{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang H"],"funding":["NICHD NIH HHS","NIH/NICHD"],"pagination":["14"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8740472"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["23(1)"],"pubmed_abstract":["<h4>Background</h4>Interferon regulatory factor-8 (IRF8) and nuclear factor-activated T cells c1 (NFATc1) are two transcription factors that have an important role in osteoclast differentiation. Thanks to ChIP-seq technology, scientists can now estimate potential genome-wide target genes of IRF8 and NFATc1. However, finding target genes that are consistently up-regulated or down-regulated across different studies is hard because it requires analysis of a large number of high-throughput expression studies from a comparable context.<h4>Method</h4>We have developed a machine learning based method, called, Cohort-based TF target prediction system (cTAP) to overcome this problem. This method assumes that the pathway involving the transcription factors of interest is featured with multiple \"func"],"journal":["BMC genomics"],"pubmed_title":["Predicting the targets of IRF8 and NFATc1 during osteoclast differentiation using the machine learning method framework cTAP."],"pmcid":["PMC8740472"],"funding_grant_id":["R01 HD098636","1R01HD098636-0"],"pubmed_authors":["Joshi P","Wang H","Maye PF","Rowe DW","Hong SH","Shin DG"],"additional_accession":[]},"is_claimable":false,"name":"Predicting the targets of IRF8 and NFATc1 during osteoclast differentiation using the machine learning method framework cTAP.","description":"<h4>Background</h4>Interferon regulatory factor-8 (IRF8) and nuclear factor-activated T cells c1 (NFATc1) are two transcription factors that have an important role in osteoclast differentiation. Thanks to ChIP-seq technology, scientists can now estimate potential genome-wide target genes of IRF8 and NFATc1. However, finding target genes that are consistently up-regulated or down-regulated across different studies is hard because it requires analysis of a large number of high-throughput expression studies from a comparable context.<h4>Method</h4>We have developed a machine learning based method, called, Cohort-based TF target prediction system (cTAP) to overcome this problem. This method assumes that the pathway involving the transcription factors of interest is featured with multiple \"func","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan","modification":"2025-04-21T17:38:42.58Z","creation":"2022-02-11T15:09:04.606Z"},"accession":"S-EPMC8740472","cross_references":{"pubmed":["34991467"],"doi":["10.1186/s12864-021-08159-z"]}}