Protein classification is responsible for the biological sequence, we came up with an idea whichdeals with the classification of proteomics using deep learning algorithm. This algorithm focusesmainly to classify sequences of protein-vector which is used for the representation of proteomics.Selection  of  the  type  protein  representation  is  challenging  based  on  which  output  in  terms  ofaccuracy is depended on, The protein representation used here is n-gram i.e.  3-gram and Kerasembedding used for biological sequences like protein. In this paper we are working on the Proteinclassification to show the strength and representation of biological sequence of the proteins Human body comprises of the many cells the key for the formation of all these are the DNA(Deoxyribonucleic acid) which is a chain of nucleotides that are thread like chain in nature responsible for carrying genetic instruction for development, functioning of organisms in the body and RNA(Ribonucleic acid) which is a polymeric molecule which is very much essential for the three biological roles namely CDRE(Coding, Decoding ,Regulation and Expression) of each and every gene present in the body, which are present in every living being. Like human beings use various language for communication, biological organisms use these type of the codes by using DNA and RNA for the communication. Selection of the type of feature extraction is a challenging task because it helps for the studying of the types of the genes to the machine Email address: naveensivakumarr@gmail.com (Naveenkumar K S) using the machine learning algorithm, Even a most highly sophisticated algorithm would go wrong if the feature extraction is not done in a proper form. The features from the existing data can be obtained by manually or by using unsupervised (data without labels) fashion BID0 , BID1 . This work focuses on protein family classification wit the publically avilable data Swiss-Prot BID2 . In this work application of keras embedding and n-gram technique is used to map the protein sequences into numeric vectors and followed by traditional machine learning and deep neural network (DNN) for classification.The rest of the paper are organized as follows. Section 2 discusses the related works in a detailed manner. Section 3 presents the background works. Section 4 presents the description of the data set. Section 5 gives a overview about the proposed architecture which is used in building this work. Section 6 and 7 presents results and conclusion, future work directions and discussions. This paper has proposed deep learning method for protein classification. To transform protein to numeric vector, the n-gram and Keras embedding representation is used. Deep learning method with Keras embedding has performed well in comparison to the n-gram with deep neural network. The main reason is due to the Keras embedding has the capability to preserver the sequential information among the protein sequences. Thus, the deep learning algorithms able to capture the optimal information from the syntactic and semantic information of Keras embedding vectors. The proposed methodology can be employed for other domain in biology tasks such as genomics, DNA classification. This is one of the significant directions towards future work.
Protein Family Classification using Deep Learning
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