By Albert Y. Zomaya
Detect the way to streamline advanced bioinformatics purposes with parallel computing This ebook allows readers to address extra complicated bioinformatics functions and bigger and richer information units. because the editor basically indicates, utilizing robust parallel computing instruments may end up in major breakthroughs in decoding genomes, knowing genetic sickness, designing custom-made drug treatments, and figuring out evolution. A huge diversity of bioinformatics functions is roofed with demonstrations on how every one could be parallelized to enhance functionality and achieve quicker premiums of computation. present parallel computing strategies and applied sciences are tested, together with dispensed computing and grid computing. Readers are supplied with a mix of algorithms, experiments, and simulations that offer not just qualitative but in addition quantitative insights into the dynamic box of bioinformatics. Parallel Computing for Bioinformatics and Computational Biology is a contributed paintings that serves as a repository of case reviews, jointly demonstrating how parallel computing streamlines tough difficulties in bioinformatics and produces larger effects. all of the chapters is authored via a longtime specialist within the box and punctiliously edited to make sure a constant process and excessive commonplace during the booklet. The paintings is geared up into 5 components: * Algorithms and types * series research and microarrays * Phylogenetics * Protein folding * systems and allowing applied sciences Researchers, educators, and scholars within the box of bioinformatics will notice how high-performance computing can allow them to deal with extra advanced facts units, achieve deeper insights, and make new discoveries.
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Extra resources for Parallel Computing for Bioinformatics and Computational Biology: Models, Enabling Technologies, and Case Studies
The interaction of each of the GA component, affecting the ability of the GA to search the space of available solutions and the design of an efﬁcient GA to solve a particular problem, necessitates some understanding of how the individual components will work together. Their interaction is the primary driver of effective performance of the GA. The complex interactions of the GA components and the generality of the approach are both a strength and a weakness. Therefore, proper understanding of the approach allows one to avoid the weakness and exploit the strength of the GA approach.
The ﬁeld of computational biology covers many areas: structural biology, biochemistry, physical chemistry, molecular biology, genomics and bioinformatics, control theory, statistics, mathematics, and computer science. Bioinformatics provides a wealth of potential challenges that can be used to advance the state of the art by creating scalable applications that can be used in customer environments. Thus, in computational biology, conducting research related to the realization of parallel/distributed scalable applications requires an understanding of the basics of all related ﬁelds.
It is desirable for individuals with signiﬁcant shared characteristics to have similar ﬁtness values. The ﬁtness function should point the GA toward the correct value, rather than away from it. In contrast, choosing a representation for a problem is a critical design decision and deﬁnes the search space. The representation speciﬁcally should help preserve the building blocks of the problem. The interaction of each of the GA component, affecting the ability of the GA to search the space of available solutions and the design of an efﬁcient GA to solve a particular problem, necessitates some understanding of how the individual components will work together.
Parallel Computing for Bioinformatics and Computational Biology: Models, Enabling Technologies, and Case Studies by Albert Y. Zomaya