Zobrazeno 1 - 10
of 284
pro vyhledávání: '"Zhang Xiang-Sun"'
Publikováno v:
BMC Bioinformatics, Vol 13, Iss 1, p 70 (2012)
Abstract Background Gene expression profiling technologies have gradually become a community standard tool for clinical applications. For example, gene expression data has been analyzed to reveal novel disease subtypes (class discovery) and assign pa
Externí odkaz:
https://doaj.org/article/b4fdfa625ffe432abc2944162d99bcd6
Publikováno v:
BMC Bioinformatics, Vol 12, Iss 1, p 409 (2011)
Abstract Background With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason
Externí odkaz:
https://doaj.org/article/035c3ddcfcce4316b6ac8d583b0ccd04
Publikováno v:
BMC Bioinformatics, Vol 11, Iss 1, p 26 (2010)
Abstract Background The accumulation of high-throughput data greatly promotes computational investigation of gene function in the context of complex biological systems. However, a biological function is not simply controlled by an individual gene sin
Externí odkaz:
https://doaj.org/article/d49e7a1e554b4091aab5b0990a49d39d
Publikováno v:
BMC Bioinformatics, Vol 8, Iss 1, p 475 (2007)
Abstract Background Annotation of protein functions is an important task in the post-genomic era. Most early approaches for this task exploit only the sequence or global structure information. However, protein surfaces are believed to be crucial to p
Externí odkaz:
https://doaj.org/article/7e54388a9e634c9b8c5b087b5dfe3969
Publikováno v:
BMC Bioinformatics, Vol 8, Iss 1, p 391 (2007)
Abstract Background Domains are the basic functional units of proteins. It is believed that protein-protein interactions are realized through domain interactions. Revealing multi-domain cooperation can provide deep insights into the essential mechani
Externí odkaz:
https://doaj.org/article/f0aa8952eec14e3cb05531a79378ce0a
Potts model based on a Markov process computation solves the community structure problem effectively
Publikováno v:
Physical Review E, 86(1), 012801, 2012
Potts model is a powerful tool to uncover community structure in complex networks. Here, we propose a new framework to reveal the optimal number of communities and stability of network structure by quantitatively analyzing the dynamics of Potts model
Externí odkaz:
http://arxiv.org/abs/1503.08035
Identifying overlapping communities in social networks using multi-scale local information expansion
Publikováno v:
European Physical Journal B, 85(6), 109, 2012
Most existing approaches for community detection require complete information of the graph in a specific scale, which is impractical for many social networks. We propose a novel algorithm that does not embrace the universal approach but instead of tr
Externí odkaz:
http://arxiv.org/abs/1503.08024
Autor:
Li, Hui-Jia, Zhang, Xiang-Sun
Publikováno v:
EPL (Europhysics Letters),103(5),2013
The analysis of stability of community structure is an important problem for scientists from many fields. Here, we propose a new framework to reveal hidden properties of community structure by quantitatively analyzing the dynamics of Potts model. Spe
Externí odkaz:
http://arxiv.org/abs/1503.08018
Community detection in complex networks is a topic of high interest in many fields. Bipartite networks are a special type of complex networks in which nodes are decomposed into two disjoint sets, and only nodes between the two sets can be connected.
Externí odkaz:
http://arxiv.org/abs/1501.00432
Autor:
Csermely, Peter, Hodsagi, Janos, Korcsmaros, Tamas, Modos, Dezso, Perez-Lopez, Aron R., Szalay, Kristof, Veres, Daniel V., Lenti, Katalin, Wu, Ling-Yun, Zhang, Xiang-Sun
Publikováno v:
Seminars in Cancer Biology 30 (2015) 42-51
Cancer is increasingly perceived as a systems-level, network phenomenon. The major trend of malignant transformation can be described as a two-phase process, where an initial increase of network plasticity is followed by a decrease of plasticity at l
Externí odkaz:
http://arxiv.org/abs/1312.6356