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Evolutionary framework for coding area selection from cancer data

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Abstract

Cancer data analysis is significant to detect the codes that are responsible for cancer diseases. It is significant to find out the coding regions from diseases infected biological data. The infected data will be helpful to design proper drugs and will be supportable in laboratory assessments. Codes bear specific meaning on various features as well as symptoms of diseases. Coding of biological data is a key area to get exact information on animals to discover the desired medicine. In the current work, four different machine learning approaches such as support vector machine (SVM), principal component analysis (PCA) technique, neural mapping skyline filtering (NMSF) and Fisher’s discriminant analysis (FDA) were applied for data reduction and coding area selection. The experimental analysis established that the SVM outperforms PCA and FDA. However, due to the mapping facility, NMSF outperforms SVM. Thus, the NMSF achieved the preeminent results among the four techniques. Matthews’s correlation coefficient was used to evaluate the accuracy, specificity, sensitivity, F-measures and error rate of the four methods that are used to determine the coding area. Detailed experimental analysis included comparison study among the four classifiers for the deoxyribonucleic acid dataset.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh

    Sarwar Kamal, Shamim H. Ripon & Nawab Yousuf Ali

  2. Techno India Institute of Technology, Kolkata, India

    Nilanjan Dey

  3. Notre Dame University, Dhaka, Bangladesh

    Sonia Farhana Nimmy

  4. Department of Electronics and Electrical Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  5. Department of Computer Science, Taif University, Taif, Kingdom of Saudi Arabia

    Wahiba Ben Abdessalem Karaa

  6. Duy Tan University, Danang, Vietnam

    Gia Nhu Nguyen

  7. College of Information and Engineering, Wenzhou Medical University, Wenzhou, People’s Republic of China

    Fuqian Shi

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  1. Sarwar Kamal
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  2. Nilanjan Dey
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  6. Amira S. Ashour
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  7. Wahiba Ben Abdessalem Karaa
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  8. Gia Nhu Nguyen
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Correspondence to Amira S. Ashour.

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Kamal, S., Dey, N., Nimmy, S.F. et al. Evolutionary framework for coding area selection from cancer data. Neural Comput & Applic 29, 1015–1037 (2018). https://doi.org/10.1007/s00521-016-2513-3

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