In this article we present a novel kernel function, logRatio, which was designed to address two common problems in biological applications: data preprocessing and attribute interaction modelling. An extension of the SVMRFE feature selection algorithm was built around this new kernel function and compared with the original on a number of biological data and text classification problems. Experiments showed that SVMRFE based on the logRatio kernel detects relevant information and handles attribute redundancy more effectively than SVMRFE coupled with other kernels.

Mass spectrometry is becoming an important tool in proteomics. Mass spectral data are characterized by very high dimensionality and a high level of redundancy. Both issues are quite challenging when one wants to perform knowledge discovery and push existing tools to their limits. We tackle both via a preprocessing pipeline that drastically reduces dimensionality and redundancy of the initial representation in order to focus on biologically relevant information. Essentially preprocessing performs feature extraction in a manner that reflects domain knowledge. We propose a framework for the evaluation of the given pipeline and in fact of any mass spectrometry preprocessing pipeline which is based on the level of conservation of discriminatory information. The discriminatory information content of a given representation is objectively measured by the classification performance of a number of classification algorithms evaluated on the given representation. This approach also allows us to compare a number of different preprocessing possibilities, namely using peak intensities vs peak areas to represent peaks and how non observed peaks should be treated, and demonstrate which is the most informative one

With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected. This study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set. We assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset. We examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them. We perform a series of experiments with several feature selection algorithms on a set of proteomics datasets. The experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms. Finally we show how stability profiles can support the choice of a feature selection algorithm.

We present work on a proteomics application. More specifically, from the domain of mass-spectrometry and the identification of biomarkers for stroke attacks. Mass-spectrometry based biomarker identification is an application that sets a number of challenges to the knowledge discovery process. We describe how we tackle them and present a number of machine learning experiments that we performed in order to identify the most suitable learning algorithm for the given problem. However working with real world applications one of the main issues apart from good classification performance is an indication of the factors that really determine the classification decision. Usually based on the results of a resampled-based performance estimation, e.g. cross validation, an algorithm is selected that will provide the operational classification model. On a next step the operational model should be constructed, nevertheless it is not obvious how this should be done since in resampled-based procedures a number of different models are created. We propose a method for linear classifiers that examines the different models produced with cross-validation. The method examines the stability of the models produced from the different training folds and combines them to provide a single model.