NGF Increases Cell Viability of Isolated Plasma Cells from Inflamed Airways via TrkA Signalling in a Mouse Model of Allergic Asthma

Abram, Melanie

doi:10.1016/j.jaci.2007.12.745

Cited by 2 publications

(1 citation statement)

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“…Among the 72 pathways, two are included in the 23 known asthma-related pathways and the rest 70 are the newly asthma-related pathways predicted by the algorithm. For the 70 pathways, five pathways, “h-il7Pathway”, “h-pkcPathway”, “h-melanocytepathway”, “h-ngfPathway”, and “h-trkaPathway”, are considered to be associated with asthma in previous literature ( Kelly and et al, 2009 ; Hou and et al, 2017 ; Raap et al, 2003 ; Abram, 2008 ).…”

Section: Resultsmentioning

confidence: 99%

Locally Adjust Networks Based on Connectivity and Semantic Similarities for Disease Module Detection

2021

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For studying the pathogenesis of complex diseases, it is important to identify the disease modules in the system level. Since the protein-protein interaction (PPI) networks contain a number of incomplete and incorrect interactome, most existing methods often lead to many disease proteins isolating from disease modules. In this paper, we propose an effective disease module identification method IDMCSS, where the used human PPI networks are obtained by adding some potential missing interactions from existing PPI networks, as well as removing some potential incorrect interactions. In IDMCSS, a network adjustment strategy is developed to add or remove links around disease proteins based on both topological and semantic information. Next, neighboring proteins of disease proteins are prioritized according to a suggested similarity between each of them and disease proteins, and the protein with the largest similarity with disease proteins is added into a candidate disease protein set one by one. The stopping criterion is set to the boundary of the disease proteins. Finally, the connected subnetwork having the largest number of disease proteins is selected as a disease module. Experimental results on asthma demonstrate the effectiveness of the method in comparison to existing algorithms for disease module identification. It is also shown that the proposed IDMCSS can obtain the disease modules having crucial biological processes of asthma and 12 targets for drug intervention can be predicted.

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