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Boruta - A System for Feature Selection

Kursa M.B., Jankowski A., Rudnicki W.R.

Fundamenta Informaticae

2010

Vol. 101, nr 4

271-285

Machine learning methods are often used to classify objects described by hundreds of attributes; in many applications of this kind a great fraction of attributes may be totally irrelevant to the classification problem. Even more, usually one cannot decide a priori which attributes are relevant. In this paper we present an improved version of the algorithm for identification of the full set of truly important variables in an information system. It is an extension of the random forest method which utilises the importance measure generated by the original algorithm. It compares, in the iterative fashion, the importances of original attributes with importances of their randomised copies. We analyse performance of the algorithm on several examples of synthetic data, as well as on a biologically important problem, namely on identification of the sequence motifs that are important for aptameric activity of short RNA sequences.

The new SIMD Implementation of the Smith-Waterman Algorithm on Cell Microprocessor

Rudnicki W.R., Jankowski A., Modzelewski A., Piotrowski A., Zadrożny A.

Fundamenta Informaticae

2009

Vol. 96, nr 1/2

181-194

Algorithms for estimating similarity between two macromolecular sequences are of profound importance for molecular biology. The standard methods utilize so-called primary structure, that is a string of characters denoting the sequence of monomers in hetero-polymer. These methods find the substrings of maximal similarity, as defined by the so-called similarity matrix, for a pair of two molecules. The problem is solved either by the exact dynamic programming method, or by approximate heuristic methods. The approximate algorithms are almost two orders of magnitude faster in comparison with the standard version of the exact Smith-Waterman algorithm, when executed on the same hardware, hence the exact algorithm is relatively rarely used. Recently a very efficient implementation of Smith-Waterman algorithm utilizing SIMD extensions to the standard instruction set reduced the speed advantage of heuristic algorithms to factor of three. Here we present an improved implementation of the Smith-Waterman algorithm on the Cell processor. Implementation presented here achieves execution speed of approximately 9 GCUPS. The performance is independent on the scoring system. It is 4 to 10 times faster than best Smith-Waterman implementation running on a PC and 1.5 to 3 times faster than the same implementation running on Sony PlayStation 3. It is also 5 times faster than the recent implementation of the Smith-Waterman utilizing Nvidia GPU. Our implementation running on Sony PlayStation 3 has performance which is directly comparable with that of BLAST running on PC, being up to 4 times faster in the best case and no more than two times slower in the worst case. This performance level opens possibility for using the exact Smith-Waterman algorithm in applications, where currently approximate algorithms are used.