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Classification of Breast Cancer Proteins Using DRNN Method

B Madhav Rao1 , V Srinivasa Rao2 , K Srinivasa Rao3

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-2 , Page no. 106-109, Feb-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i2.106109

Online published on Feb 28, 2019

Copyright © B Madhav Rao, V Srinivasa Rao, K Srinivasa Rao . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: B Madhav Rao, V Srinivasa Rao, K Srinivasa Rao, “Classification of Breast Cancer Proteins Using DRNN Method,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.106-109, 2019.

MLA Style Citation: B Madhav Rao, V Srinivasa Rao, K Srinivasa Rao "Classification of Breast Cancer Proteins Using DRNN Method." International Journal of Computer Sciences and Engineering 7.2 (2019): 106-109.

APA Style Citation: B Madhav Rao, V Srinivasa Rao, K Srinivasa Rao, (2019). Classification of Breast Cancer Proteins Using DRNN Method. International Journal of Computer Sciences and Engineering, 7(2), 106-109.

BibTex Style Citation:
@article{Rao_2019,
author = {B Madhav Rao, V Srinivasa Rao, K Srinivasa Rao},
title = {Classification of Breast Cancer Proteins Using DRNN Method},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2019},
volume = {7},
Issue = {2},
month = {2},
year = {2019},
issn = {2347-2693},
pages = {106-109},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3627},
doi = {https://doi.org/10.26438/ijcse/v7i2.106109}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i2.106109}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3627
TI - Classification of Breast Cancer Proteins Using DRNN Method
T2 - International Journal of Computer Sciences and Engineering
AU - B Madhav Rao, V Srinivasa Rao, K Srinivasa Rao
PY - 2019
DA - 2019/02/28
PB - IJCSE, Indore, INDIA
SP - 106-109
IS - 2
VL - 7
SN - 2347-2693
ER -

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Abstract

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.

Key-Words / Index Term

Deep Learning, Accuracy, Protein Sequence, Classification

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