Abstract
Here we develop a new approach of combined literature mining and gene expression analysis (CLMGE) to model the complex neuro-endocrine-immune (NEI) interactions. By using NEI related PubMed abstracts and the Human Genome Organisation gene glossary for subject oriented literature mining (SOLM), it is found that the NEI model serves as a scale-free network and the degree of nodes follows a power-law distribution. Then we evaluate and eliminate the redundant of SOLM-based NEI model by multivariate statistic analysis basing on selected gene expression data. Each involving expression data is tested by cross validation with Leave One Out strategy. The results suggest that the performance of CLMGE approach is much better than that of SOLM alone. The integrated strategy of CLMGE can not only eliminate false positive relations obtained by SOLM, but also form a suitable solution space for analyzing gene expression data. The reasonable biological meanings of the CLMGE-based NEI model are also evaluated and demonstrated by classifying its sub-functions according to DAVID and SwissProt databases.
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Wu, L., Li, S. (2005). Combined Literature Mining and Gene Expression Analysis for Modeling Neuro-endocrine-immune Interactions. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538356_4
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DOI: https://doi.org/10.1007/11538356_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28227-3
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