Putative Factors Interfering Cell Cycle Re-Entry in Alzheimer's Disease: An Omics Study with Differential Expression Meta-Analytics and Co-Expression Profiling.

Background: Neuronal cell cycle re-entry (CCR) is a mechanism, along with amyloid-β (Aβ) oligomers and hyperphosphorylated tau proteins, contributing to toxicity in Alzheimer's disease (AD).

Objective: This study aimed to examine the putative factors in CCR based on evidence corroboration by combining meta-analysis and co-expression analysis of omic data.

Methods: The differentially expressed genes (DEGs) and CCR-related modules were obtained through the differential analysis and co-expression of transcriptomic data, respectively.

Prediction of differentially expressed microRNAs in blood as potential biomarkers for Alzheimer's disease by meta-analysis and adaptive boosting ensemble learning.

Background: Blood circulating microRNAs that are specific for Alzheimer's disease (AD) can be identified from differentially expressed microRNAs (DEmiRNAs). However, non-reproducible and inconsistent reports of DEmiRNAs hinder biomarker development. The most reliable DEmiRNAs can be identified by meta-analysis.

A Systematic Bioinformatics Workflow With Meta-Analytics Identified Potential Pathogenic Factors of Alzheimer's Disease.

Potential pathogenic factors, other than well-known , , and , can be further identified from transcriptomics studies of differentially expressed genes (DEGs) that are specific for Alzheimer's disease (AD), but findings are often inconsistent or even contradictory. Evidence corroboration by combining meta-analysis and bioinformatics methods may help to resolve existing inconsistencies and contradictions. This study aimed to demonstrate a systematic workflow for evidence synthesis of transcriptomic studies using both meta-analysis and bioinformatics methods to identify potential pathogenic factors.

Identification of certain Panax species to be potential substitutes for Panax notoginseng in hemostatic treatments.

Panax notoginseng (Burkill) F. H. Chen ex C.

Building Molecular Interaction Networks from Microarray Data for Drug Target Screening.

Potential drug targets for the disease treatment can be identified from microarray studies on differential gene expression of patients and healthy participants. Here, we describe a method to use the information of differentially expressed (DE) genes obtained from microarray studies to build molecular interaction networks for identification of pivotal molecules as potential drug targets. The quality control and normalization of the microarray data are conducted with R packages simpleaffy and affy, respectively.