A Brief Review of Data Mining Application Involving Protein Sequence Classification

Data mining techniques have been used by researchers for analyzing protein sequences. In protein analysis, especially in protein sequence classification, selection of feature is most important. Popular protein sequence classification techniques involve extraction of specific features from the sequences. Researchers apply some well-known classification techniques like neural networks, Genetic algorithm, Fuzzy ARTMAP, Rough Set Classifier etc for accurate classification. This paper presents a review is with three different classification models such as neural network model, fuzzy ARTMAP model and Rough set classifier model. A new technique for classifying protein sequences have been proposed in the end. The proposed technique tries to reduce the computational overheads encountered by earlier approaches and increase the accuracy of classification.Comment: 10 pages, 1 table, 1 figure. arXiv admin note: substantial text overlap with arXiv:1211.465

Application of Data mining in Protein sequence Classification

Protein sequence classification involves feature selection for accurate classification. Popular protein sequence classification techniques involve extraction of specific features from the sequences. Researchers apply some well-known classification techniques like neural networks, Genetic algorithm, Fuzzy ARTMAP,Rough Set Classifier etc for accurate classification. This paper presents a review is with three different classification models such as neural network model, fuzzy ARTMAP model and Rough set classifier model. This is followed by a new technique for classifying protein sequences. The proposed model is typically implemented with an own designed tool and tries to reduce the computational overheads encountered by earlier approaches and increase the accuracy of classificationComment: 16 Pages, 7 Figures, 3 Table

Delineation of Techniques to implement on the enhanced proposed model using data mining for protein sequence classification

In post genomic era with the advent of new technologies a huge amount of complex molecular data are generated with high throughput. The management of this biological data is definitely a challenging task due to complexity and heterogeneity of data for discovering new knowledge. Issues like managing noisy and incomplete data are needed to be dealt with. Use of data mining in biological domain has made its inventory success. Discovering new knowledge from the biological data is a major challenge in data mining technique. The novelty of the proposed model is its combined use of intelligent techniques to classify the protein sequence faster and efficiently. Use of FFT, fuzzy classifier, String weighted algorithm, gram encoding method, neural network model and rough set classifier in a single model and in an appropriate place can enhance the quality of the classification system.Thus the primary challenge is to identify and classify the large protein sequences in a very fast and easy but intellectual way to decrease the time complexity and space complexity.Comment: 8 pages, 1 figure