrement, nous présentons une contribution dans la quelle nous analysons les trajectoires des visiteurs d'un musée dans le cadre du projet CrossCult. Nous utilisons du clustering de séquences et de la fouille de motifs séquentiels basée sur l'analyse formelle de concepts pour découvrir des motifs dans les données et classifier les trajectoires. Cette analyse peut ensuite être exploitée par un système de recommandation pour les futurs visiteurs. Deuxièmement, nous présentons un travail sur la découverte de médicaments antibactériens. Les jeux de données pour cette tâche, généralement des matrices numériques, décrivent des molécules par un certain nombre de variables/attributs. Le grand nombre de variables complexifie la classification des molécules par les classifieurs. Ici, nous étudions une approche de sélection de variables basée sur l'analyse log-linéaire qui découvre des associations entre variables.En somme, cette thèse présente différentes expériences de fouille de données réelles et complexes

User-Specific Bicluster-based Collaborative Filtering

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020Collaborative Filtering is one of the most popular and successful approaches for Recommender Systems. However, some challenges limit the effectiveness of Collaborative Filtering approaches when dealing with recommendation data, mainly due to the vast amounts of data and their sparse nature. In order to improve the scalability and performance of Collaborative Filtering approaches, several authors proposed successful approaches combining Collaborative Filtering with clustering techniques. In this work, we study the effectiveness of biclustering, an advanced clustering technique that groups rows and columns simultaneously, in Collaborative Filtering. When applied to the classic U-I interaction matrices, biclustering considers the duality relations between users and items, creating clusters of users who are similar under a particular group of items. We propose USBCF, a novel biclustering-based Collaborative Filtering approach that creates user specific models to improve the scalability of traditional CF approaches. Using a realworld dataset, we conduct a set of experiments to objectively evaluate the performance of the proposed approach, comparing it against baseline and state-of-the-art Collaborative Filtering methods. Our results show that the proposed approach can successfully suppress the main limitation of the previously proposed state-of-the-art biclustering-based Collaborative Filtering (BBCF) since BBCF can only output predictions for a small subset of the system users and item (lack of coverage). Moreover, USBCF produces rating predictions with quality comparable to the state-of-the-art approaches

Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2014)

International audienceThis is the third edition of the FCA4AI workshop, whose first edition was organized at ECAI 2012 Conference (Montpellier, August 2012) and second edition was organized at IJCAI 2013 Conference (Beijing, August 2013, see http://www.fca4ai.hse.ru/). Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification that can be used for many purposes, especially for Artificial Intelligence (AI) needs. The objective of the workshop is to investigate two main main issues: how can FCA support various AI activities (knowledge discovery, knowledge representation and reasoning, learning, data mining, NLP, information retrieval), and how can FCA be extended in order to help AI researchers to solve new and complex problems in their domain

Workshop Notes of the Sixth International Workshop "What can FCA do for Artificial Intelligence?"

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Société Francophone de Classification (SFC) Actes des 26èmes Rencontres

National audienceLes actes des rencontres de la Société Francophone de Classification (SFC, http://www.sfc-classification.net/) contiennent l'ensemble des contributions,présentés lors des rencontres entre les 3 et 5 septembre 2019 au Centre de Recherche Inria Nancy Grand Est/LORIA Nancy. La classification sous toutes ces formes, mathématiques, informatique (apprentissage, fouille de données et découverte de connaissances ...), et statistiques, est la thématique étudiée lors de ces journées. L'idée est d'illustrer les différentes facettes de la classification qui reflètent les intérêts des chercheurs dans la matière, provenant des mathématiques et de l'informatique