<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>4</volume><submitter>Kumar R</submitter><pubmed_abstract>Nuclear receptor proteins (NRP) are transcription factor that regulate many vital cellular processes in animal cells. NRPs form a super-family of phylogenetically related proteins and divided into different sub-families on the basis of ligand characteristics and their functions. In the post-genomic era, when new proteins are being added to the database in a high-throughput mode, it becomes imperative to identify new NRPs using information from amino acid sequence alone. In this study we report a SVM based two level prediction systems, NRfamPred, using dipeptide composition of proteins as input. At the 1st level, NRfamPred screens whether the query protein is NRP or non-NRP; if the query protein belongs to NRP class, prediction moves to 2nd level and predicts the sub-family. Using leave-one</pubmed_abstract><journal>Scientific reports</journal><pagination>6810</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC5381360</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>NRfamPred: a proteome-scale two level method for prediction of nuclear receptor proteins and their sub-families.</pubmed_title><pmcid>PMC5381360</pmcid><pubmed_authors>Kumar M</pubmed_authors><pubmed_authors>Kumari B</pubmed_authors><pubmed_authors>Kumar R</pubmed_authors><pubmed_authors>Srivastava A</pubmed_authors></additional><is_claimable>false</is_claimable><name>NRfamPred: a proteome-scale two level method for prediction of nuclear receptor proteins and their sub-families.</name><description>Nuclear receptor proteins (NRP) are transcription factor that regulate many vital cellular processes in animal cells. NRPs form a super-family of phylogenetically related proteins and divided into different sub-families on the basis of ligand characteristics and their functions. In the post-genomic era, when new proteins are being added to the database in a high-throughput mode, it becomes imperative to identify new NRPs using information from amino acid sequence alone. In this study we report a SVM based two level prediction systems, NRfamPred, using dipeptide composition of proteins as input. At the 1st level, NRfamPred screens whether the query protein is NRP or non-NRP; if the query protein belongs to NRP class, prediction moves to 2nd level and predicts the sub-family. Using leave-one</description><dates><release>2014-01-01T00:00:00Z</release><publication>2014 Oct</publication><modification>2025-04-04T21:39:47.961Z</modification><creation>2019-03-27T02:40:26Z</creation></dates><accession>S-EPMC5381360</accession><cross_references><pubmed>25351274</pubmed><doi>10.1038/srep06810</doi></cross_references></HashMap>