Speeding up index construction with GPU for DNA data sequences

The advancement of technology in scientific community has produced terabytes of biological data.This datum includes DNA sequences.String matching algorithm which is traditionally used to match DNA sequences now takes much longer time to execute because of the large size of DNA data and also the small number of alphabets.To overcome this problem, the indexing methods such as suffix arrays or suffix trees have been introduced.In this study we used suffix arrays as indexing algorithm because it is more applicable, not complex and used less space compared to suffix trees.The parallel method is then introduced to speed up the index construction process. Graphic processor unit (GPU) is used to parallelize a segment of an indexing algorithm. In this research, we used a GPU to parallelize the sorting part of suffix array construction algorithm.Our results show that the GPU is able to accelerate the process of building the index of the suffix array by 1.68 times faster than without GPU

PARALLEL PROCESSING OUTCOMES OF E-ABDULRAZZAQ ALGORITHM USING MULTI-CORE TECHNIQUE

The string matching problem is considered one of the substantial problems in the fields of computer science like speech and pattern recognition, signal and image processing, and artificial intelligence (AI). The increase in the speedup of performance is considered an important factor in meeting the growth rate of databases, Subsequently, one of the determinations to address this issue is the parallelization for exact string matching algorithms. In this study, the E-Abdulrazzaq string matching algorithm is chosen to be executed with the multi-core environment utilizing the OpenMP paradigm which can be utilized to decrease the execution time and increase the speedup of the algorithm. The parallelization algorithm got positive results within the parallel execution time, and excellent speeding-up capabilities, in comparison to the successive result. The Protein database showed optimal results in parallel execution time, and when utilizing short and long pattern lengths. The DNA database showed optimal speedup execution when utilizing short and long pattern lengths, while no specific database obtained the worst results

A Comparative Taxonomy of Parallel Algorithms for RNA Secondary Structure Prediction

RNA molecules have been discovered playing crucial roles in numerous biological and medical procedures and processes. RNA structures determination have become a major problem in the biology context. Recently, computer scientists have empowered the biologists with RNA secondary structures that ease an understanding of the RNA functions and roles. Detecting RNA secondary structure is an NP-hard problem, especially in pseudoknotted RNA structures. The detection process is also time-consuming; as a result, an alternative approach such as using parallel architectures is a desirable option. The main goal in this paper is to do an intensive investigation of parallel methods used in the literature to solve the demanding issues, related to the RNA secondary structure prediction methods. Then, we introduce a new taxonomy for the parallel RNA folding methods. Based on this proposed taxonomy, a systematic and scientific comparison is performed among these existing methods

The Improved Hybrid Algorithm for the Atheer and Berry-Ravindran Algorithms

Exact String matching considers is one of the important ways in solving the basic problems in computer science. This research proposed a hybrid exact string matching algorithm called E-Atheer. This algorithm depended on good features; searching and shifting techniques in the Atheer and Berry-Ravindran algorithms, respectively. The proposed algorithm showed better performance in number of attempts and character comparisons compared to the original and recent and standard algorithms. E-Atheer algorithm used several types of databases, which are DNA, Protein, XML, Pitch, English, and Source. The best performancein the number of attempts is when the algorithm is executed using the pitch dataset. The worst performance is when it is used with DNA dataset. The best and worst databases in the number of character comparisons with the E-Atheer algorithm are the Source and DNA databases, respectively

GPU-based odd and even hybrid string matching algorithm

String matching is considered as one of the fundamental problems in computer science.Many computer applications provide the string matching utility for their users, and how fast one or more occurrences of a given pattern can be found in a text plays a prominent role in their user satisfaction.Although numerous algorithms and methods are available to solve the string matching problem, the remarkable increase in the amount of data which is produced and stored by modern computational devices demands researchers to find much more efficient ways for dealing with this issue.In this research, the Odd and Even (OE) hybrid string matching algorithm is redesigned to be executed on the Graphics Processing Unit (GPU), which can be utilized to reduce the burden of compute-intensive operations from the Central Processing Unit (CPU).In fact, capabilities of the GPU as a massively parallel processor are employed to enhance the performance of the existing hybrid string matching algorithms.Different types of data are used to evaluate the impact of parallelization and implementation of both algorithms on the GPU. Experimental results indicate that the performance of the hybrid string matching algorithms has been improved, and the speedup, which has been obtained, is considerable enough to suggest the GPU as the suitable platform for these hybrid string-matching algorithms

Feature Selection Method using Genetic Algorithm for Medical Dataset

There is a massive amount of high dimensional data that is pervasive in the healthcare domain. Interpreting these data continues as a challenging problem and it is an active research area due to their nature of high dimensional and low sample size. These problems produce a significant challenge to the existing classification methods in achieving high accuracy. Therefore, a compelling feature selection method is important in this case to improve the correctly classify different diseases and consequently lead to help medical practitioners. The methodology for this paper is adapted from KDD method. In this work, a wrapper-based feature selection using the Genetic Algorithm (GA) is proposed and the classifier is based on Support Vector Machine (SVM). The proposed algorithms was tested on five medical datasets naming the Breast Cancer, Parkinson’s, Heart Disease, Statlog (Heart), and Hepatitis. The results obtained from this work, which apply GA as feature selection yielded competitive results on most of the datasets. The accuracies of the said datasets are as follows: Breast Cancer - 72.71%, Parkinson’s – 88.36%, Heart Disease – 86.73%, Statlog (Heart) – 85.48 %, and Hepatitis – 76.95%. This prediction method with GA as feature selection will help medical practitioners to make better diagnose with patient’s disease. 

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