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GSCNN: a composition of CNN and Gibb Sampling computational strategy for predicting promoter in bacterial genomes

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Abstract

This paper is about a new computational strategy to find the promoter sequence which is a transcription starting factor in DNA without which the gene sequence cannot be activated for further protein synthesis process. Genes are the hereditary source of organisms’ functionality of living things. Sequence of activities is carried out to reveal the protein production process hidden in the genes. In this regard promoter prediction is essential to bring out correctness of protein synthesis in decoding the genetical information. Also it enables the biologists to find the genetical malfunctions. Nowadays numerous computational methods which have been proposed are helpful to the biologist in related tasks. Earlier works applied Gibb sampling method, Markov Chain Model and machine learning strategies to predict the promoters. In this paper a new computational strategy named as GSCNN is introduced which combines Gibb sampling with Convolution Neural Network (CNN). This model uses Gibb sampling method to select the best profile features of DNA and extract the scores as feature vector with the help of position frequency matrix. Those extracted feature vectors are further fed into Convolution Neural Network to predict the sigma (σ) 54 promoter of bacterial genome. The performance of the proposed system is evaluated with the help of Bacillus subtilis NC_000964 dataset. The sigma (σ) 54 promoter prediction using the proposed GSCNN method achieved significant improvement in performance metrics compared with the traditional machine learning algorithms such as Decision Tree (DT), K-Nearest Neighbor(KNN), Random Forest(RF) and Support Vector Machine(SVM).

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Authors and Affiliations

  1. Sri Sarada College for Women, Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, 627012, Tamil Nadu, India

    Sasikala S.

  2. Department of Computer Applications, Sri Sarada College for Women, Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, 627012, Tamil Nadu, India

    Ratha Jeyalakshmi T.

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Sasikala, S., Ratha Jeyalakshmi, T. GSCNN: a composition of CNN and Gibb Sampling computational strategy for predicting promoter in bacterial genomes. Int. j. inf. tecnol. 13, 493–499 (2021). https://doi.org/10.1007/s41870-020-00565-y

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