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Titlebook: Bioinformatics; A Concept-Based Intr Venkatarajan S. Mathura,Pandjassarame Kangueane Book 2009 Springer-Verlag US 2009 In silico.Microarray

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發(fā)表于 2025-3-21 17:26:54 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Bioinformatics
期刊簡稱A Concept-Based Intr
影響因子2023Venkatarajan S. Mathura,Pandjassarame Kangueane
視頻videohttp://file.papertrans.cn/188/187224/187224.mp4
發(fā)行地址Textbooks provide a practical introduction to bioinformatics and computer technologies.Our book is a workbook which spends more time showing students techniques and applications, problems and solution
圖書封面Titlebook: Bioinformatics; A Concept-Based Intr Venkatarajan S. Mathura,Pandjassarame Kangueane Book 2009 Springer-Verlag US 2009 In silico.Microarray
影響因子.Bioinformatics is an evolving field that is gaining popularity due to genomics, proteomics and other high-throughput biological methods. The function of bioinformatic scientists includes biological data storage, retrieval and in silico analysis of the results from large-scale experiments. This requires a grasp of knowledge mining algorithms, a thorough understanding of biological knowledge base, and the logical relationship of entities that describe a process or the system. Bioinformatics researchers are required to be trained in multidisciplinary fields of biology, mathematics and computer science. Currently the requirements are satisfied by ad hoc researchers who have specific skills in biology or mathematics/computer science. But the learning curve is steep and the time required to communicate using domain specific terms is becoming a major bottle neck in scientific productivity. This workbook provides hands-on experience which has been lacking for qualified bioinformatics researchers. .
Pindex Book 2009
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書目名稱Bioinformatics影響因子(影響力)




書目名稱Bioinformatics影響因子(影響力)學(xué)科排名




書目名稱Bioinformatics網(wǎng)絡(luò)公開度




書目名稱Bioinformatics網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Bioinformatics被引頻次




書目名稱Bioinformatics被引頻次學(xué)科排名




書目名稱Bioinformatics年度引用




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發(fā)表于 2025-3-21 22:58:21 | 只看該作者
Computational Immunology: HLA-peptide Binding Prediction, competitive binding assays. Alternatively, prediction of peptide binding to HLA molecules is highly useful. Efficient prediction models enable systematic scanning of candidate peptides in an effective manner. Here, we describe some commonly used prediction models.
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students techniques and applications, problems and solution.Bioinformatics is an evolving field that is gaining popularity due to genomics, proteomics and other high-throughput biological methods. The function of bioinformatic scientists includes biological data storage, retrieval and in silico ana
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發(fā)表于 2025-3-22 10:07:25 | 只看該作者
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發(fā)表于 2025-3-22 14:42:40 | 只看該作者
Biological Sequence Search and Analysis,motifs and patterns. Sequence search tools help in identifying similar sequences in protein and genomic databases. Here, we will discuss bioinformatics tools that help in biological sequence searches and analyses.
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發(fā)表于 2025-3-22 17:52:29 | 只看該作者
d7629, XIII.2.5 Further oxoborates,es) and MEG (multi exon genes). Thus, SEG have 0 intron and MEG have at least one intron. Consequently, we compared the SEG and MEG fraction across different eukaryotic genomes. The comparison helped to discuss the evolutionary selection of SEG and MEG fraction in eukaryotic genomes.
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發(fā)表于 2025-3-23 00:43:53 | 只看該作者
Book 2009 of bioinformatic scientists includes biological data storage, retrieval and in silico analysis of the results from large-scale experiments. This requires a grasp of knowledge mining algorithms, a thorough understanding of biological knowledge base, and the logical relationship of entities that desc
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Bioinformatics Application: Eukaryotic Gene Count and Evolution,es) and MEG (multi exon genes). Thus, SEG have 0 intron and MEG have at least one intron. Consequently, we compared the SEG and MEG fraction across different eukaryotic genomes. The comparison helped to discuss the evolutionary selection of SEG and MEG fraction in eukaryotic genomes.
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發(fā)表于 2025-3-23 09:16:23 | 只看該作者
Title, Preface, etc., for Vol. III/7D2,l accuracy of 80.5% and precision 75.1% for four major organellar compartments (cytosol, membranes, mitochondria, and nucleus). The classifiers were able to predict the subcellular localization of 2390 previously uncharacterized proteins, 1370 of which were assigned to one or more compartments with at least 80% confidence.
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