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Instance reduction methods are popular methods that reduce the size of the datasets to possibly improve the classification accuracy. We present a method that reduces the size of the dataset based on the percentile of the dataset partitions which we call IPRed. We evaluate our and other popular instance reduction methods from a classification perspective by(More)
Predicting drug response to cancer disease is an important problem in modern clinical oncology that attracted increasing recent attention from various domains such as computational biology, machine learning, and data mining. Cancer patients respond differently to each cancer therapy owing to disease diversity, genetic factors, and environmental causes.(More)
Programs based on hash tables and Burrows-Wheeler are very fast for mapping short reads to genomes but have low accuracy in the presence of mismatches and gaps. Such reads can be aligned accurately with the Smith-Waterman algorithm but it can take hours and days to map millions of reads even for bacteria genomes. We introduce a GPU program called MaxSSmap(More)
  • Zentralbibliothek Albers, A Deigendesch, +16 authors J Müller
  • 2011
On the tribochemical action of engine soot. Design and prototyping of a ceramic micro turbine: a case study. Fabrication of particle and composition gradients by systematic interaction of sedimentation and electrical field in electrophoretic deposition. Influence of dispersant on rheology of zirconia-paraffin feedstocks and mechanical properties of micro(More)
Supervised methods for inferring gene regulatory networks (GRNs) perform well with good training data. However, when training data is absent, these methods are not applicable. Unsupervised methods do not need training data but their accuracy is low. In this paper, we combine supervised and unsupervised methods to infer GRNs using time-series gene expression(More)
Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity(More)
Ensemble methods such as AdaBoost are popular machine learning methods that create highly accurate classifier by combining the predictions from several classifiers. We present a parametrized method of AdaBoost that we call Top-k Parametrized Boost. We evaluate our and other popular ensemble methods from a classification perspective on several real datasets.(More)