scholarly journals Analysis on Differential Gene Expression Data for Prediction of New Biological Features in Permanent Atrial Fibrillation

PLoS ONE ◽  
2013 ◽  
Vol 8 (10) ◽  
pp. e76166 ◽  
Author(s):  
Feng Ou ◽  
Nini Rao ◽  
Xudong Jiang ◽  
Mengyao Qian ◽  
Wei Feng ◽  
...  
2021 ◽  
Author(s):  
Magdalena Navarro ◽  
T Ian Simpson

AbstractMotivationAutism spectrum disorder (ASD) has a strong, yet heterogeneous, genetic component. Among the various methods that are being developed to help reveal the underlying molecular aetiology of the disease, one that is gaining popularity is the combination of gene expression and clinical genetic data. For ASD, the SFARI-gene database comprises lists of curated genes in which presumed causative mutations have been identified in patients. In order to predict novel candidate SFARI-genes we built classification models combining differential gene expression data for ASD patients and unaffected individuals with a gene’s status in the SFARI-gene list.ResultsSFARI-genes were not found to be significantly associated with differential gene expression patterns, nor were they enriched in gene co-expression network modules that had a strong correlation with ASD diagnosis. However, network analysis and machine learning models that incorporate information from the whole gene co-expression network were able to predict novel candidate genes that share features of existing SFARI genes and have support for roles in ASD in the literature. We found a statistically significant bias related to the absolute level of gene expression for existing SFARI genes and their scores. It is essential that this bias be taken into account when studies interpret ASD gene expression data at gene, module and whole-network levels.AvailabilitySource code is available from GitHub (https://doi.org/10.5281/zenodo.4463693) and the accompanying data from The University of Edinburgh DataStore (https://doi.org/10.7488/ds/2980)[email protected]


2013 ◽  
Vol 29 (5) ◽  
pp. 622-629 ◽  
Author(s):  
Christopher L. Poirel ◽  
Ahsanur Rahman ◽  
Richard R. Rodrigues ◽  
Arjun Krishnan ◽  
Jacqueline R. Addesa ◽  
...  

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 870
Author(s):  
Leonid Bystrykh

Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.


2006 ◽  
pp. 223-238 ◽  
Author(s):  
Michael A. Langston ◽  
Lan Lin ◽  
Xinxia Peng ◽  
Nicole E. Baldwin ◽  
Christopher T. Symons ◽  
...  

2006 ◽  
Vol 7 (Suppl 4) ◽  
pp. S7 ◽  
Author(s):  
Lily R Liang ◽  
Shiyong Lu ◽  
Xuena Wang ◽  
Yi Lu ◽  
Vinay Mandal ◽  
...  

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