Novel Algorithms for Cross-Ontology Multi-Level Data Mining

The wide spread use of ontologies in many scientific areas creates a wealth of ontologyannotated data and necessitates the development of ontology-based data mining algorithms. We have developed generalization and mining algorithms for discovering cross-ontology relationships via ontology-based data mining. We present new interestingness measures to evaluate the discovered cross-ontology relationships. The methods presented in this dissertation employ generalization as an ontology traversal technique for the discovery of interesting and informative relationships at multiple levels of abstraction between concepts from different ontologies. The generalization algorithms combine ontological annotations with the structure and semantics of the ontologies themselves to discover interesting crossontology relationships. The first algorithm uses the depth of ontological concepts as a guide for generalization. The ontology annotations are translated to higher levels of abstraction one level at a time accompanied by incremental association rule mining. The second algorithm conducts a generalization of ontology terms to all their ancestors via transitive ontology relations and then mines cross-ontology multi-level association rules from the generalized transactions. Our interestingness measures use implicit knowledge conveyed by the relation semantics of the ontologies to capture the usefulness of cross-ontology relationships. We describe the use of information theoretic metrics to capture the interestingness of cross-ontology relationships and the specificity of ontology terms with respect to an annotation dataset. Our generalization and data mining agorithms are applied to the Gene Ontology and the postnatal Mouse Anatomy Ontology. The results presented in this work demonstrate that our generalization algorithms and interestingness measures discover more interesting and better quality relationships than approaches that do not use generalization. Our algorithms can be used by researchers and ontology developers to discover inter-ontology connections. Additionally, the cross-ontology relationships discovered using our algorithms can be used by researchers to understand different aspects of entities that interest them

Service-oriented discovery of knowledge : foundations, implementations and applications

In this thesis we will investigate how a popular new way of distributed computing called service orientation can be used within the field of Knowledge Discovery. We critically investigate its principles and present models for developing withing this paradigm. We then apply this model to create a web service caled Fantom, that mines subgroups in a ranked list of identifiers, based on their score. The descriptions of these subgroups are done in ontologies to provide the scientist a description in a standardized and familiar language. Finally, Fantom is tested on two different data sets from the field of life-sciences; one concerning gene data, the other concerning SNP data.LEI Universiteit LeidenAlgorithm

Sibios as a Framework for Biomarker Discovery Using Microarray Data

Submitted to the Faculty of the School of Informatics in parial fulfillment of the requirements for the degree of Master of Schience in Bioinformatics Indiana University August 2006Decoding the human genome resulted in generating large amount of data that need to be analyzed and given a biological meaning. The field of Life Schiences is highly information driven. The genomic data are mainly the gene expression data that are obtained from measurement of mRNA levels in an organism. Efficiently processing large amount of gene expression data has been possible with the help of high throughput technology. Research studies working on microarray data has led to the possibility of finding disease biomarkers. Carrying out biomarker discovery experiments has been greatly facilitated with the emergence of various analytical and visualization tools as well as annotation databases. These tools and databases are often termed as 'bioinformatics services'. The main purpose of this research was to develop SIBIOS (Bystem for Integration of Bioinformatics Services) as a platform to carry out microarray experiments for the purpose of biomarker discovery. Such experiments require the understanding of the current procedures adopted by researchers to extract biologically significant genes. In the course of this study, sample protocols were built for the purpose of biomarker discovery. A case study on the BCR-ABL subtype of ALL was selected to validate the results. Different approaches for biomarker discovery were explored and both statistical and mining techniques were considered. Biological annotation of the results was also carried out. The final task was to incorporate the new proposed sample protocols into SIBIOS by providing the workflow capabilities and therefore enhancing the system's characteristics to be able to support biomarker discovery workflows

Swarm-Organized Topographic Mapping

Topographieerhaltende Abbildungen versuchen, hochdimensionale oder komplexe Datenbestände auf einen niederdimensionalen Ausgaberaum abzubilden, wobei die Topographie der Daten hinreichend gut wiedergegeben werden soll. Die Qualität solcher Abbildung hängt gewöhnlich vom eingesetzten Nachbarschaftskonzept des konstruierenden Algorithmus ab. Die Schwarm-Organisierte Projektion ermöglicht eine Lösung dieses Parametrisierungsproblems durch die Verwendung von Techniken der Schwarmintelligenz. Die praktische Verwendbarkeit dieser Methodik wurde durch zwei Anwendungen auf dem Feld der Molekularbiologie sowie der Finanzanalytik demonstriert

Identifying chemical entities on literature:a machine learning approach using dictionaries as domain knowledge

Tese de doutoramento, Informática (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2013The volume of life science publications, and therefore the underlying biomedical knowledge, are growing at a fast pace. However the manual literature analysis is a slow and painful task. Hence, text mining systems have been developed to automatically locate the relevant information contained in the literature. An essential step in text mining is named entitiy recognition, but the inherent complexity of biomedical entities, such as chemical compounds, makes it difficult to obtain good performances in this task. This thesis proposes methods capable to improve the current performance of chemical entity recognition from text. Hereby a case based method for recognizing chemical entities is proposed and the obtained evaluation results outperform the most widely used methods, based in dictionaries. A lexical similarity based chemical entity resolution method was also developed and allows an efficient mapping of the recognized entities to the ChEBI database. To improve the chemical entity identification results we developed a validation method that exploits the semantic relationships in ChEBI to measure the similarity between the entities found in the text, in order to discriminate between the correctly identified entities that can be validated and identification errors that should be discarded. A machine learning method for entity recognition error is also proposed, which can efectively find recognition errors in rule based systems. The methods were integrated in a system capable of recognizing chemical entities in texts, map them to the ChEBI database, and provide evidence of validation or recognition error for the recognized entities.O volume de publicações científicas nas ciências da vida está a aumentar a um ritmo crescente. Contudo a análise manual da literatura é um processo árduo e moroso, pelo que têm sido desenvolvidos sistemas de prospecção de texto para identificar automaticamente a informação relevante contida na literatura. Um passo essencial em prospecção de texto é a identificação de entidades nomeadas, mas a complexidade inerente às entidades biomédicas, como é o caso dos compostos químicos, torna difícil obter bons desempenhos nesta tarefa. Esta tese propõe métodos para melhorar o desempenho actual do processo de reconhecimento de entidades químicas em texto. Para tal propõe-se um método para reconhecimento de entidades químicas baseado em aprendizagem automática, que obteve resultados superiores aos métodos baseados em dicionários utilizados actualmente. Desenvolveu-se ainda um método baseado em semelhança lexical que realiza o mapeamento de entidades para a ontologia ChEBI. Para melhorar os resultados de identificação de entidades químicas desenvolveu-se um método de validação que explora as relações semânticas do ChEBI para medir a semelhança entre as entidades encontradas no texto, de forma a discriminar as entidades correctamente identificadas dos erros de identificação. Um método de filtragem de erros baseado em aprendizagem automática é também proposto, e foi testado num sistema baseado em regras. Estes métodos foram integrados num sistema capaz de reconhecer as entidades químicas em texto, mapear para o ChEBI, e fornecer evidência para validação ou detecção de erros das entidades reconhecidas.Fundação para a Ciência e a Tecnologia (FCT, SFRH/BD/36015/2007