Separate and conquer heuristic allows robust mining of contrast sets from various types of data

Identifying differences between groups is one of the most important knowledge discovery problems. The procedure, also known as contrast sets mining, is applied in a wide range of areas like medicine, industry, or economics. In the paper we present RuleKit-CS, an algorithm for contrast set mining based on a sequential covering - a well established heuristic for decision rule induction. Multiple passes accompanied with an attribute penalization scheme allow generating contrast sets describing same examples with different attributes, unlike the standard sequential covering. The ability to identify contrast sets in regression and survival data sets, the feature not provided by the existing algorithms, further extends the usability of RuleKit-CS. Experiments on wide range of data sets confirmed RuleKit-CS to be a useful tool for discovering differences between defined groups. The algorithm is a part of the RuleKit suite available at GitHub under GNU AGPL 3 licence (https://github.com/adaa-polsl/RuleKit). Keywords: Contrast sets, Sequential covering, Rule induction, Regression, Survival, Knowledge discover

A Parallel GPU-Designed Algorithm for the Constrained Multiple Sequence Alignment Problem

Abstract. Modern graphical processing units (GPUs) offer much more computational power than modern CPUs, so it is natural that GPUs are often used for solving many computationally-intensive problems. One of the tasks of huge importance in bioinformatics is sequence alignment. We investigate its variant introduced a few years ago in which some additional requirement on the alignment is given. As a result we propose a parallel version of Center-Star algorithm computing the constrained multiple sequence alignment at the GPU. The obtained speedup over the serial CPU relative is in range [20, 200]

QuickProbs--a fast multiple sequence alignment algorithm designed for graphics processors.

Multiple sequence alignment is a crucial task in a number of biological analyses like secondary structure prediction, domain searching, phylogeny, etc. MSAProbs is currently the most accurate alignment algorithm, but its effectiveness is obtained at the expense of computational time. In the paper we present QuickProbs, the variant of MSAProbs customised for graphics processors. We selected the two most time consuming stages of MSAProbs to be redesigned for GPU execution: the posterior matrices calculation and the consistency transformation. Experiments on three popular benchmarks (BAliBASE, PREFAB, OXBench-X) on quad-core PC equipped with high-end graphics card show QuickProbs to be 5.7 to 9.7 times faster than original CPU-parallel MSAProbs. Additional tests performed on several protein families from Pfam database give overall speed-up of 6.7. Compared to other algorithms like MAFFT, MUSCLE, or ClustalW, QuickProbs proved to be much more accurate at similar speed. Additionally we introduce a tuned variant of QuickProbs which is significantly more accurate on sets of distantly related sequences than MSAProbs without exceeding its computation time. The GPU part of QuickProbs was implemented in OpenCL, thus the package is suitable for graphics processors produced by all major vendors

Learning rule sets from survival data

Abstract Background Survival analysis is an important element of reasoning from data. Applied in a number of fields, it has become particularly useful in medicine to estimate the survival rate of patients on the basis of their condition, examination results, and undergoing treatment. The recent developments in the next generation sequencing open new opportunities in survival study as they allow vast amount of genome-, transcriptome-, and proteome-related features to be investigated. These include single nucleotide and structural variants, expressions of genes and microRNAs, DNA methylation, and many others. Results We present LR-Rules, a new algorithm for rule induction from survival data. It works according to the separate-and-conquer heuristics with a use of log-rank test for establishing rule body. Extensive experiments show LR-Rules to generate models of superior accuracy and comprehensibility. The detailed analysis of rules rendered by the presented algorithm on four medical datasets concerning leukemia as well as breast, lung, and thyroid cancers, reveals the ability to discover true relations between attributes and patients’ survival rate. Two of the case studies incorporate features obtained with a use of high throughput technologies showing the usability of the algorithm in the analysis of bioinformatics data. Conclusions LR-Rules is a viable alternative to existing approaches to survival analysis, particularly when the interpretability of a resulting model is crucial. Presented algorithm may be especially useful when applied on the genomic and proteomic data as it may contribute to the better understanding of the background of diseases and support their treatments

MSAProbs algorithm computation scheme.

<p>Stages denoted with (*) are run in parallel with a use of OpenMP.</p

Algorithm 2.

<p>Pseudo-code of the generalised dynamic programming forward and reversed passes. and indicate 'th row and 'th column of matrix.</p

Algorithm 1.

<p>Pseudo-code of the posterior matrix calculation procedure. Statements show only general form of data dependencies. Procedure requires execution of 6 anti-diagonal passes (lines 2, , 5, 6, 7, and ). is computed first due to greater memory requirements.</p

Execution times for OXBench-X benchmark reported in format.

<p>Speed-ups of QuickProbs (CPU+GPU) over MSAProbs (CPU) across different hardware configurations are also shown.</p

Detailed characteristics of the protein families taken from Pfam database.

<p>Each set is described by the number of sequences and the average sequence length .</p