標(biāo)題: Titlebook: Genome Data Analysis; Ju Han Kim Textbook 2019 Springer Nature Singapore Pte Ltd. 2019 Genome data analysis.Bioinformatics.Practice in dat [打印本頁] 作者: mountebank 時間: 2025-3-21 20:08
書目名稱Genome Data Analysis影響因子(影響力)
書目名稱Genome Data Analysis影響因子(影響力)學(xué)科排名
書目名稱Genome Data Analysis網(wǎng)絡(luò)公開度
書目名稱Genome Data Analysis網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Genome Data Analysis被引頻次
書目名稱Genome Data Analysis被引頻次學(xué)科排名
書目名稱Genome Data Analysis年度引用
書目名稱Genome Data Analysis年度引用學(xué)科排名
書目名稱Genome Data Analysis讀者反饋
書目名稱Genome Data Analysis讀者反饋學(xué)科排名
作者: 一夫一妻制 時間: 2025-3-21 22:59
LSI/VLSI Board Level Guidelines,hanged genetic analysis from qualitative to quantitative. Next-generation sequencing (NGS) technology, by making analysis of the genomic sequences that form the basis of biological phenomena widely available, is constantly presenting new views on biological and disease-related phenomena. In the firs作者: Munificent 時間: 2025-3-22 04:17
2509-6125 ormatics who are experiencing difficulty in approaching the field. However, it will also serve as a simple guideline for experts unfamiliar with the new, developing subfield of genomic analysis within bioinform978-981-13-1941-9978-981-13-1942-6Series ISSN 2509-6125 Series E-ISSN 2509-6133 作者: 懸崖 時間: 2025-3-22 05:09
Bioinformatics for Lifehanged genetic analysis from qualitative to quantitative. Next-generation sequencing (NGS) technology, by making analysis of the genomic sequences that form the basis of biological phenomena widely available, is constantly presenting new views on biological and disease-related phenomena. In the firs作者: 盤旋 時間: 2025-3-22 10:18 作者: tooth-decay 時間: 2025-3-22 14:46 作者: tooth-decay 時間: 2025-3-22 18:21
Embodiment design considerations, various annotation and detection methods of SNP/InDel from the obtained sequences. This chapter describes the difference in analytical methods between common variants and rare variants, and an analysis approach using biological pathways, pharmacogenomics, and information of racial differences using作者: 易受騙 時間: 2025-3-22 23:17
Das Konzept der dysfunktionalen Kognitionen,s approaches for adding SNP annotations and medicinal interpretations, using open sources based on personal genome data and genome variation information. This chapter will cover the following: (1) effective use of SNP data in SNPedia, (2) auto annotations of large volume of SNPs using Promethease ap作者: 古文字學(xué) 時間: 2025-3-23 04:18 作者: 施加 時間: 2025-3-23 08:13 作者: 成份 時間: 2025-3-23 13:26
https://doi.org/10.1007/978-1-4842-3712-0ta. Analysis requires one to have a thorough understanding of basic biology. We will go over gene sets used to interpret data as well as analyzing data. DAVID, ArrayXPath are two apparatuses used to gather fundamental biological interpretation using gene sets given. BioLattice is also designed to an作者: flaunt 時間: 2025-3-23 14:33 作者: 懦夫 時間: 2025-3-23 19:09 作者: 序曲 時間: 2025-3-24 00:24 作者: Lice692 時間: 2025-3-24 03:56 作者: Granular 時間: 2025-3-24 08:26 作者: micronized 時間: 2025-3-24 11:17
Ju Han KimDescribes recent advances in genomics and bioinformatics.Provides numerous examples of genome data analysis.Meets the needs of life scientists, medical scientists, and others who are new to the field 作者: airborne 時間: 2025-3-24 15:03
Mohamad Al Ali,Michal Tomko,Ivo DemjanThe objectives of this chapter are to teach generating DEGs in microarray gene expression data, extracting a gene cluster of genes with similar patterns of expression, classifying the observed data using SVM and KNN, and learning the basic syntax of the R program, a useful tool for genome data analysis.作者: FLACK 時間: 2025-3-24 20:51
Mladen Kezunovic,Jinfeng Ren,Saeed LotfifardIn this chapter, in order to investigate the biological function, we will practice verifying existing knowledge with concurrent experiments on microarray data analysis to identify significant correlations of miRNA-mRNA pairs derived from the miRNA and mRNA analysis profile of the same sample.作者: Nonthreatening 時間: 2025-3-25 02:48 作者: 能得到 時間: 2025-3-25 07:21 作者: NORM 時間: 2025-3-25 10:35
Gene Expression Data AnalysisThe objectives of this chapter are to teach generating DEGs in microarray gene expression data, extracting a gene cluster of genes with similar patterns of expression, classifying the observed data using SVM and KNN, and learning the basic syntax of the R program, a useful tool for genome data analysis.作者: 輕率看法 時間: 2025-3-25 13:28 作者: 細(xì)微的差異 時間: 2025-3-25 16:46
Molecular Pathways and Gene OntologyThis chapter covers several topics: (1) understanding biomedical data and knowledge resources, such as gene ontology and biological pathways, (2) the practical use of these systems, and (3) biological text mining based on biomedical resources.作者: 爭吵 時間: 2025-3-25 23:20 作者: Glutinous 時間: 2025-3-26 00:29 作者: 使堅硬 時間: 2025-3-26 05:08
Industrialisierung und Beginn des Designsd variants. A number of software programs have been developed to identify statistically significant and clinically relevant SNPs, predict disease risk, and identify disease-related rare variants. This chapter introduces various bioinformatics resources used for the interpretation of personal genomic data.作者: Adherent 時間: 2025-3-26 11:25 作者: callous 時間: 2025-3-26 16:25
Mustapha Hamdi,Antoine Ferreirared genes obtained by microarray data clustering analysis and test the statistical significance of different prognoses between clusters. It provides an understanding of the correlation between biological interpretation and GO and pathway analysis of the clustered genes and an interpretation with GSEA of the clustered genes.作者: 混合物 時間: 2025-3-26 17:26 作者: 積習(xí)難改 時間: 2025-3-26 23:39 作者: 代替 時間: 2025-3-27 01:30
Next-Generation Sequencing Technology and Personal Genome Data Analysis various annotation and detection methods of SNP/InDel from the obtained sequences. This chapter describes the difference in analytical methods between common variants and rare variants, and an analysis approach using biological pathways, pharmacogenomics, and information of racial differences using The 1000 Genomes Project Data.作者: opinionated 時間: 2025-3-27 07:42
Personal Genome Interpretation and Disease Risk Predictiond variants. A number of software programs have been developed to identify statistically significant and clinically relevant SNPs, predict disease risk, and identify disease-related rare variants. This chapter introduces various bioinformatics resources used for the interpretation of personal genomic data.作者: Talkative 時間: 2025-3-27 10:37
Gene Ontology and Biological Pathway-Based Analysista. Analysis requires one to have a thorough understanding of basic biology. We will go over gene sets used to interpret data as well as analyzing data. DAVID, ArrayXPath are two apparatuses used to gather fundamental biological interpretation using gene sets given. BioLattice is also designed to analyze the results of the data given.作者: BRUNT 時間: 2025-3-27 16:40 作者: grandiose 時間: 2025-3-27 21:05
Motif and Regulatory Sequence Analysisenetic tree analysis (3) prediction of transcription factor and microRNA (miRNA) binding sites involved in gene regulation (4) visualization and exploration of sequence annotations using a genome browser.作者: mutineer 時間: 2025-3-28 01:39
Biological Network Analysising based on existing publications. We will analyze evolutionary distance and connectivity so that we need to confirm that the protein interaction network is a scale-free network and hub genes are evolutionarily old proteins.作者: OMIT 時間: 2025-3-28 04:59
978-981-13-1941-9Springer Nature Singapore Pte Ltd. 2019作者: Brochure 時間: 2025-3-28 09:38 作者: 有法律效應(yīng) 時間: 2025-3-28 10:50
Das Konzept der dysfunktionalen Kognitionen,o diseases or drug responses by mapping with the pharmacogenomics knowledge base or biological pathways, and (5) practicums for acquiring and using allele frequencies among races based on public data of the 1000 genomes project.作者: 陪審團 時間: 2025-3-28 16:05 作者: 入會 時間: 2025-3-28 22:33
2509-6125 ts, medical scientists, and others who are new to the field .This textbook describes recent advances in genomics and bioinformatics and provides numerous examples of genome data analysis that illustrate its relevance to real world problems and will improve the reader’s bioinformatics skills. Basic d作者: 裂縫 時間: 2025-3-29 02:47
https://doi.org/10.1007/978-1-4020-6488-3s between case and control groups, to perform cluster and classification analysis, and to understand the importance of biological pathway analysis with the interpretation of microarray data using the GSEA program and R package.作者: 全神貫注于 時間: 2025-3-29 05:50 作者: Salivary-Gland 時間: 2025-3-29 10:01 作者: 不透氣 時間: 2025-3-29 13:54
Textbook 2019evance to real world problems and will improve the reader’s bioinformatics skills. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine learning algorithms using R and Python are demonstrated for gene-expression microarrays, genotyping microarrays, next-ge作者: Muffle 時間: 2025-3-29 15:49 作者: arthroplasty 時間: 2025-3-29 20:56 作者: 奇思怪想 時間: 2025-3-30 02:40
SNPs, GWAS, CNVs: Informatics for Human Genome VariationsNP, International HapMap Project, and PharmGKB. We will study the hypothesis of common and rare disease gene variations. Finally, this chapter will also go over the assumptions and method of analysis used by GWAS research and copy-number variation research and survey the future of genome diversity.作者: Devastate 時間: 2025-3-30 04:10
Bioinformatics for Life informatics phenomena that take the form of sophisticated interactions between the materials that constitute living organisms, matter in general, and energy. As a consequence of the development of technology for acquiring vast amounts of biological information, molecular genetics has progressed fro作者: 表兩個 時間: 2025-3-30 10:18
Next-Generation Sequencing Technology and Personal Genome Data Analysis various annotation and detection methods of SNP/InDel from the obtained sequences. This chapter describes the difference in analytical methods between common variants and rare variants, and an analysis approach using biological pathways, pharmacogenomics, and information of racial differences using作者: 自制 時間: 2025-3-30 13:19
Personal Genome Data Analysiss approaches for adding SNP annotations and medicinal interpretations, using open sources based on personal genome data and genome variation information. This chapter will cover the following: (1) effective use of SNP data in SNPedia, (2) auto annotations of large volume of SNPs using Promethease ap作者: 泄露 時間: 2025-3-30 16:41 作者: Apraxia 時間: 2025-3-30 23:17 作者: myopia 時間: 2025-3-31 02:36 作者: certain 時間: 2025-3-31 06:30
Gene Set Approaches and Prognostic Subgroup Predictionred genes obtained by microarray data clustering analysis and test the statistical significance of different prognoses between clusters. It provides an understanding of the correlation between biological interpretation and GO and pathway analysis of the clustered genes and an interpretation with GSE
