Titlebook: Computational Systems Biology; Reneé Ireton,Kristina Montgomery,Jason McDermott Book 2009 Humana Press 2009 Analysis.algorithms.bioinforma

不讓做的事

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

潰爛

fertilizer

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

apropos

截?cái)?/a>

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

容易懂得

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

容易懂得

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

FLINT

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

減少

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

DEFT