標(biāo)題: Titlebook: Computational Systems Biology; Methods and Protocol Tao Huang Book 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 [打印本頁] 作者: Waterproof 時(shí)間: 2025-3-21 18:00
書目名稱Computational Systems Biology影響因子(影響力)
書目名稱Computational Systems Biology影響因子(影響力)學(xué)科排名
書目名稱Computational Systems Biology網(wǎng)絡(luò)公開度
書目名稱Computational Systems Biology網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computational Systems Biology被引頻次
書目名稱Computational Systems Biology被引頻次學(xué)科排名
書目名稱Computational Systems Biology年度引用
書目名稱Computational Systems Biology年度引用學(xué)科排名
書目名稱Computational Systems Biology讀者反饋
書目名稱Computational Systems Biology讀者反饋學(xué)科排名
作者: ONYM 時(shí)間: 2025-3-21 21:17
Michael M. Resch,Bastian Koller relatively variable regions and relatively conservative regions. The divergent parts of target DNA are enriched along with conservative parts of DNA sequence that effectively captured during hybridization. We present a protocol that allows users to overcome the low capture sensitivity problem for h作者: Archipelago 時(shí)間: 2025-3-22 02:00 作者: 元音 時(shí)間: 2025-3-22 05:40 作者: CRANK 時(shí)間: 2025-3-22 11:46 作者: emission 時(shí)間: 2025-3-22 15:39
OECD Nations and Sustainable Developmentcoding RNAs and diseases, including their advantages and disadvantages. The key issues and potential future works of predicting interactions between long noncoding RNAs and diseases are also discussed.作者: emission 時(shí)間: 2025-3-22 19:41
The Trajectories of Worklife Transitionsthods that specifically fit single-cell sequencing data. We here comprehensively survey the current strategies and challenges for multiple single-cell sequencing, including single-cell transcriptome, genome, and epigenome, beginning with a brief introduction to multiple sequencing techniques for sin作者: 熱心助人 時(shí)間: 2025-3-22 21:41 作者: COMA 時(shí)間: 2025-3-23 03:55
Integrative Analysis of Omics Big Data,ombine and investigate many datasets in a cost-effective reproducible way. Current integration approaches on biological data have two modes: one is “bottom-up integration” mode with follow-up manual integration, and the other one is “top-down integration” mode with follow-up in silico integration..T作者: Fester 時(shí)間: 2025-3-23 08:10 作者: fender 時(shí)間: 2025-3-23 11:19
Differential Coexpression Network Analysis for Gene Expression Data,ression analysis are widely used in a variety of areas in response to environmental stresses, genetic differences, or disease changes. In this chapter, we reviewed the existing methods for differential coexpression network analysis and discussed the applications to cancer research.作者: 慢慢啃 時(shí)間: 2025-3-23 16:55 作者: Cardiac-Output 時(shí)間: 2025-3-23 18:25 作者: 小臼 時(shí)間: 2025-3-23 22:21 作者: 籠子 時(shí)間: 2025-3-24 02:24
Sustained Simulation Performance 2022ature, the task to detect low mutated allele frequency (MAF) variations from noisy sequencing data remains challenging. In this chapter, the authors will first explain the difficulties of analyzing ctDNA sequencing data, review related technologies, and then present some novel bioinformatics methods for analyzing ctDNA NGS data in better ways.作者: 脆弱吧 時(shí)間: 2025-3-24 09:30 作者: 冒煙 時(shí)間: 2025-3-24 11:11
OECD Nations and Sustainable Developmentning new candidates with low toxicity in drug R&D studies. In the future, much still needs to be done to understand more completely the biological mechanisms for toxicity and to develop more accurate prediction models to screen compounds.作者: 令人悲傷 時(shí)間: 2025-3-24 15:14 作者: flourish 時(shí)間: 2025-3-24 19:50 作者: 禮節(jié) 時(shí)間: 2025-3-25 00:38
Machine Learning-Based Modeling of Drug Toxicity,ning new candidates with low toxicity in drug R&D studies. In the future, much still needs to be done to understand more completely the biological mechanisms for toxicity and to develop more accurate prediction models to screen compounds.作者: 勤勞 時(shí)間: 2025-3-25 06:37 作者: Parley 時(shí)間: 2025-3-25 11:21 作者: CLAMP 時(shí)間: 2025-3-25 11:42
Book 2018ls and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls..Authoritative and cutting-edge, .Computational Systems Biology: Methods and Protocols .aims to ensuresuccessful results in the further study of this vital field.?.作者: 頌揚(yáng)國家 時(shí)間: 2025-3-25 16:32
1064-3745 ation advice from the experts.This volume introduces the reader to the latest experimental and bioinformatics methods for DNA sequencing, RNA sequencing, cell-free tumour DNA sequencing, single cell sequencing, single-cell proteomics and metabolomics. Chapters detail advanced analysis methods, such 作者: PHONE 時(shí)間: 2025-3-25 22:04
Sustained Simulation Performance 2021 ctDNA is mostly used for cancer patients to select targeted drugs in clinical application. In addition, ctDNA could also be applied to monitor tumor progression and recurrence. In conclusion, ctDNA is a very promising tumor biomarker for diagnosis and monitoring, which would increasingly become a routine clinical application in recent years.作者: AXIOM 時(shí)間: 2025-3-26 01:28
https://doi.org/10.1007/978-981-99-9714-5sychiatric disorders. The standard strategy of population-based case-control studies for GWAS is illustrated in this chapter. We provide an overview of the concepts underlying GWAS, as well as provide guidelines for statistical methods performed in GWAS.作者: 逃避系列單詞 時(shí)間: 2025-3-26 07:27
https://doi.org/10.1007/978-1-349-21091-6e of DNA-binding proteins, and the merits and limitations of these methods are mainly discussed. This chapter focuses on the structure-based approaches and mainly discusses the framework of machine learning methods in application to DNA-binding prediction task.作者: degradation 時(shí)間: 2025-3-26 12:19
The Introduction and Clinical Application of Cell-Free Tumor DNA, ctDNA is mostly used for cancer patients to select targeted drugs in clinical application. In addition, ctDNA could also be applied to monitor tumor progression and recurrence. In conclusion, ctDNA is a very promising tumor biomarker for diagnosis and monitoring, which would increasingly become a routine clinical application in recent years.作者: 仔細(xì)檢查 時(shí)間: 2025-3-26 15:23 作者: 使閉塞 時(shí)間: 2025-3-26 19:45 作者: Meditate 時(shí)間: 2025-3-26 21:44
Bernadette Mary Mercieca,Jacquelin McDonald data analysis modules. Users without programming and statistical skills can analyze their RNA-seq data and construct publication-level graphs through a standardized yet customizable analytical pipeline. iSeq is accessible via Web browsers on any operating system at ..作者: 嘲笑 時(shí)間: 2025-3-27 04:27
https://doi.org/10.1007/978-1-349-21091-6cal and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.作者: 經(jīng)典 時(shí)間: 2025-3-27 05:34
iSeq: Web-Based RNA-seq Data Analysis and Visualization, data analysis modules. Users without programming and statistical skills can analyze their RNA-seq data and construct publication-level graphs through a standardized yet customizable analytical pipeline. iSeq is accessible via Web browsers on any operating system at ..作者: deadlock 時(shí)間: 2025-3-27 10:57 作者: 拔出 時(shí)間: 2025-3-27 14:32
Bioinformatics Analysis for Cell-Free Tumor DNA Sequencing Data,ature, the task to detect low mutated allele frequency (MAF) variations from noisy sequencing data remains challenging. In this chapter, the authors will first explain the difficulties of analyzing ctDNA sequencing data, review related technologies, and then present some novel bioinformatics methods for analyzing ctDNA NGS data in better ways.作者: interpose 時(shí)間: 2025-3-27 19:45 作者: 剛開始 時(shí)間: 2025-3-28 01:27
DNA Sequencing Data Analysis,t to understand life science more precisely. This chapter is an overview of DNA sequencing technology and its data analysis methods, providing information about DNA sequencing, several different methods, and tools applied in data analysis. Both advantages and disadvantages are discussed.作者: muffler 時(shí)間: 2025-3-28 05:35
Transcriptome Sequencing: RNA-Seq,l events, such as alternative splicing, novel transcripts, and fusion genes. In principle, RNA-seq can be carried out by almost all of the next-generation sequencing (NGS) platforms, but the libraries of different platforms are not exactly the same; each platform has its own kit to meet the special 作者: 颶風(fēng) 時(shí)間: 2025-3-28 09:56 作者: 虛假 時(shí)間: 2025-3-28 13:13 作者: NIL 時(shí)間: 2025-3-28 15:15
Bioinformatics Analysis for Cell-Free Tumor DNA Sequencing Data, provide comprehensive genetic information of tumor and better overcome the tumor heterogeneity problem comparing to tissue biopsy. Developed in recent years, next-generation sequencing (NGS) is a widely used technology for analyzing ctDNA. Although the technologies of processing ctDNA samples are m作者: embolus 時(shí)間: 2025-3-28 21:18
An Overview of Genome-Wide Association Studies,ween common single-nucleotide polymorphisms (SNPs) and common human diseases such as heart disease, inflammatory bowel disease, type 2 diabetes, and psychiatric disorders. The standard strategy of population-based case-control studies for GWAS is illustrated in this chapter. We provide an overview o作者: 晚來的提名 時(shí)間: 2025-3-28 23:00 作者: 不遵守 時(shí)間: 2025-3-29 05:39
The Reconstruction and Analysis of Gene Regulatory Networks,and their organization in living cells. For this end, gene regulatory networks (GRNs) are constructed to show relationship between biological molecules, in which the vertices of network denote biological molecules and the edges of network present connection between nodes (Strogatz, Nature 410:268–27作者: headway 時(shí)間: 2025-3-29 08:15
Differential Coexpression Network Analysis for Gene Expression Data,n profiling just focuses on the individual genes, and the interactions among genes are ignored, while genes play their roles not by isolations but by interactions with each other. Consequently, gene-to-gene coexpression analysis emerged as a powerful approach to solve the above problems. Then comple作者: 催眠 時(shí)間: 2025-3-29 13:14 作者: delta-waves 時(shí)間: 2025-3-29 15:44 作者: Largess 時(shí)間: 2025-3-29 23:43
Identifying Interactions Between Long Noncoding RNAs and Diseases Based on Computational Methods,NAs (lncRNAs) are the biggest kind of noncoding RNAs with more than 200?nt nucleotides in length. There are increasing evidences showing that lncRNAs play key roles in many biological processes. Therefore, the mutation and dysregulation of lncRNAs have close association with a number of complex huma作者: HARP 時(shí)間: 2025-3-30 03:22
Survey of Computational Approaches for Prediction of DNA-Binding Residues on Protein Surfaces,annotations on proteins. In this chapter, different kinds of computational approaches are briefly introduced to predict DNA-binding residues on surface of DNA-binding proteins, and the merits and limitations of these methods are mainly discussed. This chapter focuses on the structure-based approache作者: glans-penis 時(shí)間: 2025-3-30 05:07
Computational Prediction of Protein O-GlcNAc Modification,mall portion of O-GlcNAcylation sites. Several computational algorithms have been proposed as necessary auxiliary tools to identify potential O-GlcNAcylation sites. This chapter discusses the metrics and procedures used to assess prediction tools and surveys six computational tools for the predictio作者: 前面 時(shí)間: 2025-3-30 10:15
Machine Learning-Based Modeling of Drug Toxicity,ofile are costly and time-consuming. Therefore, it is attractive to develop the effective and accurate alternatives, such as in silico prediction models. In this review, we discuss the practical use of some prediction models on three toxicity end points, including acute toxicity, carcinogenicity, an作者: sleep-spindles 時(shí)間: 2025-3-30 14:55
Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration,tissues, and breathe exhalation, which reflects metabolic responses of a living system to pathophysiological stimuli or genetic modification. In the past decade, metabolomics has made notable progresses in providing useful systematic insights into the underlying mechanisms and offering potential bio作者: disrupt 時(shí)間: 2025-3-30 18:12
Single-Cell Protein Assays: A Review,that are being used to measure the protein copy numbers at the single-cell level, which includes flow cytometry, mass cytometry, droplet cytometry, microengraving, and single-cell barcoding microchip. The advantages and limitations of each technique are compared, and future research opportunities ar作者: olfction 時(shí)間: 2025-3-30 23:55 作者: 傷心 時(shí)間: 2025-3-31 01:57 作者: 裁決 時(shí)間: 2025-3-31 08:25 作者: paleolithic 時(shí)間: 2025-3-31 11:59
Methods in Molecular Biologyhttp://image.papertrans.cn/c/image/233165.jpg作者: Kidnap 時(shí)間: 2025-3-31 14:24 作者: 針葉 時(shí)間: 2025-3-31 19:45 作者: 上下連貫 時(shí)間: 2025-3-31 23:38 作者: 支架 時(shí)間: 2025-4-1 04:38 作者: JEER 時(shí)間: 2025-4-1 10:01
Hiroaki Kobayashi,Kazuhiko Komatsul events, such as alternative splicing, novel transcripts, and fusion genes. In principle, RNA-seq can be carried out by almost all of the next-generation sequencing (NGS) platforms, but the libraries of different platforms are not exactly the same; each platform has its own kit to meet the special requirements of the instrument design.作者: 隱士 時(shí)間: 2025-4-1 12:59
The Trajectories of Worklife Transitionsthat are being used to measure the protein copy numbers at the single-cell level, which includes flow cytometry, mass cytometry, droplet cytometry, microengraving, and single-cell barcoding microchip. The advantages and limitations of each technique are compared, and future research opportunities are highlighted.作者: FIS 時(shí)間: 2025-4-1 16:12
Hiroaki Kobayashi,Kazuhiko Komatsut to understand life science more precisely. This chapter is an overview of DNA sequencing technology and its data analysis methods, providing information about DNA sequencing, several different methods, and tools applied in data analysis. Both advantages and disadvantages are discussed.作者: 謊言 時(shí)間: 2025-4-1 20:17 作者: Canyon 時(shí)間: 2025-4-2 00:56 作者: Aesthete 時(shí)間: 2025-4-2 05:17 作者: 中古 時(shí)間: 2025-4-2 09:56 作者: 嘲弄 時(shí)間: 2025-4-2 13:16 作者: 熱心 時(shí)間: 2025-4-2 18:11
Kick Off: Government Restructuring in 2009, decade ago. They are opening a new challenge from horizontal data ensemble (e.g., the similar types of data collected from different labs or companies) to vertical data ensemble (e.g., the different types of data collected for a group of person with match information), which requires the integrativ
