MMRF for proteome annotation applied to human protein disease prediction

Proceedings of: 20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010Biological processes where every gene and protein participates is an essential knowledge for designing disease treatments. Nowadays, these annotations are still unknown for many genes and proteins. Since making annotations from in-vivo experiments is costly, computational predictors are needed for different kinds of annotation such as metabolic pathway, interaction network, protein family, tissue, disease and so on. Biological data has an intrinsic relational structure, including genes and proteins, which can be grouped by many criteria. This hinders the possibility of finding good hypotheses when attribute-value representation is used. Hence, we propose the generic Modular Multi-Relational Framework (MMRF) to predict different kinds of gene and protein annotation using Relational Data Mining (RDM). The specific MMRF application to annotate human protein with diseases verifies that group knowledge (mainly protein-protein interaction pairs) improves the prediction, particularly doubling the area under the precision-recall curvePublicad

Data Enrichment for Data Mining Applied to Bioinformatics and Cheminformatics Domains

Problemas cada vez mais complexos estão a ser tratados na àrea das ciências da vida. A aquisição de todos os dados que possam estar relacionados com o problema em questão é primordial. Igualmente importante é saber como os dados estão relacionados uns com os outros e com o próprio problema. Por outro lado, existem grandes quantidades de dados e informações disponíveis na Web. Os investigadores já estão a utilizar Data Mining e Machine Learning como ferramentas valiosas nas suas investigações, embora o procedimento habitual seja procurar a informação baseada nos modelos indutivos. Até agora, apesar dos grandes sucessos já alcançados com a utilização de Data Mining e Machine Learning, não é fácil integrar esta vasta quantidade de informação disponível no processo indutivo, com algoritmos proposicionais. A nossa principal motivação é abordar o problema da integração de informação de domínio no processo indutivo de técnicas proposicionais de Data Mining e Machine Learning, enriquecendo os dados de treino a serem utilizados em sistemas de programação de lógica indutiva. Os algoritmos proposicionais de Machine Learning são muito dependentes dos atributos dos dados. Ainda é difícil identificar quais os atributos mais adequados para uma determinada tarefa na investigação. É também difícil extrair informação relevante da enorme quantidade de dados disponíveis. Vamos concentrar os dados disponíveis, derivar características que os algoritmos de ILP podem utilizar para induzir descrições, resolvendo os problemas. Estamos a criar uma plataforma web para obter informação relevante para problemas de Bioinformática (particularmente Genómica) e Quimioinformática. Esta vai buscar os dados a repositórios públicos de dados genómicos, proteicos e químicos. Após o enriquecimento dos dados, sistemas Prolog utilizam programação lógica indutiva para induzir regras e resolver casos específicos de Bioinformática e Cheminformática. Para avaliar o impacto do enriquecimento dos dados com ILP, comparamos com os resultados obtidos na resolução dos mesmos casos utilizando algoritmos proposicionais.Increasingly more complex problems are being addressed in life sciences. Acquiring all the data that may be related to the problem in question is paramount. Equally important is to know how the data is related to each other and to the problem itself. On the other hand, there are large amounts of data and information available on the Web. Researchers are already using Data Mining and Machine Learning as a valuable tool in their researches, albeit the usual procedure is to look for the information based on induction models. So far, despite the great successes already achieved using Data Mining and Machine Learning, it is not easy to integrate this vast amount of available information in the inductive process with propositional algorithms. Our main motivation is to address the problem of integrating domain information into the inductive process of propositional Data Mining and Machine Learning techniques by enriching the training data to be used in inductive logic programming systems. The algorithms of propositional machine learning are very dependent on data attributes. It still is hard to identify which attributes are more suitable for a particular task in the research. It is also hard to extract relevant information from the enormous quantity of data available. We will concentrate the available data, derive features that ILP algorithms can use to induce descriptions, solving the problems. We are creating a web platform to obtain relevant bioinformatics (particularly Genomics) and Cheminformatics problems. It fetches the data from public repositories with genomics, protein and chemical data. After the data enrichment, Prolog systems use inductive logic programming to induce rules and solve specific Bioinformatics and Cheminformatics case studies. To assess the impact of the data enrichment with ILP, we compare with the results obtained solving the same cases using propositional algorithms

Combining learning and constraints for genome-wide protein annotation

BackgroundThe advent of high-throughput experimental techniques paved the way to genome-wide computational analysis and predictive annotation studies. When considering the joint annotation of a large set of related entities, like all proteins of a certain genome, many candidate annotations could be inconsistent, or very unlikely, given the existing knowledge. A sound predictive framework capable of accounting for this type of constraints in making predictions could substantially contribute to the quality of machine-generated annotations at a genomic scale.ResultsWe present Ocelot, a predictive pipeline which simultaneously addresses functional and interaction annotation of all proteins of a given genome. The system combines sequence-based predictors for functional and protein-protein interaction (PPI) prediction with a consistency layer enforcing (soft) constraints as fuzzy logic rules. The enforced rules represent the available prior knowledge about the classification task, including taxonomic constraints over each GO hierarchy (e.g. a protein labeled with a GO term should also be labeled with all ancestor terms) as well as rules combining interaction and function prediction. An extensive experimental evaluation on the Yeast genome shows that the integration of prior knowledge via rules substantially improves the quality of the predictions. The system largely outperforms GoFDR, the only high-ranking system at the last CAFA challenge with a readily available implementation, when GoFDR is given access to intra-genome information only (as Ocelot), and has comparable or better results (depending on the hierarchy and performance measure) when GoFDR is allowed to use information from other genomes. Our system also compares favorably to recent methods based on deep learning

Bioinformatics challenges for genome-wide association studies

Motivation: The sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype–phenotype relationship that is characterized by significant heterogeneity and gene–gene and gene–environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods

Scalable statistical learning for relation prediction on structured data

Relation prediction seeks to predict unknown but potentially true relations by revealing missing relations in available data, by predicting future events based on historical data, and by making predicted relations retrievable by query. The approach developed in this thesis can be used for a wide variety of purposes, including to predict likely new friends on social networks, attractive points of interest for an individual visiting an unfamiliar city, and associations between genes and particular diseases. In recent years, relation prediction has attracted significant interest in both research and application domains, partially due to the increasing volume of published structured data and background knowledge. In the Linked Open Data initiative of the Semantic Web, for instance, entities are uniquely identified such that the published information can be integrated into applications and services, and the rapid increase in the availability of such structured data creates excellent opportunities as well as challenges for relation prediction. This thesis focuses on the prediction of potential relations by exploiting regularities in data using statistical relational learning algorithms and applying these methods to relational knowledge bases, in particular in Linked Open Data in particular. We review representative statistical relational learning approaches, e.g., Inductive Logic Programming and Probabilistic Relational Models. While logic-based reasoning can infer and include new relations via deduction by using ontologies, machine learning can be exploited to predict new relations (with some degree of certainty) via induction, purely based on the data. Because the application of machine learning approaches to relation prediction usually requires handling large datasets, we also discuss the scalability of machine learning as a solution to relation prediction, as well as the significant challenge posed by incomplete relational data (such as social network data, which is often much more extensive for some users than others). The main contribution of this thesis is to develop a learning framework called the Statistical Unit Node Set (SUNS) and to propose a multivariate prediction approach used in the framework. We argue that multivariate prediction approaches are most suitable for dealing with large, sparse data matrices. According to the characteristics and intended application of the data, the approach can be extended in different ways. We discuss and test two extensions of the approach--kernelization and a probabilistic method of handling complex n-ary relationships--in empirical studies based on real-world data sets. Additionally, this thesis contributes to the field of relation prediction by applying the SUNS framework to various domains. We focus on three applications: 1. In social network analysis, we present a combined approach of inductive and deductive reasoning for recommending movies to users. 2. In the life sciences, we address the disease gene prioritization problem. 3. In the recommendation system, we describe and investigate the back-end of a mobile app called BOTTARI, which provides personalized location-based recommendations of restaurants.Die Beziehungsvorhersage strebt an, unbekannte aber potenziell wahre Beziehungen vorherzusagen, indem fehlende Relationen in verfügbaren Daten aufgedeckt, zukünftige Ereignisse auf der Grundlage historischer Daten prognostiziert und vorhergesagte Relationen durch Anfragen abrufbar gemacht werden. Der in dieser Arbeit entwickelte Ansatz lässt sich für eine Vielzahl von Zwecken einschließlich der Vorhersage wahrscheinlicher neuer Freunde in sozialen Netzen, der Empfehlung attraktiver Sehenswürdigkeiten für Touristen in fremden Städten und der Priorisierung möglicher Assoziationen zwischen Genen und bestimmten Krankheiten, verwenden. In den letzten Jahren hat die Beziehungsvorhersage sowohl in Forschungs- als auch in Anwendungsbereichen eine enorme Aufmerksamkeit erregt, aufgrund des Zuwachses veröffentlichter strukturierter Daten und von Hintergrundwissen. In der Linked Open Data-Initiative des Semantischen Web werden beispielsweise Entitäten eindeutig identifiziert, sodass die veröffentlichten Informationen in Anwendungen und Dienste integriert werden können. Diese rapide Erhöhung der Verfügbarkeit strukturierter Daten bietet hervorragende Gelegenheiten sowie Herausforderungen für die Beziehungsvorhersage. Diese Arbeit fokussiert sich auf die Vorhersage potenzieller Beziehungen durch Ausnutzung von Regelmäßigkeiten in Daten unter der Verwendung statistischer relationaler Lernalgorithmen und durch Einsatz dieser Methoden in relationale Wissensbasen, insbesondere in den Linked Open Daten. Wir geben einen Überblick über repräsentative statistische relationale Lernansätze, z.B. die Induktive Logikprogrammierung und Probabilistische Relationale Modelle. Während das logikbasierte Reasoning neue Beziehungen unter der Nutzung von Ontologien ableiten und diese einbeziehen kann, kann maschinelles Lernen neue Beziehungen (mit gewisser Wahrscheinlichkeit) durch Induktion ausschließlich auf der Basis der vorliegenden Daten vorhersagen. Da die Verarbeitung von massiven Datenmengen in der Regel erforderlich ist, wenn maschinelle Lernmethoden in die Beziehungsvorhersage eingesetzt werden, diskutieren wir auch die Skalierbarkeit des maschinellen Lernens sowie die erhebliche Herausforderung, die sich aus unvollständigen relationalen Daten ergibt (z. B. Daten aus sozialen Netzen, die oft für manche Benutzer wesentlich umfangreicher sind als für Anderen). Der Hauptbeitrag der vorliegenden Arbeit besteht darin, ein Lernframework namens Statistical Unit Node Set (SUNS) zu entwickeln und einen im Framework angewendeten multivariaten Prädiktionsansatz einzubringen. Wir argumentieren, dass multivariate Vorhersageansätze am besten für die Bearbeitung von großen und dünnbesetzten Datenmatrizen geeignet sind. Je nach den Eigenschaften und der beabsichtigten Anwendung der Daten kann der Ansatz auf verschiedene Weise erweitert werden. In empirischen Studien werden zwei Erweiterungen des Ansatzes--ein kernelisierter Ansatz sowie ein probabilistischer Ansatz zur Behandlung komplexer n-stelliger Beziehungen-- diskutiert und auf realen Datensätzen untersucht. Ein weiterer Beitrag dieser Arbeit ist die Anwendung des SUNS Frameworks auf verschiedene Bereiche. Wir konzentrieren uns auf drei Anwendungen: 1. In der Analyse sozialer Netze stellen wir einen kombinierten Ansatz von induktivem und deduktivem Reasoning vor, um Benutzern Filme zu empfehlen. 2. In den Biowissenschaften befassen wir uns mit dem Problem der Priorisierung von Krankheitsgenen. 3. In den Empfehlungssystemen beschreiben und untersuchen wir das Backend einer mobilen App "BOTTARI", das personalisierte ortsbezogene Empfehlungen von Restaurants bietet

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998