Analysis of gene expression and connectivity on hippocampus of Alzheimer's disease by a new comprehensive approach
Jiang, S.
Show abstract
Gene expression and gene connectivity describe two different functional aspects of a gene. These two different measures reveal different information about the involvement of genes in disorders. Previous case-control gene expression studies have often focused on expression level of individual genes. Correlated expression relationships among genes, measured as gene connectivity, have obtained limited attention. We developed a comprehensive method, TRIple Differentiation (TRID), to assess these two measures, both separately and jointly. We applied TRID to gene expression data in hippocampus tissue samples from three Alzheimers disease (AD) microarray datasets. Following TRID, comparisons among the three datasets showed poor consistency for disease-associated individual genes but reproducible changes of disease-associated biological pathways annotated for functional protein-protein interaction (PPI) modules identified from network analysis. Our results suggest that changes of gene expression in hippocampus of AD patients are highly heterogeneous at the individual gene level, while biological pathways annotated for PPI modules identified based on TRID weights demonstrate consistency among the three datasets. The R package TRID can be accessed from GitHub (https://github.com/shannjiang/TRID).
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Module analysis using single-patient differential expression signatures improve the power of association study for Alzheimer's disease 96%
- Genetic Influence underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts 93%
- Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma using Large-scale Transcriptomics Data 92%
Similar papers in this journal
- Global chemical modifications comparison of human plasma proteomes from two different age groups 93%
- Candidate genes associated with neurological manifestations of COVID-19: Meta-analysis using multiple computational approaches 92%
- Identification of miRNA signatures for kidney renal clear cell carcinoma using the tensor-decomposition method 92%
Similar papers in this journal
- Biological and Disease Hallmarks of Alzheimer’s Disease Defined by Alzheimer’s Disease Genes 94%
- Impaired learning and memory ability induced by a bilaterally hippocampal injection of streptozotocin in mice: involved with the adaptive changes of synaptic plasticity 93%
- Early functional and cognitive declines measured by auditory evoked cortical potentials in Alzheimer's disease mice 92%
Similar papers in this journal
- Identification of functionally connected multi-omic biomarkers for Alzheimer’s Disease using modularity-constrained Lasso 95%
- Olfactory Response as a Marker for Alzheimer's Disease: Evidence from Perceptual and Frontal Oscillation Coherence Deficit 92%
- c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer’s disease 92%
Similar papers in this journal
- Genetic Networks of Alzheimer’s Disease, Aging and Longevity in Humans 95%
- Biology of healthy aging: Biological hallmarks of stress resistance-related and unrelated to longevity in humans 93%
- SenolyticSynergy: An Attention-Based Network for Discovering Novel Senolytic Combinations via Human Aging Genomics 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.