Multiplicity Adjustment for Intersection-union Test: DetectingOverlapping Genes from Multiple Microarray Gene Lists

Author(s):  
Xutao Deng ◽  
Jun Xu ◽  
Charles Wang
Biometrics ◽  
2020 ◽  
Author(s):  
Wujuan Zhong ◽  
Toni Darville ◽  
Xiaojing Zheng ◽  
Jason Fine ◽  
Yun Li

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Nathaly M. Sweeney ◽  
Shareef A. Nahas ◽  
Sh. Chowdhury ◽  
Sergey Batalov ◽  
Michelle Clark ◽  
...  

AbstractCongenital heart disease (CHD) is the most common congenital anomaly and a major cause of infant morbidity and mortality. While morbidity and mortality are highest in infants with underlying genetic conditions, molecular diagnoses are ascertained in only ~20% of cases using widely adopted genetic tests. Furthermore, cost of care for children and adults with CHD has increased dramatically. Rapid whole genome sequencing (rWGS) of newborns in intensive care units with suspected genetic diseases has been associated with increased rate of diagnosis and a net reduction in cost of care. In this study, we explored whether the clinical utility of rWGS extends to critically ill infants with structural CHD through a retrospective review of rWGS study data obtained from inpatient infants < 1 year with structural CHD at a regional children’s hospital. rWGS diagnosed genetic disease in 46% of the enrolled infants. Moreover, genetic disease was identified five times more frequently with rWGS than microarray ± gene panel testing in 21 of these infants (rWGS diagnosed 43% versus 10% with microarray ± gene panels, p = 0.02). Molecular diagnoses ranged from syndromes affecting multiple organ systems to disorders limited to the cardiovascular system. The average daily hospital spending was lower in the time period post blood collection for rWGS compared to prior (p = 0.003) and further decreased after rWGS results (p = 0.000). The cost was not prohibitive to rWGS implementation in the care of this cohort of infants. rWGS provided timely actionable information that impacted care and there was evidence of decreased hospital spending around rWGS implementation.


2007 ◽  
Vol 19 (02) ◽  
pp. 71-78 ◽  
Author(s):  
Cheng-Long Chuang ◽  
Chung-Ming Chen ◽  
Grace S. Shieh ◽  
Joe-Air Jiang

A neuro-fuzzy inference system that recognizes the expression patterns of genes in microarray gene expression (MGE) data, called GeneCFE-ANFIS, is proposed to infer gene interactions. In this study, three primary features are utilized to extract genes' expression patterns and used as inputs to the neuro-fuzzy inference system. The proposed algorithm learns expression patterns from the known genetic interactions, such as the interactions confirmed by qRT-PCR experiments or collected through text-mining technique by surveying previously published literatures, and then predicts other gene interactions according to the learned patterns. The proposed neuro-fuzzy inference system was applied to a public yeast MGE dataset. Two simulations were conducted and checked against 112 pairs of qRT-PCR confirmed gene interactions and 77 TFs (Transcriptional Factors) pairs collected from literature respectively to evaluate the performance of the proposed algorithm.


Sign in / Sign up

Export Citation Format

Share Document