scholarly journals Why is there selective subcortical vulnerability in ADHD? Clues from postmortem brain gene expression data

2017 ◽  
Vol 23 (8) ◽  
pp. 1787-1793 ◽  
Author(s):  
J L Hess ◽  
G C Akutagava-Martins ◽  
J D Patak ◽  
S J Glatt ◽  
S V Faraone
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Postmortem Brain ◽  
Expression Data ◽  
Brain Gene Expression

scholarly journals Insights into TREM2 biology by network analysis of human brain gene expression data

2013 ◽  
Vol 34 (12) ◽  
pp. 2699-2714 ◽  
Author(s):  
Paola Forabosco ◽  
Adaikalavan Ramasamy ◽  
Daniah Trabzuni ◽  
Robert Walker ◽  
Colin Smith ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Human Brain ◽  
Gene Expression Data ◽  
Expression Data ◽  
Brain Gene Expression

scholarly journals Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Jun Wang ◽  
Liangjiang Wang
Keyword(s):  
Gene Expression ◽  
Random Forest ◽  
Gene Expression Data ◽  
Autism Spectrum ◽  
Expression Data ◽  
Risk Genes ◽  
Sequence Features ◽  
Brain Gene Expression ◽  
Transcript Sequence ◽  
Non Coding Rnas

Abstract Background Autism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which are genetically complex and heterogeneous with most of the genetic risk factors also found in the unaffected general population. Although all the currently known ASD risk genes code for proteins, long non-coding RNAs (lncRNAs) as essential regulators of gene expression have been implicated in ASD. Some lncRNAs show altered expression levels in autistic brains, but their roles in ASD pathogenesis are still unclear. Results In this study, we have developed a new machine learning approach to predict candidate lncRNAs associated with ASD. Particularly, the knowledge learnt from protein-coding ASD risk genes was transferred to the prediction and prioritization of ASD-associated lncRNAs. Both developmental brain gene expression data and transcript sequence were found to contain relevant information for ASD risk gene prediction. During the pre-training phase of model construction, an autoencoder network was implemented for a representation learning of the gene expression data, and a random-forest-based feature selection was applied to the transcript-sequence-derived k-mers. Our models, including logistic regression, support vector machine and random forest, showed robust performance based on tenfold cross-validations as well as candidate prioritization with hypothetical loci. We then utilized the models to predict and prioritize a list of candidate lncRNAs, including some reported to be cis-regulators of known ASD risk genes, for further investigation. Conclusions Our results suggest that ASD risk genes can be accurately predicted using developmental brain gene expression data and transcript sequence features, and the models may provide useful information for functional characterization of the candidate lncRNAs associated with ASD.

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