The eight papers in this special section were presented at the 17th Asia Pacific bioinformaticsconference (APBC), which was held in Wuhan, China, 14-16 January 2019.
The eight papers in this special section were presented at the 17th Asia Pacific bioinformaticsconference (APBC), which was held in Wuhan, China, 14-16 January 2019.
Finding approximately conserved sequences, called motifs, across multiple DNA or protein sequences is an important problem in computationalbiology. In this paper, we consider the (l, d) motif search problem of identi...
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Finding approximately conserved sequences, called motifs, across multiple DNA or protein sequences is an important problem in computationalbiology. In this paper, we consider the (l, d) motif search problem of identifying one or more motifs of length l present in at least q of the n given sequences, with each occurrence differing from the motif in at most d substitutions. The problem is known to be NP-complete, and the largest solved instance reported to date is (26, 11). We propose a novel algorithm for the (l, d) motif search problem using streaming execution over a large set of non-deterministic finite automata (NFA). This solution is designed to take advantage of the micron automata processor, a new technology close to deployment that can simultaneously execute multiple NFA in parallel. We demonstrate the capability for solving much larger instances of the (l, d) motif search problem using the resources available within a single automata processor board, by estimating run-times for problem instances (39, 18) and (40, 17). The paper serves as a useful guide to solving problems using this new accelerator technology.
Motivated by known preferences for certain amino acids in positions around a-helices, we developed neural network-based predictors of both N and C a-helix ends, which achieved about 88% accuracy. We applied a similar ...
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Post-translational prote in modifications play an important role in many protein path ways and interactions. It has been hypothesized that modifications to prote insoccur in regions that are easily accessible, and man...
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Classifying Microarray data, which are of high dimensional nature, requires high computational power. Support Vector Machines-based classifier (SVM) is among the most common and successful classifiers used in the anal...
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ISBN:
(纸本)9781457702167
Classifying Microarray data, which are of high dimensional nature, requires high computational power. Support Vector Machines-based classifier (SVM) is among the most common and successful classifiers used in the analysis of Microarray data but also requires high computational power due to its complex mathematical architecture. Implementing SVM on hardware exploits the parallelism available within the algorithm kernels to accelerate the classification of Microarray data. In this work, a flexible, dynamically and partially reconfigurable implementation of the SVM classifier on Field Programmable Gate Array (FPGA) is presented. The SVM architecture achieved up to 85x speed-up over equivalent general purpose processor (GPP) showing the capability of FPGAs in enhancing the performance of SVM-based analysis of Microarray data as well as future bioinformatics applications.
EURYALE is a Nextflow pipeline designed for the sensitive taxonomic classification and flexible functional annotation of metagenomic shotgun sequences. It provides a comprehensive solution for preprocessing, assembly,...
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This book constitutes the thoroughly refereed post-conference proceedings of the 11th International Meeting on computationalintelligence Methods for bioinformatics and Biostatistics, CIBB 2014, held in Cambridge, UK,...
ISBN:
(数字)9783319244624
ISBN:
(纸本)9783319244617
This book constitutes the thoroughly refereed post-conference proceedings of the 11th International Meeting on computationalintelligence Methods for bioinformatics and Biostatistics, CIBB 2014, held in Cambridge, UK, in June *** 25 revised full papers presented were carefully reviewed and selected from 44 submissions. The papers focus problems concerning computational techniques in bioinformatics, systems biology, medical informatics and biostatistics.
The papers in this special section were presented at the 13th International Workshop on Data Mining in bioinformatics (BIOKDD’14) was organized in conjunction with the ACM SIGKDD International conference on Knowledge...
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The papers in this special section were presented at the 13th International Workshop on Data Mining in bioinformatics (BIOKDD’14) was organized in conjunction with the ACM SIGKDD International conference on Knowledge Discovery and Data Mining that was held on August 24, 2014 in New York, NY. It brought together international researchers in the interacting disciplines of data mining, systems biology, and bioinformatics at the Bloomberg Headquarters venue. The goal of this workshop is to encourage Knowledge Discovery and Data mining (KDD) researchers to take on the numerous challenges that bioinformatics offers.
Correlation is a very widely used filter criterion for gene selection in cancer classification. However, it uses all the training samples in ranking, which may not be equally important for the classification. Using su...
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Development and application of pattern recognition techniques in the field of bioinformatics is of utmost importance for gaining new insights about phenomena in life sciences through the analysis of biological data. I...
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Development and application of pattern recognition techniques in the field of bioinformatics is of utmost importance for gaining new insights about phenomena in life sciences through the analysis of biological data. In this special section, three research manuscripts in their significantly extended form were selected from the papers presented at the Eighth IAPR International conference on Pattern Recognition in bioinformatics (PRIB 2013), which was held in Nice, France.
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