title: Dynamic regulatory network reconstruction for Alzheimer's disease based on matrix decomposition techniques.
abstract: Alzheimer's disease (AD) is the most common form of dementia and leads to irreversible neurodegenerative damage of the brain. Finding the dynamic responses of genes, signaling proteins, transcription factor (TF) activities, and regulatory networks of the progressively deteriorative progress of AD would represent a significant advance in discovering the pathogenesis of AD. However, the high throughput technologies of measuring TF activities are not yet available on a genome-wide scale. In this study, based on DNA microarray gene expression data and a priori information of TFs, network component analysis (NCA) algorithm is applied to determining the TF activities and regulatory influences on TGs of incipient, moderate, and severe AD. Based on that, the dynamical gene regulatory networks of the deteriorative courses of AD were reconstructed. To select significant genes which are differentially expressed in different courses of AD, independent component analysis (ICA), which is better than the traditional clustering methods and can successfully group one gene in different meaningful biological processes, was used. The molecular biological analysis showed that the changes of TF activities and interactions of signaling proteins in mitosis, cell cycle, immune response, and inflammation play an important role in the deterioration of AD.
authors.affiliation: Information Engineering College, Shanghai Maritime University, Shanghai 201306, China.
authors.forename: Wei
authors.identifier: 0000-0002-6554-4338
authors.initials: W
authors.lastname: Kong
authors.affiliation: DNJ Pharma and Rowan University, NJ 08028, USA.
authors.forename: Xiaoyang
authors.initials: X
authors.lastname: Mou
authors.affiliation: Information Engineering College, Shanghai Maritime University, Shanghai 201306, China.
authors.forename: Xing
authors.initials: X
authors.lastname: Zhi
authors.affiliation: Information Engineering College, Shanghai Maritime University, Shanghai 201306, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000473:pathology; D000818:Animals; D001921:Brain / Q000473:pathology*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D007249:Inflammation; D051379:Mice; D015233:Models, Statistical; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012984:Software; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014157:Transcription Factors