Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Motif Discovery Problem

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1 Sensors & Transducers 204 by IFSA Publishing S. L. Hybridizing Adaptive Biogeography-Based Optiization with Differential Evolution for Motif Discovery Proble 2 Si-Ling FENG Qing-Xin ZHU 2 Sheng ZHONG 3 Xiu-Jun GONG School of Coputer Science & Engineering University of Electronic Science & Technology of China Chengdu China 2 College of Inforation Science & Technology Hainan University Haikou China 3 School of Coputer Science and Technology Tianjin University Tianjin China Tel.: E-ail: fengsiling2008@63.co Received: 4 Noveber 203 /Accepted: 9 January 204 /Published: 3 January 204 Abstract: The coputational discovery of DNA otifs for previously uncharacterized transcription factors in groups of co-regulated genes is a well-studied proble with a great deal of practical relevance to the biologist. In this paper we applied an iproved hybridization of adaptive Biogeography-Based Optiization (ABBO) with differential evolution (DE) approach naely ABBO/DE/GEN to predict otifs fro DNA sequences. ABBO/DE/GEN adaptively changes igration probability and utation probability based on the relation between the cost of fitness function and average cost every generation and the utation operators of BBO are odified based on DE algorith and the igration operators of BBO are odified based on nuber of iteration to eet otif discovery requireents. Hence it can generate the proising candidate solutions. Statistical coparisons with soe typical existing approaches on three coonly used datasets are provided which deonstrates the validity and effectiveness of the ABBO/DE/GEN algorith. Copared with BBO/DE/GEN approaches ABBO/DE/GEN perfors better or at least coparably in ters of the quality of the final solutions. Copyright 204 IFSA Publishing S. L. Keywords: Adaptive biogeography-based optiization Differential evolution Motif discovery proble.. Introduction The Motif Discovery Proble (MDP) is applied to the specific task of discovering novel Transcription Factor Binding Sites (TFBS) in deoxyribonucleic acid (DNA) sequences []. The study of MDP has not only specific biological eaning to regulate the transcriptional activity of genes but also helps us to clarify the evolutionary relationship between sequences and understand the functions of sequence regulations. However the otifs are relative short recurring conservative patterns in the regulatory regions of DNA. This leads to MDP a coputational non-trivial task thus any research efforts have been devoted to this hot topic in the fields of bioinforatics counities. Biogeography-based optiization (BBO) algorith (Sion 2008) [2] is based on the echanis of the species igrating fro one island to another in nature. Based on two ain operators igration and utation BBO is of good exploitation ability. However it is slow exploring of the search space. On the other hand DE is good at exploring the Article nuber P_

2 search space. In order to balance the exploration and the exploitation of BBO a hybrid BBO approach called BBO/DE/GEN is proposed for MDP in [3]. Experient results illustrate effectiveness of BBO/DE/GEN. In order to iprove perforance of BBO/DE/GEN ABBO/DE/GEN is proposed and shows good perforance at test functions in [4]. So in this paper we attept to solve otif discovery proble by hybridizing adaptive BBO with DE algorith naed ABBO/DE/GEN algorith. In this paper we proposed an ABBO/DE/GEN algorith which incorporates the utation procedure inherited fro DE to replace the BBO-based utation for solving otif discovery proble. And a new igration operator is proposed based on nuber of iteration to iprove perforance. And ABBO/DE/GEN adaptively changes igration probability and utation probability based on the relation between the cost of fitness function and average cost every generation to predict otifs fro DNA sequences. Statistical coparisons with soe typical existing approaches on three coonly used datasets are provided which deonstrates the validity and effectiveness of the proposed iproved adaptively hybrid algorith for otif discovery probles. The reainder of the paper is organized as follows. The hybrid algorith for the otif discovery probles is proposed in Section 2. Experiental results and Coparisons with soe existing approaches are provided in Section 3. Finally we draw the conclusions in Section ABBO/DE/GEN for MDP In this paper we attept to solve otif discovery proble by ABBO/DE/GEN algorith. Objective function of otif discovery proble and Main procedure of ABBO/DE/GEN algorith for MDP are introduced as follows. 2.. Objective Function of Motif Discovery Proble For a set of N sequences S={S S 2 S N } each of which is fored by nucleotides fro the finite alphabet Σ={ACGT} where the length of each sequence is l the otif discovery is to find a set of instances M={ 2 n } (n N) where i is a subsequence of length w fro sequence S i. For the purpose of explore otifs we use a total fitness score function which is siilar to [5] we also do soe odification to adapt to our BBO algorith and describe it below [6]. So we consider the fitness score of one single sequence defined as follows: k FS ( S Pn ) ax j i atch ( S Pni ) 0 w atch ( S Pni ) L / w / w Pni P if if S S ni () where S is the sequence sets of DNA P is the population sets of algoriths is the index of sequences n is the index of otif patterns w is the length of otif pattern P and L is the su value of the nuber of all the continuous isatched segents inus one it reflects the ultiple sequence alignent phenoena biologically. The total fitness score (TFS) function of an individual is the suation of fitness score function for all sequences. We establish the total fitness score function as follows. Clearly the bigger is the value of TFS the better is otif pattern. FS( Sl Pn ) TFS( S P l n ) 2.2. Migration Operator for MDP (2) In BBO there are two ain operators: igration and utation. Modified igration operator is a generalization of the standard BBO igration operator and which is otivated fro [7]. Modified igration is defined as t t Hi ( s) Hi ( s) ( ) Hj( s) (3) tax tax where H i is the i th candidate solution in the BBO population s is its solution feature and t is nuber of iteration t ax is axiu nuber of iteration. Eq. (3) eans that a solution feature of solution H i is coprised of two coponents: a feature fro another solution and a feature fro itself Mutation Operator for MDP In BBO if a solution is selected for utation then it is replaced by a randoly generated new solution set. This rando utation affects the exploration ability of BBO. In MDBBO odified utation operator creates new feasible solution by inheriting fro DE to replace the BBO-based utation. A utated individual SIV ( H i ( ) is generated according to the following equation Hi( Hi( F *( Hbest( Hi( ) F *( H ( H ( )) r r2 j (4) where H i ( is the parent SIV to be utated F is the utation scaling factor. Hr( Hr2( is the randoly selected SIV ( r [ popsize] ). In MDBBO this utation schee tends to increase the diversity aong the population. It acts as a fine tuning unit and accelerates the convergence characteristics of the original algorith. 234

3 2.4. Adaptive BBO There are odification probability and utation probability factors in BBO. The two factors which range fro 0 to are defined by users. When generated rando nuber is less than odification probability the progra executes igration operator; when generated rando nuber is less than utation probability the progra executes utation operator. In adaptive BBO algorith odification probability and utation probability are odified in ter of the relation of the cost of fitness function generated last generation to average cost. In other words if the cost of fitness function is equal or greater than average cost odification probability and utation probability are odified by equation (5) and equation (6) respectively. Otherwise odification probability and utation probability are not changed. In equation (5) and equation (6) constant factor k and k3 which range fro 0 to are defined by users. Podification k* (MaxCost - fitnesscos t) /(MaxCost - AvgCost) (5) P utation k3 * (MaxCost - fitnesscos t) /(MaxCost - AvgCost) 2.5. Main Procedure of ABBO/DE/GEN for MDP (6) By incorporating the above-entioned igration operator and utation operator into BBO and odification probability and utation probability are odified in ter of the relation of the cost of fitness function to average cost generated every generation. The ABBO/DE/GEN approach is developed and shown in Algorith. ABBO/DE/GEN is able to explore the new search space with the utation operator of DE and to exploit the population inforation with the igration operator of BBO. This feature overcoes the lack of exploration of the original BBO algorith. Note that the difference between ABBO/DE/GEN and the BBO/DE/GEN algorith is that odification probability and utation probability are adaptively odified. Our proposed ABBO/DE/GEN approach for MDP is described as follows. Algorith : The ain procedure of ABBO/DE/GEN for MDP. Input: The Sequences S Output: The Best Motif Instance and corresponding Total Fitness Score (TFS). Generate the initial a set of solutions to MDP. Modification probability utation probability elitis paraeter nuber of iterations 2. Evaluating the fitness function for each candidate otif by forula (2). 3. While the halting criterion is not satisfied do 4. For each individual ap the fitness to the nuber of species 5. Calculate the iigration rate i and the eigration rate i for each individual X i 6. Modify the population with the igration operator by Eq Modify the population with the utation operator by Eq Evaluating the fitness function 9. Sort the population fro best to worst 0. Replace the worst with the previous generation's elites.. Clear any duplicates by randoly population utation. 2. Evaluating axiu cost iniu cost and average cost of the population 3. Modify the Modification probability and utation probability based on section End while 3. Experients We have tested ABBO/DE/GEN for MDP on three real sequence datasets which were selected fro TRANSFAC database [8]. A dataset consists of sequences with otif instances already tagged. Therefore we can use these datasets as a benchark for the discovery of TBFSs. Besides we assue the widths of the otifs are known beforehand. We say a otif instance is correctly discovered if the predicted binding site is within 5 bp away fro the true binding site. To easure the perforance of ABBO/DE/GEN for MDP and other algoriths we adopt the standard etrics of Precision Recall and F score as defined in Eq. 7 [9] where the operator is the cardinality of the set. After we find the candidate instances coputationally the results need to be verified in biological experients. We hope for a high Precision and a high Recall. F score ixes Precision and Recall since there is a tradeoff between Precision and Recall. 235

4 The three real datasets are h03r us02r and yst08r. The benchark datasets are shown in Table. We have run ABBO/DE/GEN for MDP for each dataset 30 ties with different rando seeds. The population size is 200 and the axial generation G is 50. The average results in the 30 runs are recorded. We have copared the perforance of ABBO/DE/GEN for MDP to 2 different algoriths for MDP such as MEME and AlignACE etc. The results are reported by Tables 2. Table 2 shows the average results of the twelve algoriths in 30 runs. According to the F-score ABBO/DE/GEN is the best on h03r and us02r dataset and it is better than BBO/DE/GEN on h03r and us02r dataset however it is worse than MEME MEME3 and MOTIFSAMPLE on yst08r. The experients deonstrate the validity of the proposed iproved hybrid ABBO/DE algorith for otif discovery probles. correctotif correctotif Precision Recall otif found true otif Precision*Recall F score 2* Precision Recall Table. The setting of the benchark datasets. dataset #sequence length #Width of otifs #instance h03r us02r yst08r (7) Table 2. coparisons of BBO/DE and AlignACE MEME etc. on the three datasets: average results (precisions(p) recalls(r) and F-scores(F). Algoriths for MDP Dataset dataset Algoriths for MDP H03 Mu02 Yst08 H03 Mu02 Yst08 P 0/25 /2 0/ P 0/4 0/0 9/4 YMF R 0/5 /2 0/4 AlignACE R 0/5 0/2 9/4 F F P /0 0/9 0/2 P /2 2/4 6/ SeSiMCMC R /5 0/2 0/4 MEME R /5 2/2 6/4 F F P 0/22 /22 3/56 P 0/2 /8 7/9 QuickScore R 0/5 /2 3/4 MOTIFSAMPLE R 0/5 /2 7/4 F F P 0/0 0/9 /2 P 0/3 /32 7/26 MITRA R 0/5 0/2 /4 ANN-SPEC R 0/5 /2 7/4 F F P /20 0/8 /22 P 0/7 0/0 9/7 Iprobizer R /5 0/2 /4 MEME3 R 0/5 0/2 9/4 F F P 3/30 4/30 8/30 P 5/30 5/30 8/30 BBO/DE/GEN R 3/5 4/2 8/4 ABBO/DE/GEN R 5/5 5/2 8/4 F F Conclusions In this paper we proposed an ABBO/DE/GEN algorith to predict otifs fro DNA sequences which incorporates the utation procedure inherited fro DE to replace the BBO-based utation and a new igration operator is proposed based on nuber of iteration to eet otif discovery requireents. And ABBO/DE/GEN algorith adaptively changes igration probability and utation probability based on the relation between the cost of fitness function and average cost every generation Statistical coparisons with soe typical existing approaches on three coonly used datasets are provided; the experients have verified that ABBO/DE/GEN algorith outperfors BBO/DE/GEN algoriths on h03r and us02r dataset. It deonstrates the validity and effectiveness of the proposed iproved hybrid algorith for otif discovery probles. Acknowledgeents This research was partially supported by the NSFC (67077). References []. Patrik D'haeseleer What are DNA sequence otifs? Nature Biotechnology Vol. 24 Issue pp [2]. D. Sion Biogeography-based optiization IEEE Transactions on Evolutionary Coputing Vol. 2 Issue pp

5 [3]. S. L. Feng Q. X. Zhu X. J. Gong and S. Zhong Hybridizing biogeography-based optiization with differential evolution for otif discovery proble ICIC Express Letters Vol.7 No pp [4]. S. L. Feng Q. X. Zhu X. J. Gong and S. Zhong Hybridizing adaptive biogeography-based optiization with differential evolution for global nuerical optiization in Proceedings of the 2 nd International Conference on Advanced in Control Engineering and Inforation Science (CEIS 203) 203 pp [5]. F. M. Liu FMGA: finding otifs by genetic algorith in Proceedings of the IEEE Fourth Syposiu on Bioinforatics and Bioengineering (BIBE 04) 2004 pp [6]. Wang Tieqi Qiu Dehua and Hu Guiwu Migration particle swar optiization enseble and its application for otif detection Journal of Hengyang Noral University Vol. 29 No [7]. H. Ma & D. Sion Blended biogeography-based optiization for constrained optiization Engineering Applications of Artificial Intelligence Vol. 24 Issue 3 20 pp [8]. M. Topa N. Li T. Bailey G. Church B. De Moor et al. Assessing coputational tools for the discovery of transcription factor binding sites Nature Biotechnology No pp [9]. Gang Li Tak-Ming Chan Kwong-Sak Leung et al. A cluster refineent algorith for otif discovery IEEE Transactions on Coputational Biology and Bioinforatics Vol. 7 Issue pp Copyright International Frequency Sensor Association (IFSA) Publishing S. L. All rights reserved. ( 237