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10.1093/bioinformatics/btac373 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btac373 35640981!ä!35640981 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\35640981.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Bioinformatics 2022 ; 38 (14): 3501-3512 Nephropedia Template TP {{tp|p=35640981|t=2022. DeepLUCIA: predicting tissue-specific chromatin loops using Deep Learning-based Universal Chromatin Interaction Annotator |pdf=|usr=}}{{35640981}} gab.com Text DeepLUCIA: predicting tissue-specific chromatin loops using Deep Learning-based Universal Chromatin Interaction Annotator JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=35640981 #FOAvip MOTIVATION: The importance of chromatin loops in gene regulation is broadly accepted. There are mainly two approaches to predict chromatin loops: transcription factor (TF) binding-dependent approach and genomic variation-based approach. However, neither of these approaches provides an adequate understanding of gene regulation in human tissues. To address this issue, we developed a deep learning-based chromatin loop prediction model called Deep Learning-based Universal Chromatin Interaction Annotator (DeepLUCIA). RESULTS: Although DeepLUCIA does not use TF binding profile data which previous TF binding-dependent methods critically rely on, its prediction accuracies are comparable to those of the previous TF binding-dependent methods. More importantly, DeepLUCIA enables the tissue-specific chromatin loop predictions from tissue-specific epigenomes that cannot be handled by genomic variation-based approach. We demonstrated the utility of the DeepLUCIA by predicting several novel target genes of SNPs identified in genome-wide association studies targeting Brugada syndrome, COVID-19 severity and age-related macular degeneration. Availability and implementation DeepLUCIA is freely available at https://github.com/bcbl-kaist/DeepLUCIA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Twit Text FOAVip DeepLUCIA: predicting tissue-specific chromatin loops using Deep Learning-based Universal Chromatin Interaction Annotator ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=35640981 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35640981 #FOAvip English Wikipedia <ref>{{Cite pmid|35640981}}</ref> DeepLUCIA: predicting tissue-specific chromatin loops using Deep Learning-based Universal Chromatin Interaction Annotator #MMPMID35640981 Yang D; Chung T; Kim D Bioinformatics 2022[Jul]; 38 (14): 3501-3512 PMID35640981show ga MOTIVATION: The importance of chromatin loops in gene regulation is broadly accepted. There are mainly two approaches to predict chromatin loops: transcription factor (TF) binding-dependent approach and genomic variation-based approach. However, neither of these approaches provides an adequate understanding of gene regulation in human tissues. To address this issue, we developed a deep learning-based chromatin loop prediction model called Deep Learning-based Universal Chromatin Interaction Annotator (DeepLUCIA). RESULTS: Although DeepLUCIA does not use TF binding profile data which previous TF binding-dependent methods critically rely on, its prediction accuracies are comparable to those of the previous TF binding-dependent methods. More importantly, DeepLUCIA enables the tissue-specific chromatin loop predictions from tissue-specific epigenomes that cannot be handled by genomic variation-based approach. We demonstrated the utility of the DeepLUCIA by predicting several novel target genes of SNPs identified in genome-wide association studies targeting Brugada syndrome, COVID-19 severity and age-related macular degeneration. Availability and implementation DeepLUCIA is freely available at https://github.com/bcbl-kaist/DeepLUCIA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |*COVID-19[MESH] |*Deep Learning[MESH] |Chromatin[MESH] |Genome-Wide Association Study[MESH] |Genomics/methods[MESH]DeepLUCIA: predicting tissue-specific chromatin loops using Deep Learn(epmc).pdf DeepDyve Pubget Overpricing