Journal ArticleDOI
Reverse engineering large-scale genetic networks: synthetic versus real data.
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TLDR
The study illustrates that connections of gene networks can be significantly detected via SWNI with high confidence, when single gene perturbation experiments are performed complying with the algorithm requirements.Abstract:
Development of microarray technology has resulted in an exponential rise in gene expression data. Linear computational methods are of great assistance in identifying molecular interactions, and elucidating the functional properties of gene networks. It overcomes the weaknesses of in vivo experiments including high cost, large noise, and unrepeatable process. In this paper, we propose an easily applied system, Stepwise Network Inference (SWNI), which integrates deterministic linear model with statistical analysis, and has been tested effectively on both simulated experiments and real gene expression data sets. The study illustrates that connections of gene networks can be significantly detected via SWNI with high confidence, when single gene perturbation experiments are performed complying with the algorithm requirements. In particular, our algorithm shows efficiency and outperforms the existing ones presented in this paper when dealing with large-scale sparse networks without any prior knowledge.read more
Citations
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Journal ArticleDOI
Identifying Tmem59 related gene regulatory network of mouse neural stem cell from a compendium of expression profiles
TL;DR: The findings suggest that the expression of tmem59 is an important factor contributing to AD and the parallelized SWNI algorithm increased the efficiency of network reconstruction significantly.
Journal ArticleDOI
Investigating meta-approaches for reconstructing gene networks in a mammalian cellular context.
TL;DR: It is demonstrated for biological expression data that the meta-analysis approaches consistently outperformed the best gene regulatory network inference methods in the literature and have a low computational complexity.
Journal ArticleDOI
Structure of Small World Innovation Network and Learning Performance
TL;DR: In this paper, the authors examine the differences of learning performance of 5 MNCs (multinational corporations) that filed the largest number of patents in China, and establish the innovation network with the patent coauthorship data by these 5MNCs and classify the networks by the tail of distribution curve of connections.
References
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Journal ArticleDOI
Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Journal ArticleDOI
Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Proceedings ArticleDOI
The relationship between Precision-Recall and ROC curves
Jesse Davis,Mark Goadrich +1 more
TL;DR: It is shown that a deep connection exists between ROC space and PR space, such that a curve dominates in R OC space if and only if it dominates in PR space.
Journal ArticleDOI
Using Bayesian networks to analyze expression data
TL;DR: A new framework for discovering interactions between genes based on multiple expression measurements is proposed and a method for recovering gene interactions from microarray data is described using tools for learning Bayesian networks.
Journal ArticleDOI
Modeling and simulation of genetic regulatory systems: a literature review.
TL;DR: This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equation, stochastic equations, and so on.