A Comparison of Apache Spark Supervised Machine Learning Algorithms for DNA Splicing Site Prediction

Morfino, Valerio; Rampone, Salvatore; Weitschek, Emanuel

doi:10.1007/978-981-13-8950-4_13

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…According to Formulas ( 6) and ( 7), the hyperplane is obtained, and its decision function is shown in Formula (8).…”

Section: Related Workmentioning

confidence: 99%

“…Sharma et al [7] proposed an acceptor site recognition method, which combined Adaptive Short Time Fourier Transform (ASTFT), period-3 measure, and with principal component analysis algorithm. Morfino et al [8] dealt with the splicing site recognition problem in DNA sequences by using supervised machine learning algorithms included in the MLlib library of Apache Spark, a fast and general engine for big data processing. L. Wang et al [9] proposed a recognition algorithm on slicing site based on improved SVM.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Study on Gene Splicing Site Recognition Based on Particle Swarm Optimization Twin Support Vector Machine Algorithm for Smart Healthcare

Zhang

Wang

Mei

et al. 2023

Wireless Communications and Mobile Computing

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Gene splicing site recognition is a very important research topic in smart healthcare. Gene splicing site recognition is of great significance, not only for the large-scale and high-quality computational annotation of genomes but also for the analysis and recognition of the gene sequences evolutionary process. It is urgent to study a reliable and effective algorithm for gene splice site recognition. Traditional Twin Support Vector Machine (TWSVM) algorithm has advantages in solving small-sample, nonlinear, and high-dimensional problems, but it cannot deal with parameter selection well. To avoid the blindness of parameter selection, particle swarm optimization algorithm was used to find the optimal parameters of twin support vector machine. Therefore, a Particle Swarm Optimization Twin Support Vector Machine (PSO-TWSVM) algorithm for gene splicing site recognition was proposed in this paper. The proposed algorithm was compared with traditional Support Vector Machine algorithm, TWSVM algorithm, and Least Squares Support Vector Machine algorithm. The comparison results show that the positive sample recognition rate, negative sample recognition rate, and correlation coefficient (CC) of the proposed algorithm are the best among the four different support vector machine algorithms. The proposed algorithm effectively improves the recognition rate and the accuracy of splice sites. The comparison experiments verify the feasibility of the proposed algorithm.

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“…According to Formulas ( 6) and ( 7), the hyperplane is obtained, and its decision function is shown in Formula (8).…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study on Gene Splicing Site Recognition Based on Particle Swarm Optimization Twin Support Vector Machine Algorithm for Smart Healthcare

Zhang

Wang

Mei

et al. 2023

Wireless Communications and Mobile Computing

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show abstract

“…According to formula (6) and formula (7), the hyperplane is obtained, and its decision function is shown in formula (8).…”

Section: Twsvm Algorithmmentioning

confidence: 99%

“…Sharma et al [7] proposed an acceptor site prediction method, which combined Adaptive Short Time Fourier Transform (ASTFT), period-3 measure, with principal component analysis algorithm. Morfino et al [8] dealt with the splicing site prediction problem in DNA sequences by using supervised machine learning algorithms included in the MLlib library of Apache Spark, a fast and general engine for big data processing. Wang et al [9] proposed a prediction algorithm on slicing site based on improved Support Vector Machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

An Advanced Twin Support Vector Machine Algorithm for Gene Splicing Sites Prediction

Zhang

Wang²,

Peng

et al. 2022

Preprint

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Gene splicing sites prediction is of great significance, not only for the large-scale and high-quality computational annotation of genomes, but also for the analysis and prediction of the gene sequences evolutionary process. It is helpful to promote the development of smart healthcare. Traditional Twin Support Vector Machines (TWSVM) algorithm has advantages in solving small-sample, nonlinear and high-dimensional problems, but it cannot deal with parameter selection well. Therefore, a Particle Swarm Optimization Twin Support Vector Machines (PSO-TWSVM) algorithm for gene splicing sites prediction was proposed in this paper. In order to avoid the blindness of parameter selection, particle swarm optimization algorithm was used to find the optimal parameters of twin support vector machines. The proposed algorithm was compared with traditional TWSVM algorithm and Least Squares Support Vector Machine (LSSVM) algorithm. The comparison results show that the positive sample prediction rate, negative sample prediction rate and correlation coefficient (CC) of the proposed algorithm are the best among the three different support vector machines. The proposed algorithm effectively improves the prediction rate and the accuracy of splice sites. The comparison experiments verify the feasibility of the proposed algorithm.

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SP-BRAIN: scalable and reliable implementations of a supervised relevance-based machine learning algorithm

2019

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A Comparison of Apache Spark Supervised Machine Learning Algorithms for DNA Splicing Site Prediction

Cited by 4 publications

References 16 publications

Study on Gene Splicing Site Recognition Based on Particle Swarm Optimization Twin Support Vector Machine Algorithm for Smart Healthcare

Study on Gene Splicing Site Recognition Based on Particle Swarm Optimization Twin Support Vector Machine Algorithm for Smart Healthcare

An Advanced Twin Support Vector Machine Algorithm for Gene Splicing Sites Prediction

SP-BRAIN: scalable and reliable implementations of a supervised relevance-based machine learning algorithm

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