Titlebook: Network Inference in Molecular Biology; A Hands-on Framework Jesse M. Lingeman,Dennis Shasha Book 2012 The Author(s) 2012 Clustering.Gene R

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Book 2012one tailored to a specific data situation..Network Inference in Molecular Biology. is intended for advanced-level students and researchers as a reference guide. Practitioners and professionals working in a related field will also find this book valuable..

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Book 2012s. While researchers have developed techniques to find some of the underlying network structure, there is still no one-size-fits-all algorithm for every data set. .Network Inference in Molecular Biology. examines the current techniques used by researchers, and provides key insights into which algori

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Clustering Data,ps even make certain relationships stronger!). For example, if there are two genes that both behave in exactly the same way across the experimental conditions of interest, then little to no information is lost if you treat them as though they were a single “gene”.

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Step 2: Use Steady State Data for Network Inference,re genes have been knocked out, overexpressed, or otherwise perturbed. If you know what happens to the network when a gene is missing or when it has been perturbed, it is easier to infer which genes it influences.

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Step 3: Using Time-Series Data,pes of algorithms will be presented in this section: mutual information, ordinary differential equations with l1 regularization, and dynamic Bayesian Networks. Each of these approaches makes different assumptions about the data.

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SpringerBriefs in Electrical and Computer Engineeringhttp://image.papertrans.cn/n/image/662807.jpg

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