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\title{IN-AIS-MACA: Integrated Artificial Immune System based Multiple Attractor Cellular Automata for Human Protein Coding and Promoter Prediction of 252bp Length DNA Sequence}
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\begin{document}

             \author[1]{Pokkuluri Kiran  Sree}

             \author[2]{Pokkuluri Kiran  Sree}

             \author[3]{Inampudi Ramesh  Babu}

             \affil[1]{  University College of Engineering}

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\date{\small \em Received: 12 December 2013 Accepted: 4 January 2014 Published: 15 January 2014}

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\begin{abstract}
        


Gene prediction involves protein coding and promoter predictions. There is a need of integrated algorithms which can predict both these regions at a faster rate. Till date, we have individual algorithms for addressing these problems. We have developed a novel classifier IN-AIS-MACA, which can predict both these regions in genomic DNA sequences of length 252bp with 93.5% accuracy and total prediction time of 1031ms. This classifier will certainly create intuition to develop more classifiers like this.

\end{abstract}


\keywords{IN-AIS-MACA, protein coding, promoter predictions, DNA sequences.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
NA contains lots of information. For DNA sequence to transcript and form RNA which copies the required information, we need a promoter. So promoter plays a vital role in DNA transcription. It is defined as "the sequence in the region of the upstream of the transcriptional start site (TSS)''?.\par
Identifying a new promoter in a DNA sequence will lead to find a new protein. If we identify the promoter region we can extract information regarding gene expression patterns, cell specificity and development. Promoters will regulate a gene expression. Some of the genetic diseases which are associated with variations in promoters are asthma, beta thalassemia and rubinsteintaybi syndrome. Promoter sequence can be used to control the speed of translation from DNA into protein. It is also used in genetically modified foods. 
\section[{II.}]{II.} 
\section[{Literature Review}]{Literature Review}\par
Steven Salzberg \hyperref[b6]{[7]} has used a decision tree algorithm for locating protein coding region. This algorithm is adoptable and can handle DNA sequences of length 54,108 and 162. P.Maji \hyperref[b7]{[8]} et al. has developed neural network tree classifier for prediction of splice junction and coding regions in genomic DNA. A decision tree named as NNTree (Neural Network Tree) is constructed by dividing the training set with their corresponding labels to recursively generates a tree. Ying Xu \hyperref[b8]{[9]} et al. has developed an improved system GRAIL II which is a hybrid AI system which can predict the number of exons in a human DNA sequence and also supports gene modeling. This process combines edge signal like accepter, donor, translation start site detection and coding feature analysis.\par
Eric E Snyder \hyperref[b9]{[10]} et al. has applied dynamic programming and neural networks for predicting protein coding regions from a genomic DNA. They have developed a program Gene Parser which first scores the DNA sequences based on exon-intron specific measures like local compositional complexity, codon usage, length distribution, 6-tuple frequency and periodic asymmetry. Edward C Uberbacher \hyperref[b10]{[11]} et al. has proposed a method which combines some set of sensor algorithms and neural network to predict the protein coding regions in eukaryotes. The programs developed will calculate the values of seven sensors that were considered by the authors. The measures are frame bias matrix, Fickett(three periodicity) , dinucleotide fractal dimension, coding six tuple word preferences, coding six tuple in frame preferences, word commonality and repetitive six tuple word preferences.\par
J. Pinho \hyperref[b11]{[12]} et al. has proposed a three state model for protein coding region prediction. Authors have considered three base periodicity property. M.Q. Zhang \hyperref[b12]{[13]} has used quadratic discriminant analysis method named as MZEF for identifying protein coding regions in genomic human DNA. David J. States \hyperref[b21]{[14]} at el. proposed a computer program named BLASTC which In vertebrates only five percentage of the gene is made up of exons. Genes mostly will have seven to eight exons with 145 bp length at an average. Introns have 3365 bp length at an average. Promoter comprises a small percentage of entire genome. The features of promoters are different from other functional regions like exons, introns and 3'UTRs. These facts make protein coding and promoter region predictions as very difficult tasks.\par
uses sequence similarity and codon utilization for predicting the protein coding regions.\par
Method \hyperref[b7]{[8]} takes more time to construct a tree for sequences of length 162. The height of the trees is also a major concern for using this algorithm with DNA sequences of more length. Method \hyperref[b8]{[9]} suffers with less accuracy due to more error rate at classifier nodes. Methods \hyperref[b9]{[10]}, \hyperref[b10]{[11]}, \hyperref[b11]{[12]} depends more on the statistical information. After this literature survey the concern of a new classifier is to achieve a good classifier accuracy and develop a classifier which can handle DNA sequences of length more than 162 with a fewer nodes. Jia Zeng \hyperref[b19]{[15]} et al. has proposed a hierarchical promoter prediction system named as SCS where they have used signal, structure and context features .Xiomeng Li \hyperref[b20]{[16]} et al. has proposed a method PCA-HPR (Principal Component Analysis-Human Promoter Recognition) to predict the promoters and transcription sites (TSS). Sridgar Hannenhalli \hyperref[b13]{[17]} et al. tried to enhance the accuracy of promoter prediction by combining CpG island feature with information of independent signals which are biologically motivated and these cover most of the knowledge to predict the promoter in human genome. Shuanhu Wu et al. have proposed a method \hyperref[b14]{[18]} for enhancing the performance of human promoter region identification by selecting most important features of DNA sequence for each different functional region.Uwe Ohler et al. have proposed a model \hyperref[b15]{[19]} which integrates physical properties of DNA into a probabilistic eukaryotic promoter prediction system.Goni J Ramon et al. has proposed a system ProStar \hyperref[b16]{[20]} which uses structural parameters for promoter region identification. Authors only used descriptors derived from physical first principles.\par
Vladimir B. Bajic \hyperref[b17]{[21]} et al. has developed new software for identifying promoters in a DNA sequence of vertebrates. This program takes input as DNA sequence and generates a list of predicted TSS (Transcription Stating Site).Michael Q.Zhang \hyperref[b18]{[22]} has proposed a new program for predicting a core promoter in human gene named as CorePromoter. After the literature survey on promoter prediction, the main goal of proposed classifier is to reduce the false prediction rates and improve specificity and sensitivity values.  Human promoter data sets are collected from DBTSS database consist of 30,966 of length 251. We have used 7,741 for constructing an IN-AIS-MACA tree and 7,741 for checking the accuracy of the tree. Rest of the 15,483 promoter sequences are used for testing the proposed classifier. 
\section[{III.}]{III.} 
\section[{Design of In-Ais-Maca}]{Design of In-Ais-Maca}\par
Human non-promoter data sets are collected from EID and UTRdb databases. We have extracted 75,438 exons from EID database, where 18,859 are used for constructing an IN-AIS-MACA tree and 18,860 data components are used for checking the accuracy of the constructed tree. Rest of 37,719 data components are used for testing the classifier. We have extracted 53,684 introns from EID database, where 13,421 are used for constructing the tree and 13,421 are used for checking the accuracy of the constructed tree, rest of the 26,842 are used for testing the classifier. We have extracted 80,538 3'UTRs from UTR dB. In that \hyperref[b17]{21} No information regarding the reading frame is used in our study. We are going to predict both regions where nothing is known. Each window should belong to a single class (promoter/non-promoter, coding /non-V. Step 6: Store the basins (Be, Bi, Bu, Bp, Bpr). 
\section[{Learning of In-Ais-Maca}]{Learning of In-Ais-Maca}\par
Step 7: Repeat the steps 1 to 6 till the completion of input or individual attractor basins count is 6.\par
Step 8: Stop Where Be represents the exon basins, Bi represents the intron basins, Bu represents the 3'UTR basins, Bp represents the promoter basins and Bpr represents the protein coding region basins. 
\section[{VI.}]{VI.} 
\section[{Testing of In-Ais-Maca}]{Testing of In-Ais-Maca}\par
The accuracy of protein coding region prediction with IN-AIS-MACA depends on the accuracy of exon prediction. As the promoter prediction module has reported 96.5\% accuracy, the protein coding region prediction accuracy gets improved. The main aim of this algorithm is to process the DNA sequence based on the features and distribute it into any one of the basin. 
\section[{Algorithm:}]{Algorithm:}\par
Input: DNA Sequence Output: Class of the sequence Step 1: Read the DNA sequence in the multiples of three.\par
Step 2: Encode the sequence in the multiples of three Step 3: Extract the features Step 4: Check whether the input belongs to EXON class, if not, go to step 6. If it is found as EXON report the corresponding class and boundary.\par
Step 5: (a) Read the encoded DNA sequence starting with the upper bound to the end of the string.  
\section[{Data Sets and Methods}]{Data Sets and Methods} 
\section[{Global Journal of Computer Science and Technology}]{Global Journal of Computer Science and Technology}\par
Volume XIV Issue II Version I Step 6: Check whether the sequence belongs to intron, 3'UTR or promoter. 6a) Choose the best fitness rule to direct the sequence to the attractor basins of Bi,Bu,Bp 6c) Report the boundaries and respective class.\par
Step 7: Stop. 
\section[{VII.}]{VII.} 
\section[{Output \& Experimental Results of In-Ais-Maca}]{Output \& Experimental Results of In-Ais-Maca}\par
The output1 shown below is a DNA sequence of length 252bp. The output of promoter prediction has indicated initial exon at 30 to 64. So the protein coding interface starts its processing from 64 to 251.The next internal and terminal exons are reported in both the strands.\par
Output  
\section[{Comparison of the Performance of In-Ais-Maca}]{Comparison of the Performance of In-Ais-Maca}\par
IN-AIS-MACA uses the strength of existing AIS-PRMACA design to predict both PR \& PCR regions. The accuracy, Se, Sp and execution time of PR prediction with IN-AIS-MACA is same as of AIS-PRMACA reported in chapter 6. So we report the accuracy, Se and Sp of predicting PCR using this IN-AIS-MACA. The important challenge of IN-AIS-MACA is to reduce the total prediction time (TPT) of both PCR and PR which will be discussed in this section.\par
The performance of IN-AIS-MACA is measured with Se,Sp and accuracy as shown in table 1. We have extended the DT and NNtree to accommodate 252 length DNA sequences and compared the results with them. IN-AIS-MACA reports a high sensitivity, specificity, accuracy of 0.934, 0.925 and 0.93 respectively. This improved performance, when compared with AIS-MACA prediction for 252bp length DNA sequence is due to the classifier accuracy of AI-PRMACA. 
\section[{Table 1 : IN-AIS-MACA Performance in PCR prediction}]{Table 1 : IN-AIS-MACA Performance in PCR prediction}\par
If the accuracy of AIS-PRMACA to predict the first exon is more, then the accuracy of predicting the PCR with IN-AIS-MACA is more. The accuracy of AIS-PRMACA prediction of exon is 94.5\%, so there is a considerable improvement of PCR prediction with IN-AIS-MACA particularly in the 252bp length DNA sequences. IN-AIS-MACA maintains good balance between Se and Sp, Se+Sp ie 1.859. The performance of a decision tree in processing lengths of 252bp is poor due to the height of the tree build for predicting the PCR is more. Decision tree reports an accuracy of 86.5\%. NNtree performs better compared with DT reports 87.3\% accuracy. Performance of both classifiers suffers when processing a DNA sequence of length more than 162.  
\section[{IX. Execution Time Comparisons with In-Ais-Maca}]{IX. Execution Time Comparisons with In-Ais-Maca}\par
The aim of IN-AIS-MACA is to predict both PCR and PR in human DNA sequence of length 252bp. Since this is the first algorithm to handle predictions of both regions, we have chosen better algorithms in combination , to report the corresponding execution times of individual predictions and total predictions. In the first combination we have used classifiers AIS-MACA   
\section[{IN-AIS-MACA Se,Sp,Accuracy Comparison}]{IN-AIS-MACA Se,Sp,Accuracy Comparison} 
\section[{Table 2 : IN-AIS-MACA total prediction time comparison}]{Table 2 : IN-AIS-MACA total prediction time comparison}\par
In the fourth combination we have used classifiers decision tree and AIS-PRMACA which reports the total prediction time of 1930 ms. In the fifth combination we have used classifiers NNtree and AIS-PRMACA which reports the total prediction time of 1897 ms. In the sixth combination we have used classifiers dicodon usage and AIS-PRMACA which reports the total prediction time of 1897 ms. The proposed classifier IN-AIS-MACA reports a total prediction time of 1031ms which is best among all the reported classifiers in table 2 and figure 3. Identifying both PCR and PR with a minimum execution time leads to a faster gene prediction.  For achieving higher accuracies with IN-AIS-MACA to predict protein coding regions and promoter regions, we have to analyze three important parameters. The first parameter is the number of generations. We have to extract higher accuracies with lesser generations. Figure 4 shows that the minimum number of generations that required to achieve a higher accuracy for PCR prediction is 75.   
\section[{Conclusion}]{Conclusion}\par
We have successfully developed an integrated classifier which can predict both protein coding and promoter regions in human DNA of length 252bp. IN -AIS-MACA reports a Sensitivity (Se) of 0.934 ,Specificity(Sp) of 0.925 and accuracy of 93\% which makes this as the best algorithm for predicting both PCR and PR. The important contribution of this classifier lies in predicting both these regions with an execution time of 1031ms, which will faster the gene perdition rate. \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 1 :}\end{figure}
          \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.04072334429309535\textwidth}P{0.5437764208548614\textwidth}P{0.2171911695631752\textwidth}P{0.033137623297322685\textwidth}P{0.015171441991545326\textwidth}}
\tabcellsep VIII.\tabcellsep \tabcellsep \tabcellsep \\
Year 2014\tabcellsep \multicolumn{4}{l}{1: DNA Sequence GAATTCTTGTTGAGAAGGAATTGGGCTCAATGAAGTTCGGGGATATTCCAAGTGAATTATTCCAGTGAGTGTTATTCAG CAATGGACGTGACTGTCGTTTGCCAGATCAGCAGAAGCCGAAAGGAATCCTTTCGGCTTCTGCTGATCTGGCAAAC}\\
4\tabcellsep \multicolumn{4}{l}{GACAGTCACGTCCATTGCTGAATAACACTCACTGGAATAATTCACTTGGAATATCCCCGAACTTCATTGAGCCCAATT CCTTCTCAACAAGAATTC}\\
Volume XIV Issue II Version I ( D D D D ) G\tabcellsep \multicolumn{4}{l}{\# Sequence Kiran\textunderscore 63jntuh Length = 252 bp Sequence Kiran\textunderscore 63jntuh, Start End Score 30 64 0.61 Sequence Name Program ATGAAGTTCGGGGATATTCCAAGTGAATTATTCC Human Promoter Prediction Non Promoter Sequence/Exon Type of Exon Boundary Strand Kiran\textunderscore 63jntuh IN-AIS-MACA First 82 189 + Kiran\textunderscore 63jntuh IN-AIS-MACA First 82 207 + Kiran\textunderscore 63jntuh IN-AIS-MACA First 82 222 +}\\
Global Journal of Computer Science and Technology\tabcellsep Kiran\textunderscore 63jntuh Kiran\textunderscore 63jntuh Kiran\textunderscore 63jntuh Kiran\textunderscore 63jntuh Kiran\textunderscore 63jntuh Kiran\textunderscore 63jntuh Kiran\textunderscore 63jntuh Kiran\textunderscore 63jntuh Kiran\textunderscore 63jntuh Kiran\textunderscore 63jntuh Kiran\textunderscore 63jntuh Kiran\textunderscore 63jntuh Kiran\textunderscore 63jntuh Kiran\textunderscore 63jntuh Kiran\textunderscore 63jntuh Kiran\textunderscore 63jntuh\tabcellsep IN-AIS-MACA IN-AIS-MACA IN-AIS-MACA IN-AIS-MACA IN-AIS-MACA IN-AIS-MACA IN-AIS-MACA Internal 53 First 198 First 198 First 198 First 198 First 198 First 198 IN-AIS-MACA Internal 66 IN-AIS-MACA Internal 80 IN-AIS-MACA Internal 80 IN-AIS-MACA Internal 80 IN-AIS-MACA Internal 106 IN-AIS-MACA Internal 106 IN-AIS-MACA Terminal 106 IN-AIS-MACA Terminal 106 IN-AIS-MACA Terminal 111\tabcellsep 207 214 222 226 232 207 222 87 199 207 222 132 207 136 197 136\tabcellsep + + + + + + + + + + + + + + + +\\
\tabcellsep Kiran\textunderscore 63jntuh\tabcellsep IN-AIS-MACA Terminal 111\tabcellsep 197\tabcellsep +\\
\tabcellsep Kiran\textunderscore 63jntuh\tabcellsep IN-AIS-MACA Terminal 167\tabcellsep 193\tabcellsep +\\
\tabcellsep Kiran\textunderscore 63jntuh\tabcellsep IN-AIS-MACA Terminal 167\tabcellsep 197\tabcellsep +\\
\tabcellsep Kiran\textunderscore 63jntuh\tabcellsep IN-AIS-MACA Internal 151\tabcellsep 249\tabcellsep -\\
\tabcellsep Kiran\textunderscore 63jntuh\tabcellsep IN-AIS-MACA Internal 151\tabcellsep 249\tabcellsep -\\
\tabcellsep Kiran\textunderscore 63jntuh\tabcellsep IN-AIS-MACA Internal 130\tabcellsep 249\tabcellsep -\\
\tabcellsep Kiran\textunderscore 63jntuh\tabcellsep IN-AIS-MACA Terminal 194\tabcellsep 249\tabcellsep -\end{longtable} \par
 
\caption{\label{tab_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.29725433526011563\textwidth}P{0.09335260115606936\textwidth}P{0.1449421965317919\textwidth}P{0.11546242774566473\textwidth}P{0.034393063583815026\textwidth}P{0.0565028901734104\textwidth}P{0.06878612716763005\textwidth}P{0.039306358381502884\textwidth}}
\multicolumn{2}{l}{Kiran\textunderscore 63jntuh}\tabcellsep \multicolumn{4}{l}{IN-AIS-MACA Terminal 76}\tabcellsep 249\tabcellsep -\\
\multicolumn{2}{l}{Kiran\textunderscore 63jntuh}\tabcellsep \multicolumn{4}{l}{IN-AIS-MACA Terminal 72}\tabcellsep 249\tabcellsep -\\
\multicolumn{2}{l}{Method}\tabcellsep Se\tabcellsep Sp\tabcellsep \tabcellsep Se+Sp\tabcellsep Accuracy\\
\multicolumn{3}{l}{IN-AIS-MACA}\tabcellsep 0.934\tabcellsep \multicolumn{2}{l}{0.925}\tabcellsep 1.859\tabcellsep 0.93\\
\multicolumn{3}{l}{Decision Tree}\tabcellsep 0.851\tabcellsep \multicolumn{2}{l}{0.879}\tabcellsep 1.73\tabcellsep 0.865\\
\multicolumn{3}{l}{Neural Network}\tabcellsep 0.876\tabcellsep \multicolumn{2}{l}{0.87}\tabcellsep 1.746\tabcellsep 0.873\\
Tree\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 0.96\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 0.94\tabcellsep \tabcellsep \tabcellsep \\
Se, Sp , Accuracy\tabcellsep 0.84 0.86 0.88 0.9 0.92\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 0.82\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 0.8\tabcellsep IN-AIS-MACA\tabcellsep \multicolumn{2}{l}{Decision Tree}\tabcellsep Neural Network\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Standared Methods}\tabcellsep Tree\end{longtable} \par
 
\caption{\label{tab_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.17393822393822395\textwidth}P{0.33638996138996136\textwidth}P{0.041023166023166024\textwidth}P{0.09517374517374518\textwidth}P{0.10337837837837838\textwidth}P{0.1000965250965251\textwidth}}
\tabcellsep \multicolumn{3}{l}{X. Parameters Manipulation for Higher}\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{Accuracies of In-Ais-Maca}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep Execution\tabcellsep Execution\tabcellsep Total\\
\tabcellsep \multicolumn{2}{l}{Method}\tabcellsep time to predict PCR\tabcellsep time to predict PR\tabcellsep Prediction Time(TPT)\\
\tabcellsep \tabcellsep \tabcellsep (ms)\tabcellsep (ms)\tabcellsep (ms)\\
Year 2014\tabcellsep \multicolumn{2}{l}{IN-AIS-MACA AIS-MACA \& AIS+PRMACA}\tabcellsep 1031 796\tabcellsep 1031 1031\tabcellsep 1031 1827\\
6\tabcellsep \multicolumn{2}{l}{AIS-MACA \& SCS}\tabcellsep 796\tabcellsep 1121\tabcellsep 1917\\
Volume XIV Issue II Version I\tabcellsep \multicolumn{2}{l}{AIS-MACA \& McPromoter DT \& AIS-PRMACA NNTree \& AIS-PRMACA Dicodon Usage \& AIS-PRMACA}\tabcellsep 796 899 866 956\tabcellsep 1025 1031 1031 1031\tabcellsep 1821 1930 1897 1987\\
D D D D ) G\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
(\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Total Global Journal of Computer Science and Technology}\tabcellsep 0 500 1000 1500 2000 2500\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Prediction Time ms Methods in Combination Total Time to predict both regions (ms)}\end{figure}
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