Despite the grand promises of the postgenomic era, such as personalized prevention, diagnosis, drugs, and treatments, the landscape of biomedicine looks more and more complex. The fullfillment of these promises for diseases significant in public health requires new approaches to induction for statistical and causal inferences from observations and interventions. Within the biomedical world an important response to this challenge is the mapping and relatively cheap measuring of the genetic variations, such as single nucleotide polymorphisms (SNPs). The recent mapping of the genetic variations has opened a new dimension in the postgenomic research at all phenotypic levels, such as genomic, proteomic, and clinical, and it has sparked a series of Genetic Association Studies (GAS), based on the application of machine learning and data mining techniques. To overcome such problems, different strategies are being investigated within the research community. The aim of this thesis work is to contribute to the progress in this field giving a step forward towards the solution. I have investigated the suitable machine learning and data mining algorithms for this task and the state of the art of the currently available implementations of them intended for biomedical research applications. As a result I have proposed a solution strategy, and chosen and extended the functionality of the Java-ML library, an open source machine learning library written in Java, implementing some missing algorithms and functionality that necessary for the proposed approach. This thesis work is structured into three main blocks. Section 3 “An approach to the use of machine learning techniques with genotype data” addresses the faced problem and the proposed solution. It begins with the definition of some introductory GAS concepts and the description of the solution strategy and elaborates in subsequent subsections on the description of the theoretical underpinnings of the algorithms setting up the solution. Specifically, the first subsection, “The feature selection problem in the bioinformatics domain”, justifies the necessity of reducing the dimensionality of data sets in order to allow for acceptable performance in the application of machine learning techniques to the broader field of bioinformatics implications and establishes a comparative taxonomy of the currently available techniques. In the second subsection, entitled “Feature selection using support-vector machines”, the idea behind support-vector machines classifiers and their application to feature subset selection is defined while the third subsection, “Ranking fusion as averaging technique: Markov chain based algorithms”, describes the ranking fusion algorithms which implementation has been chosen for the combination of the feature subsets obtained from different data sets. Section 4 “Analysis of available tools for experimental design” analyses the available suitable tools for experimental design in GAS based on machine learning techniques. In this sense in the first subsection, “Advantages of high level languages for machine learning algorithms”, the convenience of using high level languages for the kind of applications we are working in is discussed. In the second subsection, “Machine learning algorithms implementations in Java”, the election of the Java language is justified followed by an analysis of the currently available implementations of machine learning algorithms in this language that are worthwhile to be considered for our purposes, namely WEKA, RapidMiner and Java-ML. In Section 5 “Implemented extensions to the Java-ML library” a description of the functionalities that have been added to enable a framework suitable for the design of GAS experiments in order to test the proposed approach is provided. The “Missing values imputation: the dataset.tools package” subsection focuses on data sets handling functionalities while the “Averaging through ranking fusion: rankingfusion and rankingfusion.scoring package” subsection details the ranking fusion algorithms implementations. Finally the “How to use the code” subsection is a tutorial on how to use both the library and its extension for the development of applications. In addition to these main blocks, a final section called “Future Work” reflects how the developed work can be used by GAS domain experts to evaluate the usefulness of the proposed technique.Ingeniería de Telecomunicació
