Amrita Mathuriya, David A. Bader, Christine E. Heitsch, Stephen C. Harvey
The prediction of the correct secondary structures of large RNAs is one of the unsolved challenges of computational molecular biology. Among the major obstacles is the fact that accurate calculations scale as O(n4), so the computational requirements become prohibitive as the length increases. Existing folding programs implement heuristics and approximations to overcome these limitations. We present a new parallel multicore and scalable program called GTfold, which is one to two orders of magnitude faster than the de facto standard programs and achieves comparable accuracy of prediction. Development of GTfold opens up a new path for the algorithmic improvements and application of an improved thermodynamic model to increase the prediction accuracy. In this paper we analyze the algorithm's concurrency and describe the parallelism for a shared memory environment such as a symmetric multiprocessor or multicore chip. In a remarkable demonstration, GTfold now optimally folds 11 picornaviral RNA sequences ranging from 7100 to 8200 nucleotides in 8 minutes, compared with the two months it took in a previous study. We are seeing a paradigm shift to multicore chips and parallelism must be explicitly addressed to continue gaining performance with each new generation of systems. We also show that the exact algorithms like internal loop speedup can be implemented with our method in an affordable amount of time. GTfold is freely available as open source from our website.
{"title":"GTfold","authors":"Amrita Mathuriya, David A. Bader, Christine E. Heitsch, Stephen C. Harvey","doi":"10.1145/1529282.1529497","DOIUrl":"https://doi.org/10.1145/1529282.1529497","url":null,"abstract":"The prediction of the correct secondary structures of large RNAs is one of the unsolved challenges of computational molecular biology. Among the major obstacles is the fact that accurate calculations scale as O(n4), so the computational requirements become prohibitive as the length increases. Existing folding programs implement heuristics and approximations to overcome these limitations. We present a new parallel multicore and scalable program called GTfold, which is one to two orders of magnitude faster than the de facto standard programs and achieves comparable accuracy of prediction. Development of GTfold opens up a new path for the algorithmic improvements and application of an improved thermodynamic model to increase the prediction accuracy.\u0000 In this paper we analyze the algorithm's concurrency and describe the parallelism for a shared memory environment such as a symmetric multiprocessor or multicore chip. In a remarkable demonstration, GTfold now optimally folds 11 picornaviral RNA sequences ranging from 7100 to 8200 nucleotides in 8 minutes, compared with the two months it took in a previous study. We are seeing a paradigm shift to multicore chips and parallelism must be explicitly addressed to continue gaining performance with each new generation of systems. We also show that the exact algorithms like internal loop speedup can be implemented with our method in an affordable amount of time. GTfold is freely available as open source from our website.","PeriodicalId":339815,"journal":{"name":"Proceedings of the 2009 ACM symposium on Applied Computing - SAC '09","volume":"3 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132871210","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}
M. Dragone, David Lillis, R. Collier, G. M. P. O'Hare
Modern computing systems require powerful software frameworks to ease their development and manage their complexity. These issues are addressed within both Component-Based Software Engineering and Agent-Oriented Software Engineering, although few integrated solutions exist. This paper discusses a novel integration strategy, which builds upon both paradigms to address their shortcomings while leveraging their different characteristics to define a complete software framework.
{"title":"SoSAA","authors":"M. Dragone, David Lillis, R. Collier, G. M. P. O'Hare","doi":"10.1145/1529282.1529432","DOIUrl":"https://doi.org/10.1145/1529282.1529432","url":null,"abstract":"Modern computing systems require powerful software frameworks to ease their development and manage their complexity. These issues are addressed within both Component-Based Software Engineering and Agent-Oriented Software Engineering, although few integrated solutions exist. This paper discusses a novel integration strategy, which builds upon both paradigms to address their shortcomings while leveraging their different characteristics to define a complete software framework.","PeriodicalId":339815,"journal":{"name":"Proceedings of the 2009 ACM symposium on Applied Computing - SAC '09","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123727537","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}
Nowadays, many organizations have relational databases with millions of records and an important question is how to extract information from them. This work proposes HTILDE (Hoeffding TILDE) to handle very large relational databases, based on the Inductive Logic Programming (ILP) system TILDE (Top-down Induction of Logical Decision Trees) and the propositional Very Fast Decision Tree (VFDT) learner. It is an incremental and anytime algorithm that uses the Hoeffding bound to find out the amount of examples that must be considered for choosing the best test for a node. The results show that, compared to TILDE, HTILDE generates theories from very large relational datasets more efficiently without harming their quality measures (F-measure, precision, recall and accuracy). Also, HTILDE learns less complex theories than TILDE.
{"title":"HTILDE","authors":"C. Lopes, Gerson Zaverucha","doi":"10.1145/1529282.1529610","DOIUrl":"https://doi.org/10.1145/1529282.1529610","url":null,"abstract":"Nowadays, many organizations have relational databases with millions of records and an important question is how to extract information from them. This work proposes HTILDE (Hoeffding TILDE) to handle very large relational databases, based on the Inductive Logic Programming (ILP) system TILDE (Top-down Induction of Logical Decision Trees) and the propositional Very Fast Decision Tree (VFDT) learner. It is an incremental and anytime algorithm that uses the Hoeffding bound to find out the amount of examples that must be considered for choosing the best test for a node. The results show that, compared to TILDE, HTILDE generates theories from very large relational datasets more efficiently without harming their quality measures (F-measure, precision, recall and accuracy). Also, HTILDE learns less complex theories than TILDE.","PeriodicalId":339815,"journal":{"name":"Proceedings of the 2009 ACM symposium on Applied Computing - SAC '09","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114975775","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}
Reginaldo Mendes, Paulo F. Pires, Flávia Coimbra Delicato, T. Batista
This paper presents WebFlowAH, an environment for ad-hoc specifying and executing Web services-based business processes. WebFlowAH employs common domain ontology to describe both Web services and business processes. It allows specifying processes in terms of users' goals or desires that are expressed based on the concepts of such common domain ontology. This approach allows processes to be specified in an abstract high-level way, unburdening the user from dealing with underlying details needed to effectively run the process workflow.
{"title":"WebFlowAH","authors":"Reginaldo Mendes, Paulo F. Pires, Flávia Coimbra Delicato, T. Batista","doi":"10.1145/1529282.1529426","DOIUrl":"https://doi.org/10.1145/1529282.1529426","url":null,"abstract":"This paper presents WebFlowAH, an environment for ad-hoc specifying and executing Web services-based business processes. WebFlowAH employs common domain ontology to describe both Web services and business processes. It allows specifying processes in terms of users' goals or desires that are expressed based on the concepts of such common domain ontology. This approach allows processes to be specified in an abstract high-level way, unburdening the user from dealing with underlying details needed to effectively run the process workflow.","PeriodicalId":339815,"journal":{"name":"Proceedings of the 2009 ACM symposium on Applied Computing - SAC '09","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127922996","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：