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Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part, which features interactive data analysis and component-based assembly of data mining procedures. In the selection and design of components, we focus on the flexibility of their reuse: our principal(More)
Most of the present subgroup discovery approaches aim at finding subsets of attribute-value data with unusual distribution of a single output variable. In general, real-life problems may be described with richer, multi-dimensional descriptions of the outcome. The discovery task in such domains is to find subsets of data instances with similar outcome(More)
The vastness of chemical space and the relatively small coverage by experimental data recording molecular properties require us to identify subspaces, or domains, for which we can confidently apply QSAR models. The prediction of QSAR models in these domains is reliable, and potential subsequent investigations of such compounds would find that the(More)
ATP-binding cassette (ABC) transporters can translocate a broad spectrum of molecules across the cell membrane including physiological cargo and toxins. ABC transporters are known for the role they play in resistance towards anticancer agents in chemotherapy of cancer patients. There are 68 ABC transporters annotated in the genome of the social amoeba(More)
Transcriptional profiling methods have been utilized in the analysis of various biological processes in Dictyostelium. Recent advances in high-throughput sequencing have increased the resolution and the dynamic range of transcriptional profiling. Here we describe the utility of RNA sequencing with the Illumina technology for production of transcriptional(More)
Machine learning methods that can use additional knowledge in their inference process are central to the development of integrative bioinformatics. Inclusion of background knowledge improves robustness, predictive accuracy and interpretability. Recently, a set of such techniques has been proposed that use information on gene sets for supervised data mining(More)
BACKGROUND Computational methods that infer single nucleotide polymorphism (SNP) interactions from phenotype data may uncover new biological mechanisms in non-Mendelian diseases. However, practical aspects of such analysis face many problems. Present experimental studies typically use SNP arrays with hundreds of thousands of SNPs but record only hundreds of(More)
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