MOTIVATION: Whole-genome sequencing has revolutionized biosciences by providing tools for constructing complete DNA sequences of individuals. With entire genomes at hand, scientists can pinpoint DNA fragments responsible for oncogenesis and predict patient responses to cancer treatments. Machine learning plays a paramount role in this process. However, the sheer volume of whole-genome data makes it difficult to encode the characteristics of genomic variants as features for learning algorithms. RESULTS: In this article, we propose three feature extraction methods that facilitate classifier learning from sets of genomic variants. The core contributions of this work include: (i) strategies for determining features using variant length binning, clustering and density estimation; (ii) a programing library for automating distribution-based feature extraction in machine learning pipelines. The proposed methods have been validated on five real-world datasets using four different classification algorithms and a clustering approach. Experiments on genomes of 219 ovarian, 61 lung and 929 breast cancer patients show that the proposed approaches automatically identify genomic biomarkers associated with cancer subtypes and clinical response to oncological treatment. Finally, we show that the extracted features can be used alongside unsupervised learning methods to analyze genomic samples. AVAILABILITY AND IMPLEMENTATION: The source code of the presented algorithms and reproducible experimental scripts are available on Github at https://github.com/MNMdiagnostics/dbfe. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract There is a certain belief among data science researchers and enthusiasts alike that clustering can be used to improve classification quality. Insofar as this belief is fairly uncontroversial, it is also very general and therefore produces a lot of confusion around the subject. There are many ways of using clustering in classification and it obviously cannot always improve the quality of predictions, so a question arises, in which scenarios exactly does it help? Since we were unable to find a rigorous study addressing this question, in this paper, we try to shed some light on the concept of using clustering for classification. To do so, we first put forward a framework for incorporating clustering as a method of feature extraction for classification. The framework is generic w.r.t. similarity measures, clustering algorithms, classifiers, and datasets and serves as a platform to answer ten essential questions regarding the studied subject. Each answer is formulated based on a separate experiment on 16 publicly available datasets, followed by an appropriate statistical analysis. After performing the experiments and analyzing the results separately, we discuss them from a global perspective and form general conclusions regarding using clustering as feature extraction for classification.

Now that the use of XML is prevalent, methods for mining semi-structured documents have become even more important. In particular, one of the areas that could greatly benefit from in-depth analysis of XML’s semi-structured nature is cluster analysis. Most of the XML clustering approaches developed so far employ pairwise similarity measures. In this paper, we study clustering algorithms, which use patterns to cluster documents without the need for pairwise comparisons. We investigate the shortcomings of existing approaches and establish a new pattern-based clustering framework called XPattern, which tries to address these shortcomings. The proposed framework consists of four steps: choosing a pattern definition, pattern mining, pattern clustering, and document assignment. The framework’s distinguishing feature is the combination of pattern clustering and document-cluster assignment, which allows to group documents according to their characteristic features rather than their direct similarity. We experimentally evaluate the proposed approach by implementing an algorithm called PathXP, which mines maximal frequent paths and groups them into profiles. PathXP was found to match, in terms of accuracy, other XML clustering approaches, while requiring less parametrization and providing easily interpretable cluster representatives. Additionally, the results of an in-depth experimental study lead to general suggestions concerning pattern-based XML clustering.