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標(biāo)題: Titlebook: Selected Nuclear Materials and Engineering Systems; G. Effenberg,S. Ilyenko Book 20071st edition Springer-Verlag Berlin Heidelberg 2007 cr [打印本頁]

作者: 請(qǐng)回避    時(shí)間: 2025-3-21 17:53
書目名稱Selected Nuclear Materials and Engineering Systems影響因子(影響力)




書目名稱Selected Nuclear Materials and Engineering Systems影響因子(影響力)學(xué)科排名




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書目名稱Selected Nuclear Materials and Engineering Systems被引頻次




書目名稱Selected Nuclear Materials and Engineering Systems被引頻次學(xué)科排名




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書目名稱Selected Nuclear Materials and Engineering Systems年度引用學(xué)科排名




書目名稱Selected Nuclear Materials and Engineering Systems讀者反饋




書目名稱Selected Nuclear Materials and Engineering Systems讀者反饋學(xué)科排名





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ll world property and their (1-hop) degree distribution seems to form a power law. More recently, a number of duplication based random graph models have been proposed with the aim of emulating the evolution of protein-protein interaction networks and satisfying these two graph theoretical properties
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atical Biology, etc..Systems Biology not only to addresses kSystem Biology encompasses the knowledge from diverse fields such as Molecular Biology, Immunology, Genetics, Computational Biology, Mathematical Biology, etc. not only to address key questions that are not answerable by individual fields a
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in genetics and genomics studies with agricultural and livestock species. First, some key definitions regarding stochastic graphical models are provided, as well as basic principles of inference related to graphical structure and model parameters. Next is a discussion of some examples of applicatio
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l level. However, since genomic and transcript profiling likely cannot alone sufficiently predict protein pathway activation in each patient’s tumor, and it is these signaling pathways that represent the targets for new molecular guided therapeutics. Thus, it is critical that we begin to define huma
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rated and systems technologies unique to cancer drug discove.Systems Biology in Cancer Research and Drug Discovery provides a unique collection of chapters, by world-class researchers, describing the use of integrated systems biology and network modeling in the cancer field where traditional tools h
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at are involved in a signaling pathway. It therefore becomes imperative to take into account of these studies also, while constructing our network maps to highlight the interconnectedness of the entire signalin978-1-4939-3962-6978-1-4419-5797-9Series ISSN 2191-222X Series E-ISSN 2191-2238
作者: 擁護(hù)    時(shí)間: 2025-3-25 15:23
at are involved in a signaling pathway. It therefore becomes imperative to take into account of these studies also, while constructing our network maps to highlight the interconnectedness of the entire signalin978-1-4939-3962-6978-1-4419-5797-9Series ISSN 2191-222X Series E-ISSN 2191-2238
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Index of Selected Nuclear Materials and Engineering Systems,
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作者: 江湖郎中    時(shí)間: 2025-3-26 14:52
el understanding of diseases and both the therapeutic and adverse mechanisms of drug actions usingsystem biology approaches. Cutting across the disciplines, this book is a valuable source for researchers in genetics, molecular biology, cell biology, microbiology, and biomedical sciences..?
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GFR) network, our method proposes several new, biologically-viable pathways that explain the evidence for a new signaling pathway. These results demonstrate that the method has potential for aiding biologists in generating hypothetical pathways to explain experimental findings..Our method is impleme
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these models, for .?>?1, are very different from that of the yeast proteome network. We address this problem by introducing a new network growth model that takes into account the sequence similarity between pairs of proteins (as a binary relationship) as well as their interactions. The new model ca
作者: Hallowed    時(shí)間: 2025-3-27 07:50
small number of regulatory prototypes such as transcription factors, underscoring the fact that a small number of regulatory genes have predominant impact to the expression of most of genes and function in a combinatorial manner. The methods allow partitioning genes into mutually non-exclusive trans
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icroT 3.0, a target prediction program, predicted reliable targets from 273 miRNAs. Among them, KeyMolnet, a bioinformatics tool for analyzing molecular interactions on the comprehensive knowledgebase, successfully extracted molecular networks from 232 miRNAs. In miRNA targetome networks, the most r




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