Classification of large amount data is one of the major difficult tasks in data science. This problem can be solved by using deep learning techniques like CNN and RNN. In computational bio informatics, protein sequence classification plays a crucial role to determine the accuracy. The proposed approach uses the RNN based architecture with GRU, LSTM, and basic LSTM and find the accuracy of training data and testing data by considering mean value of three methods. In this method the top fifteen proteins which are obtained by using preprocessing and sequence analyzer methods as one set of input and TCGA breast cancer dataset as second input to this proposed method. Every sequence in test dataset will compare with sequences in train dataset to get accurate classification results. Supervised learning requires complete labeled data where as unsupervised learning requires unlabelled data. In this approach semi supervised learning is used to get high throughput.

Deep Learning, Accuracy, Protein Sequence, Classification

[1] A. Bhola, S. K. Yadav, A. K. Tiwari, Machine Learning Based Approach For Protein Function Prediction Using Sequence Derived Properties, International Journal Of Computer Applications 105 (12).
[2] ABDALRAOUF HASSAN,AUSIF MAHMOOD,” Convolutional Recurrent Deep Learning Model For Sentence Classification”, VOLUME XX, 2017, 2169-3536,IEEE,2017
[3] B Madhav Rao, V Srinivasa Rao,” Preprocessing Of Breast Cancer Protein Expressions Using Correlation Co-Efficient Factors”, JASC: Journal Of Applied Science And Computations, Volume VI, Issue I, January/2019,Pp. 2198-2207,2019
[4] Chaitanya Gupte and Shruti Gadewar, “Diagnosis of Parkinson’s Disease using Acoustic Analysis of Voice”, International Journal of Scientific Research in Network Security and Communication, Vol 5, Issue 3, pp.14-18, June 2017
[5] Dr. S.Vijayarani And Ms. S.Deepa,” PROTEIN SEQUENCE CLASSIFICATION IN DATA MINING– A STUDY”, International Journal Of Information Technology, Modeling And Computing (IJITMC) Vol. 2, No.2, May 2014
[6] Deepika mallampati,”An efficient spam filtering using supervised learning techniques”, International Journal of Scientific Research in Science and Engineering, vol.6 ,issue.2 , pp. 33-37, April 2018.
[7] H. Nielsen, S. Brunak, G. Von Heijne, Machine Learning Approaches For The Prediction Of Signal Peptides And Other Protein Sorting Signals, Protein Engineering 12 (1) Pp:3–9, 1999.
[8] I. Krasteva, N. Inglis, F. Sacchini, R. Nicholas, R. Ayling, C. Churchward, Et Al., Proteomic Characterisation Of Two Strains Of Mycoplasma Mycoides Subsp. Mycoides Of Differing Pathogenicity, J Proteomics Bioinform S 13, 2014 .
[9]Matthew D. Zeiler And Rob Fergus,” Visualizing And Understanding Convolutional Networks”, D. Fleet Et Al. (Eds.): ECCV 2014, Part I, LNCS 8689, Pp. 818–833, 2014. ,Springer International Publishing Switzerland 2014.
[10] P. Johansson, M. Ringner, Classification Of Genomic And Pro- ´ Teomic Data Using Support Vector Machines, In: Fundamentals Of Data Mining In Genomics And Proteomics, Springer, Pp. 187–202, 2007.
[11] S. Saha, R. Chaki, Application Of Data Mining In Protein Sequence Classification, Arxiv Preprint Arxiv:1211.4654.
[12] Sofia Edström Josefin Ondrus,” Sequence Classification Applied To User Log Data”,Pp.9-12,2016,SOFIA EDSTRÖM, JOSEFIN ONDRUS, June 2017.
[13] Timothy K. Lee, Tuan Nguyen,” Protein Family Classification With Neural Networks”, Pp: 1-9.
[14] Xingyou Wang , Weijie Jiang , Zhiyong Luo,” Combination Of Convolutional And Recurrent Neural Network For Sentiment Analysis Of Short Texts”, Proceedings Of COLING 2016, The 26th International Conference On Computational Linguistics: Technical Papers, Pages 2428–2437, Osaka, Japan, December 11-17 2016.
[15]Y. Lecun, Y. Bengio, G. Hinton, “Deep Learning”, Nature International Journal of Science,521 (7553) (2015) pp. 436-444, 2015.