標(biāo)題: Titlebook: Computational Systems Biology; Reneé Ireton,Kristina Montgomery,Jason McDermott Book 2009 Humana Press 2009 Analysis.algorithms.bioinforma [打印本頁] 作者: 不讓做的事 時間: 2025-3-21 19:16
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: 潰爛 時間: 2025-3-21 23:59 作者: fertilizer 時間: 2025-3-22 04:09
David B. Audretsch,Werner B?nte,Max Keilbachiptional regulation in higher eukaryotes, particularly in metazoans, could be an important factor contributing to their organismal complexity. Here we present an integrated approach where networks of co-expressed genes are combined with gene ontology–derived functional networks to discover clusters 作者: apropos 時間: 2025-3-22 07:49 作者: 截斷 時間: 2025-3-22 08:46
M. H. Bala Subrahmanya,Rumki Majumdarys to understand the intricate interplay between interactome and proteome. Ultimately, the combination of these sources of information will allow the prediction of interactions among proteins where only domain composition is known. Based on the currently available protein–protein interaction and dom作者: 容易懂得 時間: 2025-3-22 13:45
International Studies in Entrepreneurshiptions. While being successful on specific data, the concept has never been tested on a large set of proteins. In this chapter we analyze the feasibility of the co-evolution principle for protein–protein interaction prediction through one of its derivatives, the correlated divergence model. Given two作者: 容易懂得 時間: 2025-3-22 20:25
International Studies in Entrepreneurshipzation and evolution of biological systems. Data quality of experimental interactome maps can be assessed and improved by integrating multiple sources of evidence using machine learning methods. Here we describe the commonly used algorithms for predicting protein–protein interaction by genome data i作者: FLINT 時間: 2025-3-22 23:53
David B. Audretsch,Werner B?nte,Max Keilbachseveral results from genome-wide analysis of transcriptional regulatory networks are available, they are limited to model organisms such as yeast ... and worm .... Beyond these networks, experiments on TRIs study only individual genes and proteins of specific interest. In this chapter, we present a 作者: 減少 時間: 2025-3-23 03:23
Forest Assessment and Observation,r, detecting them in highly integrated biological networks requires a thorough understanding of the organization of these networks. In this chapter I argue that many biological networks are organized into many small, highly connected topologic modules that combine in a hierarchical manner into large作者: DEFT 時間: 2025-3-23 09:34 作者: 焦慮 時間: 2025-3-23 12:44 作者: 粗魯?shù)娜?nbsp; 時間: 2025-3-23 14:31 作者: cruise 時間: 2025-3-23 20:14 作者: 加強防衛(wèi) 時間: 2025-3-23 22:19 作者: neurologist 時間: 2025-3-24 03:48
Conceptualisation, Data and Method,he need to replicate. Internally these adjustments are manifest as changes in metabolite, protein, and gene activities. Such changes have become increasingly obvious to experimentalists, with the advent of high-throughput technologies. In this chapter we highlight some of the quantitative approaches作者: 廢墟 時間: 2025-3-24 07:23 作者: 香料 時間: 2025-3-24 14:21 作者: 我就不公正 時間: 2025-3-24 17:52 作者: 性學(xué)院 時間: 2025-3-24 22:10 作者: integrated 時間: 2025-3-25 01:40
Inferring Molecular Interactions Pathways from eQTL Data to find a pathway of molecular interactions responsible for controlling the expression levels. Here we describe a series of techniques for finding explanatory pathways by exploring the graphs of molecular interactions. We show several simple methods can find complete pathways that explain the mechanism of differential expression in eQTL data.作者: Benzodiazepines 時間: 2025-3-25 06:14 作者: sed-rate 時間: 2025-3-25 08:33 作者: gerontocracy 時間: 2025-3-25 15:44
David B. Audretsch,Werner B?nte,Max Keilbachusing a Gibbs sampling algorithm implemented in the A-GLAM software package. Using this approach, we analyze the cell-cycle co-expression network of the yeast ., showing that this approach correctly identifies .-regulatory elements present in clusters of co-expressed genes.作者: Immortal 時間: 2025-3-25 16:15
Forest Assessment and Observation,e degree, a property that can be used as a signature of hierarchical organization. As a case study, I identify the hierarchical modules within the . metabolic network, and show that the uncovered hierarchical modularity closely overlaps with known metabolic functions.作者: Stagger 時間: 2025-3-25 21:44 作者: OWL 時間: 2025-3-26 01:49 作者: 輕快帶來危險 時間: 2025-3-26 06:07
Detecting Hierarchical Modularity in Biological Networkse degree, a property that can be used as a signature of hierarchical organization. As a case study, I identify the hierarchical modules within the . metabolic network, and show that the uncovered hierarchical modularity closely overlaps with known metabolic functions.作者: adj憂郁的 時間: 2025-3-26 08:40 作者: 貴族 時間: 2025-3-26 16:25
International Studies in Entrepreneurshipmodel are considered, including algorithms that attempt to identify the subset of the database proteins (the homologs of the query proteins) that are more likely to interact. We test the models over a large set of protein interactions extracted from several sources, including BIND, DIP, and HPRD.作者: SKIFF 時間: 2025-3-26 18:49
International Year of Planet Earth. In this chapter, we will focus in detail on the first approach and describe methods to reconstruct and analyze the transcriptional regulatory networks of uncharacterized organisms by using a known regulatory network as a template.作者: Optometrist 時間: 2025-3-26 23:15 作者: cochlea 時間: 2025-3-27 03:14
Prediction of Protein–Protein Interactions: A Study of the Co-evolution Modelmodel are considered, including algorithms that attempt to identify the subset of the database proteins (the homologs of the query proteins) that are more likely to interact. We test the models over a large set of protein interactions extracted from several sources, including BIND, DIP, and HPRD.作者: 四海為家的人 時間: 2025-3-27 07:41
Methods to Reconstruct and Compare Transcriptional Regulatory Networks. In this chapter, we will focus in detail on the first approach and describe methods to reconstruct and analyze the transcriptional regulatory networks of uncharacterized organisms by using a known regulatory network as a template.作者: cliche 時間: 2025-3-27 10:51 作者: meritorious 時間: 2025-3-27 16:46
1064-3745 ological network analysis and data representation and manageComputational systems biology is the term that we use to describe computational methods to identify, infer, model, and store relationships between the molecules, pathways, and cells (‘‘systems’’) involved in a living organism. Based on this作者: A保存的 時間: 2025-3-27 20:05 作者: 創(chuàng)造性 時間: 2025-3-27 23:27
Computational Reconstruction of Protein–Protein Interaction Networks: Algorithms and Issues of evidence using machine learning methods. Here we describe the commonly used algorithms for predicting protein–protein interaction by genome data integration, and discuss several important yet often overlooked issues in computational reconstruction of protein–protein interaction networks.作者: Solace 時間: 2025-3-28 05:37
Structure-Based , Prediction of Transcription Factor–Binding Sitest molecular water solvent and counter-ions. For computational efficiency, we use a standard additive approximation for the contribution of each DNA base pair to the total binding free energy. The additive approximation is not strictly necessary, and more detailed computations could be used to invest作者: charisma 時間: 2025-3-28 06:45 作者: 忍受 時間: 2025-3-28 11:59 作者: 記憶法 時間: 2025-3-28 16:56
Methods for the Inference of Biological Pathways and Networkspters of this book will need to be integrated. Other chapters of this book describe a number of methods to identify or predict network components such as physical interactions. At the end of this chapter, we speculate that some of the approaches from other chapters could be integrated and used to “a作者: Climate 時間: 2025-3-28 22:06
Exploring Pathways from Gene Co-expression to Network Dynamicslity to disease, potential usefulness of a given drug, and consequences of such external stimuli as pharmacological interventions or caloric restriction. We demonstrated the applications of CoExMiner and PathwayPro by examining gene expression profiles of ligands and receptors in cancerous and non-c作者: Oversee 時間: 2025-3-29 02:30 作者: 勛章 時間: 2025-3-29 03:22 作者: 負(fù)擔(dān) 時間: 2025-3-29 09:14 作者: Prologue 時間: 2025-3-29 11:54 作者: flaggy 時間: 2025-3-29 17:48
M. H. Bala Subrahmanya,Rumki Majumdar as the driving force between interactions, we also modified the algorithm to account for combinations of more than two domains that govern a protein–protein interaction. This approach allows us to predict the previously unknown protein–protein interactions in . and ., with a degree of sensitivity a作者: 無辜 時間: 2025-3-29 20:47
David B. Audretsch,Werner B?nte,Max Keilbach, and protein families. Predicted TRIs expand the networks of gene regulation for a large number of organisms. The integration of experimentally verified and predicted TRIs with other known protein–protein interactions (PPIs) gives insight into specific pathways, network motifs, and the topological 作者: PHON 時間: 2025-3-30 03:00 作者: Neonatal 時間: 2025-3-30 07:52 作者: 明智的人 時間: 2025-3-30 08:44 作者: 奇怪 時間: 2025-3-30 16:22
John Barry,Marcel Wissenburg,Marius de Geustative, in vivo data, preferably obtained from standardised measurements and expressed as absolute units rather than relative units. Isolation of subsets of regulatory networks may render a system amenable to ‘bottom-up’ modelling, providing a valuable tool to the experimental molecular biologist. D作者: 充滿人 時間: 2025-3-30 18:52
Book 2009imentalists focused on smaller subsystems. The promise of such approaches is that they will elucidate patterns, relationships, and general features, which are not evident from examining specific components or subsystems. These predictions are either interesting in and of themselves (e. g. , the iden作者: 預(yù)示 時間: 2025-3-31 00:46 作者: 癡呆 時間: 2025-3-31 02:14
Structure-Based , Prediction of Transcription Factor–Binding Sites further information required. We use molecular dynamics and free energy calculations to compute the relative binding free energies for a transcription factor with multiple possible DNA sequences. These sequences are then used to construct a position weight matrix to represent the transcription fact作者: 贊成你 時間: 2025-3-31 06:27
Inferring Protein–Protein Interactions from Multiple Protein Domain Combinationsys to understand the intricate interplay between interactome and proteome. Ultimately, the combination of these sources of information will allow the prediction of interactions among proteins where only domain composition is known. Based on the currently available protein–protein interaction and dom作者: 惡心 時間: 2025-3-31 11:29
