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10.1093/bib/bbaa360 http://scihub22266oqcxt.onion/10.1093/bib/bbaa360 33381809!7799288!33381809     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Brief+Bioinform 2021 ; 22 (4): ä Nephropedia Template TP {{tp|p=33381809|t=2021. Improving gene network inference with graph wavelets and making insights about ageing-associated regulatory changes in lungs |pdf=|usr=}}{{33381809}} gab.com Text Improving gene network inference with graph wavelets and making insights about ageing-associated regulatory changes in lungs JO: Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=33381809 #FOAvip Using gene-regulatory-networks-based approach for single-cell expression profiles can reveal unprecedented details about the effects of external and internal factors. However, noise and batch effect in sparse single-cell expression profiles can hamper correct estimation of dependencies among genes and regulatory changes. Here, we devise a conceptually different method using graphwavelet filters for improving gene network (GWNet)-based analysis of the transcriptome. Our approach improved the performance of several gene network-inference methods. Most Importantly, GWNet improved consistency in the prediction of gene regulatory network using single-cell transcriptome even in the presence of batch effect. The consistency of predicted gene network enabled reliable estimates of changes in the influence of genes not highlighted by differential-expression analysis. Applying GWNet on the single-cell transcriptome profile of lung cells, revealed biologically relevant changes in the influence of pathways and master regulators due to ageing. Surprisingly, the regulatory influence of ageing on pneumocytes type II cells showed noticeable similarity with patterns due to the effect of novel coronavirus infection in human lung. Twit Text FOAVip Improving gene network inference with graph wavelets and making insights about ageing-associated regulatory changes in lungs ????Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=33381809 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33381809 #FOAvip English Wikipedia <ref>{{Cite pmid|33381809}}</ref> Improving gene network inference with graph wavelets and making insights about ageing-associated regulatory changes in lungs #MMPMID33381809 Mishra S; Srivastava D; Kumar V Brief Bioinform 2021[Jul]; 22 (4): ä PMID33381809show ga Using gene-regulatory-networks-based approach for single-cell expression profiles can reveal unprecedented details about the effects of external and internal factors. However, noise and batch effect in sparse single-cell expression profiles can hamper correct estimation of dependencies among genes and regulatory changes. Here, we devise a conceptually different method using graphwavelet filters for improving gene network (GWNet)-based analysis of the transcriptome. Our approach improved the performance of several gene network-inference methods. Most Importantly, GWNet improved consistency in the prediction of gene regulatory network using single-cell transcriptome even in the presence of batch effect. The consistency of predicted gene network enabled reliable estimates of changes in the influence of genes not highlighted by differential-expression analysis. Applying GWNet on the single-cell transcriptome profile of lung cells, revealed biologically relevant changes in the influence of pathways and master regulators due to ageing. Surprisingly, the regulatory influence of ageing on pneumocytes type II cells showed noticeable similarity with patterns due to the effect of novel coronavirus infection in human lung. |*Gene Regulatory Networks[MESH] |COVID-19/*genetics/pathology/virology[MESH] |Cellular Senescence/*genetics[MESH] |Gene Expression Profiling[MESH] |Humans[MESH] |Lung/metabolism/*pathology[MESH] |SARS-CoV-2/isolation & purification[MESH] |Sequence Analysis, RNA[MESH] |Single-Cell Analysis[MESH]Improving gene network inference with graph wavelets and making insigh(epmc).pdf DeepDyve Pubget Overpricing