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RSMD-repeat searcher and motif detector
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作者 Udayakumar Mani Vaidhyanathan Mahaganapathy +1 位作者 Sadhana Ravisankar Sai Mukund Ramakrishnan 《The Journal of Biomedical Research》 CAS 2014年第5期416-422,共7页
The functionality of a gene or a protein depends on codon repeats occurring in it.As a consequence of their vitality in protein function and apparent involvement in causing diseases,an interest in these repeats has de... The functionality of a gene or a protein depends on codon repeats occurring in it.As a consequence of their vitality in protein function and apparent involvement in causing diseases,an interest in these repeats has developed in recent years.The analysis of genomic and proteomic sequences to identify such repeats requires some algorithmic support from informatics level.Here,we proposed an offline stand-alone toolkit Repeat Searcher and Motif Detector(RSMD),which uncovers and employs few novel approaches in identification of sequence repeats and motifs to understand their functionality in sequence level and their disease causing tendency.The tool offers various features such as identifying motifs,repeats and identification of disease causing repeats.RSMD was designed to provide an easily understandable graphical user interface(GUI),for the tool will be predominantly accessed by biologists and various researchers in all platforms of life science.GUI was developed using the scripting language Perl and its graphical module PerlTK.RSMD covers algorithmic foundations of computational biology by combining theory with practice. 展开更多
关键词 motif repeats genomic sequence proteomic sequence computational biology combination algorithm
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An Extended Particle Swarm Optimizer
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作者 XU Jun-jie XIN Zhan-hong 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2005年第3期105-108,共4页
An Extended Particle Swarm Optimizer (EPSO) is proposed in this paper. In this new algorithm, not only the local but also the global best position will impact the particle's velocity updating process. EPSO is an in... An Extended Particle Swarm Optimizer (EPSO) is proposed in this paper. In this new algorithm, not only the local but also the global best position will impact the particle's velocity updating process. EPSO is an integration of Local Best paradigm (LBEST) and Global Best paradigm (GBEST) and it significantly enhances the performance of the conventional particle swarm optimizers. The experiment results have proved that EPSO deserves to be investigated. 展开更多
关键词 particle swarm optimization simulating biology intelligent algorithm evolutionary computation
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