Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 2010

La programmation par contraintes permet de modéliser des problèmes et offre des méthodes de résolution efficaces. Cependant, sa complexité augmentant ces dernières années, son utilisation, notamment pour modéliser des problèmes, est devenue limitée à des utilisateurs possédant une bonne expérience dans le domaine. Cette thèse s’inscrit dans un cadre visant à automatiser la modélisation. Les techniques existantes ont montré des résultats encourageants mais certaines exigences rendent leur utilisation encore problématique. Dans une première partie, nous proposons de dépasser une limite existante qui réside dans la nécessité pour l’utilisateur de fournir des solutions du problème qu’il veut modéliser. En remplacement, il nous fournit des solutions de problèmes proches, c’est-à-dire de problèmes dont la sémantique de fond est la même mais dont les variables et leur domaine peuvent changer. Pour exploiter de telles données, nous proposons d’acquérir, grâce à des techniques de programmation logique inductive, un modèle plus abstrait que le réseau de contraintes. Une fois appris, ce modèle est ensuite transformé pour correspondre au problème initial que souhaitait résoudre l’utilisateur. Nous montrons également que la phase d’apprentissage se heurte à des limites pathologiques et qui nous ont contraints à développer un nouvel algorithme pour synthétiser ces modèles abstraits. Dans une seconde partie, nous nous intéressons à la possibilité pour l’utilisateur de ne pas donner d’exemples du tout. En partant d’un CSP sans aucune contrainte, notre méthode consiste à résoudre le problème de l’utilisateur de manière classique. Grâce à un arbre de recherche, nous affectons progressivement des valeurs aux variables. Quand notre outil ne peut décider si l’affectation partielle courante est correcte ou non, nous demandons à l’utilisateur de guider la recherche sous forme de requêtes. Ces requêtes permettent de trouver des contraintes à ajouter aux modèles du CSP et ainsi améliorer la recherche. / Constraint programming allows to model many kind of problems with efficient solving methods. However, its complexity has increased these last years and its use, notably to model problems, has become limited to people with a fair expertise in the domain. This thesis deals with automating the modeling task in constraint programming. Methods already exist, with encouraging results, but many requirements are debatable. In a first part, we propose to avoid the limitation consisting, for the user, in providing solutions of the problem she aims to solve. As a replacement of these solutions, the user has to provide solutions of closed problem, i.e problem with same semantic but where variables and domains can be different. To handle this kind of data, we acquire, thanks to inductive logic programming, a more abstract model than the constraint network. When this model is learned, it is translated in the very constraint network the user aims to model. We show the limitations of learning method to build such a model due to pathological problems and explain the new algorithm we have developed to build these abstract models. In a second part, we are interesting in the possibility to the user to not provide any examples. Starting with a CSP without constraints, our method consists in solving the problem the user wants in a standard way. Thanks to a search tree, we affect to each variable a value. When our tool cannot decide if the current partial affectation is correct or not, we ask to the user, with yes/no queries, to guide the search. These queries allow to find constraints to add to the model and then to improve the quality of the search.

In the past decade, fast advancements have been made in the sequencing, digitalization and collection of the biological data. However the bottleneck remains at the point of analysis and extraction of patterns from the data. We have developed a method that is aimed at widening this bottleneck by automating the knowledge extraction from the biological data. Our approach is aimed at discovering patterns in a set of DNA sequences based on the location of transcription factor binding sites or any other biological markers with the emphasis of discovering relationships. A variety of statistical and computational methods exists to analyze such data. However, they either require an initial hypothesis, which is later tested, or classify the data based on its attributes. Our approach does not require an initial hypothesis and the classification it produces is based on the relationships between attributes. The value of such approach is that is is able to uncover new knowledge about the data by inducing a general theory based on basic known rules. The core of our approach lies in an inductive logic programming engine, which, based on positive and negative examples as well as background knowledge, is able to induce a descriptive, human-readable theory, describing the data. An application provides an end-to-end analysis of DNA sequences. A simple to use Web interface accepts a set of related sequences to be analyzed, set of negative example sequences to contrast the main set (optional), and a set of possible genetic markers as position-specific scoring matrices. A Java-based backend formats the sequences, determines the location of the genetic markers inside them and passes the information to the ILP engine, which induces the theory. The model, assumed in our background knowledge, is a set of basic interactions between biological markers in any DNA sequence. This makes our approach applicable to analyze a wide variety of biological problems, including detection of cis-regulatory modules and analysis of ChIP-Sequencing experiments. We have evaluated our method in the context of such applications on two real world datasets as well as a number of specially designed synthetic datasets. The approach has shown to have merit even in situations when no significant classification could be determined.

In the past decade, fast advancements have been made in the sequencing, digitalization and collection of the biological data. However the bottleneck remains at the point of analysis and extraction of patterns from the data. We have developed a method that is aimed at widening this bottleneck by automating the knowledge extraction from the biological data. Our approach is aimed at discovering patterns in a set of DNA sequences based on the location of transcription factor binding sites or any other biological markers with the emphasis of discovering relationships. A variety of statistical and computational methods exists to analyze such data. However, they either require an initial hypothesis, which is later tested, or classify the data based on its attributes. Our approach does not require an initial hypothesis and the classification it produces is based on the relationships between attributes. The value of such approach is that is is able to uncover new knowledge about the data by inducing a general theory based on basic known rules. The core of our approach lies in an inductive logic programming engine, which, based on positive and negative examples as well as background knowledge, is able to induce a descriptive, human-readable theory, describing the data. An application provides an end-to-end analysis of DNA sequences. A simple to use Web interface accepts a set of related sequences to be analyzed, set of negative example sequences to contrast the main set (optional), and a set of possible genetic markers as position-specific scoring matrices. A Java-based backend formats the sequences, determines the location of the genetic markers inside them and passes the information to the ILP engine, which induces the theory. The model, assumed in our background knowledge, is a set of basic interactions between biological markers in any DNA sequence. This makes our approach applicable to analyze a wide variety of biological problems, including detection of cis-regulatory modules and analysis of ChIP-Sequencing experiments. We have evaluated our method in the context of such applications on two real world datasets as well as a number of specially designed synthetic datasets. The approach has shown to have merit even in situations when no significant classification could be determined.

In the past decade, fast advancements have been made in the sequencing, digitalization and collection of the biological data. However the bottleneck remains at the point of analysis and extraction of patterns from the data. We have developed a method that is aimed at widening this bottleneck by automating the knowledge extraction from the biological data. Our approach is aimed at discovering patterns in a set of DNA sequences based on the location of transcription factor binding sites or any other biological markers with the emphasis of discovering relationships. A variety of statistical and computational methods exists to analyze such data. However, they either require an initial hypothesis, which is later tested, or classify the data based on its attributes. Our approach does not require an initial hypothesis and the classification it produces is based on the relationships between attributes. The value of such approach is that is is able to uncover new knowledge about the data by inducing a general theory based on basic known rules. The core of our approach lies in an inductive logic programming engine, which, based on positive and negative examples as well as background knowledge, is able to induce a descriptive, human-readable theory, describing the data. An application provides an end-to-end analysis of DNA sequences. A simple to use Web interface accepts a set of related sequences to be analyzed, set of negative example sequences to contrast the main set (optional), and a set of possible genetic markers as position-specific scoring matrices. A Java-based backend formats the sequences, determines the location of the genetic markers inside them and passes the information to the ILP engine, which induces the theory. The model, assumed in our background knowledge, is a set of basic interactions between biological markers in any DNA sequence. This makes our approach applicable to analyze a wide variety of biological problems, including detection of cis-regulatory modules and analysis of ChIP-Sequencing experiments. We have evaluated our method in the context of such applications on two real world datasets as well as a number of specially designed synthetic datasets. The approach has shown to have merit even in situations when no significant classification could be determined.

Σε αυτήν την εργασία μελετάται ο Επαγωγικός Λογικός Προγραμματισμός μέσα απο το πρίσμα της μάθησης της Γραμματικής της αγγλικής γλώσσας. Ο λόγος που επιλέχθηκε αυτό το πρόβλημα είναι ότι εξομοιώνει, μέχρι ενός σγημείου, τον τρόπο που τα παιδιά μαθαίνουν να μιλούν κάποια γλώσσα, υπό την έννοια ότι μαθαίνουν να μιλάνε χωρίς να έρθουν σε επαφή με τους κανόνες – τη γραμματική – της γλώσσας, αλλά από την επαφή τους με τα με τα ερεθίσματα – τα παραδείγματα – που έχουν από τον περίγυρό τους. / This paper is about Inductive Logic Programming through the prism of a problem. In our case Grammar Learning.

In the past decade, fast advancements have been made in the sequencing, digitalization and collection of the biological data. However the bottleneck remains at the point of analysis and extraction of patterns from the data. We have developed a method that is aimed at widening this bottleneck by automating the knowledge extraction from the biological data. Our approach is aimed at discovering patterns in a set of DNA sequences based on the location of transcription factor binding sites or any other biological markers with the emphasis of discovering relationships. A variety of statistical and computational methods exists to analyze such data. However, they either require an initial hypothesis, which is later tested, or classify the data based on its attributes. Our approach does not require an initial hypothesis and the classification it produces is based on the relationships between attributes. The value of such approach is that is is able to uncover new knowledge about the data by inducing a general theory based on basic known rules. The core of our approach lies in an inductive logic programming engine, which, based on positive and negative examples as well as background knowledge, is able to induce a descriptive, human-readable theory, describing the data. An application provides an end-to-end analysis of DNA sequences. A simple to use Web interface accepts a set of related sequences to be analyzed, set of negative example sequences to contrast the main set (optional), and a set of possible genetic markers as position-specific scoring matrices. A Java-based backend formats the sequences, determines the location of the genetic markers inside them and passes the information to the ILP engine, which induces the theory. The model, assumed in our background knowledge, is a set of basic interactions between biological markers in any DNA sequence. This makes our approach applicable to analyze a wide variety of biological problems, including detection of cis-regulatory modules and analysis of ChIP-Sequencing experiments. We have evaluated our method in the context of such applications on two real world datasets as well as a number of specially designed synthetic datasets. The approach has shown to have merit even in situations when no significant classification could be determined.

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Some upstream open reading frames (uORFs) regulate gene expression (i.e. they are functional) and can play key roles in keeping organisms healthy. However, how uORFs are involved in gene regulation is not het fully understood. In order to get a complete view of how uORFs are involved in gene regulation, it is expected that a large number of functional uORFs are needed. Unfortunately , lab experiments to verify that uORFs are functional are expensive. In this thesis, for the first time, the use of inductive logic programming (ILP) is explored for the task of learning which uORFs regulate gene expression in the yeast Saccharomyces cerevisiae. This work is directed to help select sets of candidate functional uORFs for experimental studies. With limited background knowledge, ILP can generate hypotheses which make the search for novel functional uORFs 17 times more efficient than random sampling. Adding mRNA secondary structure to the background knowledge results in hypotheses with significantly increased performance. This work is the first machine learning work to study both uORFs and mRNA secondary structures in the context of gene regulation. Using a novel combination of knowledge about biological conservation, gene ontology annotations and genes' response to different conditions results in hypotheses that are simple, informative, have an estimated sensitivity of 81% and provide provisional insights into biological characteristics of functional uORFs. The hypotheses predict 299 further genes to have 450 novel functional uORFs. A comparison with a related study suggests that 8 of these predicted functional uORFs (from 8 genes) are strong candidates for experimental studies.

Inductive logic programming is a discipline investigating invention of clausal theories from observed examples such that for given evidence and background knowledge we are finding a hypothesis covering all positive examples and excluding all negative ones. In this thesis we extend an existing work on template consistency to general consistency. We present a three-phase algorithm DeMeR decomposing the original problem into smaller subtasks, merging all subsolutions together yielding a complete solution and finally refining the result in order to get a compact final hypothesis. Furthermore, we focus on a method how each individual subtask is solved and we introduce a generate-and-test method based on the probabilistic history-driven approach for this purpose. We analyze each stage of the proposed algorithms and demonstrate its impact on a runtime and a hypothesis structure. In particular, we show that the first phase of the algorithm concentrates on solving the problem quickly at the cost of longer solutions whereas the other phases refine these solutions into an admissible form. Finally, we prove that our technique outperforms other algorithms by comparing its results for identifying common structures in random graphs to existing systems.