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標(biāo)題: Titlebook: Comparative Gene Finding; Models, Algorithms a Marina Axelson-Fisk Book 2015Latest edition Springer-Verlag London 2015 Algorithms.Bioinform [打印本頁]

作者: 紀(jì)念性 時(shí)間: 2025-3-21 16:11
書目名稱Comparative Gene Finding影響因子(影響力)

書目名稱Comparative Gene Finding影響因子(影響力)學(xué)科排名

書目名稱Comparative Gene Finding網(wǎng)絡(luò)公開度

書目名稱Comparative Gene Finding網(wǎng)絡(luò)公開度學(xué)科排名

書目名稱Comparative Gene Finding被引頻次

書目名稱Comparative Gene Finding被引頻次學(xué)科排名

書目名稱Comparative Gene Finding年度引用

書目名稱Comparative Gene Finding年度引用學(xué)科排名

書目名稱Comparative Gene Finding讀者反饋

書目名稱Comparative Gene Finding讀者反饋學(xué)科排名

作者: 伴隨而來 時(shí)間: 2025-3-21 22:39

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作者: disciplined 時(shí)間: 2025-3-22 13:42
Lqg design of ship steering control systems,ibutions, sequence compositional measures, and splice site detection models. Each section details a number of the most commonly used algorithms for the characteristic in question. In particular, the splice site section includes various Markovian models, neural networks, linear discriminant analysis, Bayesian networks, and support vector machines.

作者: disciplined 時(shí)間: 2025-3-22 17:24

作者: gruelling 時(shí)間: 2025-3-22 21:55
Sequence Alignment,lexity. As a result, a flora of heuristic alignment algorithms have evolved. In the second part of the chapter, we give an account of the most common of these models, including progressive alignments, iterative methods, hidden Markov models, genetic algorithms, simulated annealing, and alignment profiles.

作者: BLINK 時(shí)間: 2025-3-23 02:02

作者: conifer 時(shí)間: 2025-3-23 05:50
Gene Structure Submodels,of a candidate exon or intron. In this chapter we describe some of the main submodels used in gene finding algorithms, and detail a number of different methods for integrating the sensors the submodels incorporate.

作者: Melanocytes 時(shí)間: 2025-3-23 12:39

作者: 寡頭政治 時(shí)間: 2025-3-23 15:34
Annotation Pipelines for Next-Generation Sequencing Projects,nd quality control issues when dealing with completely novel sequences. In this chapter we present the various issues and aspects involved in building a genome annotation pipeline, particularly aiming at next-generation sequencing data.

作者: 帶來的感覺 時(shí)間: 2025-3-23 21:05

作者: Bmd955 時(shí)間: 2025-3-24 01:19

作者: FRAX-tool 時(shí)間: 2025-3-24 02:46
https://doi.org/10.1007/978-3-642-97406-9d Markov models, neural networks, decision trees, and conditional random fields. Each model is described in algorithmic detail, and each model section is finished off by exemplifying a gene finder that uses the model in question.

作者: NATAL 時(shí)間: 2025-3-24 08:50

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作者: 縫紉 時(shí)間: 2025-3-25 07:41
https://doi.org/10.1007/978-3-642-97406-9s represent various features of a gene, such as exons, introns, and splice site models. Each submodel scores the probability, or likelihood, that each given sequence region constitutes the corresponding gene feature, and then these scores are passed on up?to the main algorithm. The main algorithm in

作者: 褻瀆 時(shí)間: 2025-3-25 12:17
Signal Processing and Systems Theoryed into two parts. In the first part, we describe the basic concepts of pairwise alignments, including substitution schemes and gap models, and move on to the application of dynamic programming to global and local alignments. We finish off by giving an overview of heuristic database searches and the

作者: CAND 時(shí)間: 2025-3-25 18:24
G?sta H. Granlund,Hans Knutsson of advantages over its single species predecessors, including higher prediction accuracy, and the ability to annotate more varying gene features that previously have eluded computational approaches. In Chap.?. we described some of the most common algorithms used as main algorithms in single species

作者: Conflagration 時(shí)間: 2025-3-25 23:30

作者: FAWN 時(shí)間: 2025-3-26 02:58

作者: Inflated 時(shí)間: 2025-3-26 06:11
https://doi.org/10.1007/978-0-387-72500-0 finder particularly adapted to eukaryotes, and works by simultaneously aligning and annotating two homologous sequences. The basic framework of SLAM is a generalized pair hidden Markov model, which is a seamless merging of pair hidden Markov models typically used for pairwise alignments, and genera

作者: plasma 時(shí)間: 2025-3-26 09:30
Multimedia Systems and Applicationslenges, not the least within the bioinformatics field. The opportunities include the possibility to sequence and analyze a wide variety of organisms, spanning distant parts of the tree of life. The challenges include dealing with the shorter sequence lengths, the reduced data quality, and training a

作者: 拖債 時(shí)間: 2025-3-26 12:48