Titlebook: Data Mining in Bioinformatics; Xindong Wu,Lakhmi Jain,Dennis Shasha Book 2005 Springer-Verlag London 2005 Alignment.Markov chain.XML.bioin

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書目名稱Data Mining in Bioinformatics影響因子(影響力)




書目名稱Data Mining in Bioinformatics影響因子(影響力)學科排名




書目名稱Data Mining in Bioinformatics網(wǎng)絡公開度




書目名稱Data Mining in Bioinformatics網(wǎng)絡公開度學科排名




書目名稱Data Mining in Bioinformatics被引頻次




書目名稱Data Mining in Bioinformatics被引頻次學科排名




書目名稱Data Mining in Bioinformatics年度引用




書目名稱Data Mining in Bioinformatics年度引用學科排名




書目名稱Data Mining in Bioinformatics讀者反饋




書目名稱Data Mining in Bioinformatics讀者反饋學科排名

HEAVY

橢圓

https://doi.org/10.1007/978-1-4614-7560-6ies and for accurately estimating the costs of the various query evaluation plans. Our performance studies show that the proposed techniques are very efficient and can provide scientists with interactive secondary structure querying options even on large protein datasets.

輕浮思想

Book 2005ly involved in intracellular tra?cking and support. The context of protein functionality is well represented by protein subcellular location. Proteins have various subcellular location patterns [250]. One major category of proteins is synthesized on free ribosomes in the cytoplasm. Soluble proteins

patriot

AntiClustAl: Multiple Sequence Alignment by Antipole Clusteringces. In particular, a clustering data structure called antipole tree and an approximate linear 1-median computation are used. Our algorithm enjoys a better running time with equivalent alignment quality compared with ClustalW, a widely used tool for multiple sequence alignment. A successful biologic

記成螞蟻

Data Mining Methods for a Systematics of Protein Subcellular Locationom the fluorescence microscope images. By selecting the most discriminative features from the entire feature set and recruiting various state-of-the-art classifiers, the system is able to outperform human experts in distinguishing protein patterns. The discriminative features can also be used for ro

記成螞蟻

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外星人

座右銘

https://doi.org/10.1007/978-3-031-41837-2d on them to make new discoveries on his or her own. The book contains twelve chapters in four parts, namely, overview, sequence and structure alignment, biological data mining, and biological data management. This chapter provides an introduction to the field and describes how the chapters in the book relate to one another.