2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)最新文献_第4页

A particle swarm optimization solution for challenging planted(l, d)-Motif problem 挑战性种植(1,d)-Motif问题的粒子群优化解

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2013-04-16 DOI: 10.1109/CIBCB.2013.6595413

Srinivasulu Reddy, M. Arock, A. V. Reddy

{"title":"A particle swarm optimization solution for challenging planted(l, d)-Motif problem","authors":"Srinivasulu Reddy, M. Arock, A. V. Reddy","doi":"10.1109/CIBCB.2013.6595413","DOIUrl":"https://doi.org/10.1109/CIBCB.2013.6595413","url":null,"abstract":"In Bioinformatics, Planted (l, d)-Motif finding is an important and challenging problem, which has many applications. Generally, it is to locate recurring patterns in the promoter regions of co-expressed or co-regulated genes. As we can't expect the pattern to be exact matching copies owing to biological mutations, the motif finding turns to be an NP-complete problem. By approximating the same in different aspects, scientists have provided many solutions in the literature. These solutions are either “exact” or “approximate”. All the proposed exact solutions take exponential-time; they need more time to search for larger parameters l and d. The problems of Bioinformatics seldom need the exact optimum solution; rather what they need is robust, fast and near optimal solutions. Therefore, it is impractical to use an exact algorithm to search for large parameters of motifs in real biological dataset. In this paper, we have adopted the features of the Particle Swarm Optimization (PSO) with k-nearest neighbor algorithm to solve the Planted (l, d)-Motif Finding Problem. PSO is a global approximation optimization technique and has wide applications. It finds the global best solution by simply adjusting the trajectory of each individual towards its own best location and towards the best particle of the swarm at each generation. We have performed some experiments on synthetic data by increasing number of sequences and the length of the sequences for different (l, d)-Motifs for the following data sets: general instances (10, 2), (11, 2), (12, 3), (15, 4), (16, 5), (18, 6), (20, 7) (30, 11) and (40,15). Challenging instances: (9, 2), (11, 3), (13, 4), (15, 5), (20, 7), (30, 11), (40, 15) and finally, we have applied our proposed method for real biological sequences. From the experimental results we observe that the proposed algorithm is more efficient and accurate compared to existing approximation algorithms and even it works better for larger motif instances.","PeriodicalId":350407,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126954233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

GAknot: RNA secondary structures prediction with pseudoknots using genetic algorithm GAknot:基于遗传算法的假结RNA二级结构预测

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2013-04-16 DOI: 10.1109/CIBCB.2013.6595399

Kwok-Kit Tong, Kwan-Yau Cheung, Kin-Hong Lee, K. Leung

{"title":"GAknot: RNA secondary structures prediction with pseudoknots using genetic algorithm","authors":"Kwok-Kit Tong, Kwan-Yau Cheung, Kin-Hong Lee, K. Leung","doi":"10.1109/CIBCB.2013.6595399","DOIUrl":"https://doi.org/10.1109/CIBCB.2013.6595399","url":null,"abstract":"Predicting RNA secondary structure is a significant challenge in Bioinformatics especially including pseudoknots. There are so many researches proposed that pseudoknots have their own biological functions inside human body, so it is important to predict this kind of RNA secondary structures. There are several methods to predict RNA secondary structure, and the most common one is using minimum free energy. However, finding the minimum free energy to predict secondary structure with pseudoknots has been proven to be an NP-complete problem, so there are many heuristic approaches trying to solve this kind of problems. In this paper, we propose GAknot, a computational method using genetic algorithm (GA), to predict RNA secondary structure with pseudoknots. GAknot first generates a set of maximal stems, and then it tries to generate several individuals by different combinations of stems. After halting condition is reached, GAknot will output the best solution as the output of predicted secondary structure. By using two commonly used validation data sets, GAknot is shown to be a better prediction method in terms of accuracy and speed comparing to several competitive prediction methods. Source code and datasets can be downloaded.","PeriodicalId":350407,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129994107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Early lung cancer detection using nucleus segementation based features 基于核分割特征的早期肺癌检测

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2013-04-16 DOI: 10.1109/CIBCB.2013.6595393

K. Kancherla, Srinivas Mukkamala

引用次数: 21

Intellectual property protection for bioinformatics and computational intelligence 生物信息学和计算智能的知识产权保护

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2013-04-16 DOI: 10.1109/CIBCB.2013.6595385

D. Fernandez, Antonia Maninang, Shumpei Kobayashi

引用次数: 2

Managing memory and reducing I/O cost for correlation matrix calculation in bioinformatics 生物信息学中相关矩阵计算的内存管理和I/O成本降低

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2013-04-15 DOI: 10.1109/CIBCB.2013.6595386

A. P. D. Krishnajith, W. Kelly, R. Hayward, Yu-Chu Tian

{"title":"Managing memory and reducing I/O cost for correlation matrix calculation in bioinformatics","authors":"A. P. D. Krishnajith, W. Kelly, R. Hayward, Yu-Chu Tian","doi":"10.1109/CIBCB.2013.6595386","DOIUrl":"https://doi.org/10.1109/CIBCB.2013.6595386","url":null,"abstract":"The generation of a correlation matrix from a large set of long gene sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. The generation is not only computationally intensive but also requires significant memory resources as, typically, few gene sequences can be simultaneously stored in primary memory. The standard practice in such computation is to use frequent input/output (I/O) operations. Therefore, minimizing the number of these operations will yield much faster run-times. This paper develops an approach for the faster and scalable computing of large-size correlation matrices through the full use of available memory and a reduced number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different problems with different correlation matrix sizes. The significant performance improvement of the approach over the existing approaches is demonstrated through benchmark examples.","PeriodicalId":350407,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116173774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5