Comput Math Methods Med - Dynamic regulatory network reconstruction for Alzheimer's disease based on matrix decomposition techniques.

Tópicos

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Resumo

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.

Resumo Limpo

alzheim diseas ad common form dementia lead irrevers neurodegen damag brain find dynam respons gene signal protein transcript factor tf activ regulatori network progress deterior progress ad repres signific advanc discov pathogenesi ad howev high throughput technolog measur tf activ yet avail genomewid scale studi base dna microarray gene express data priori inform tfs network compon analysi nca algorithm appli determin tf activ regulatori influenc tgs incipi moder sever ad base dynam gene regulatori network deterior cours ad reconstruct select signific gene differenti express differ cours ad independ compon analysi ica better tradit cluster method can success group one gene differ meaning biolog process use molecular biolog analysi show chang tf activ interact signal protein mitosi cell cycl immun respons inflamm play import role deterior ad

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