{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["10"],"submitter":["Yan RX"],"pubmed_abstract":["<h4>Background</h4>Machine learning-based methods have been proven to be powerful in developing new fold recognition tools. In our previous work [Zhang, Kochhar and Grigorov (2005) Protein Science, 14: 431-444], a machine learning-based method called DescFold was established by using Support Vector Machines (SVMs) to combine the following four descriptors: a profile-sequence-alignment-based descriptor using Psi-blast e-values and bit scores, a sequence-profile-alignment-based descriptor using Rps-blast e-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. In this work, we focus on the improvement of DescFold by incorporating more powerful descriptors and setting up a user-friendly "],"journal":["BMC bioinformatics"],"pagination":["416"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC2803855"],"repository":["biostudies-literature"],"pubmed_title":["DescFold: a web server for protein fold recognition."],"pmcid":["PMC2803855"],"pubmed_authors":["Zhang Z","Wang C","Yan RX","Si JN"],"additional_accession":[]},"is_claimable":false,"name":"DescFold: a web server for protein fold recognition.","description":"<h4>Background</h4>Machine learning-based methods have been proven to be powerful in developing new fold recognition tools. In our previous work [Zhang, Kochhar and Grigorov (2005) Protein Science, 14: 431-444], a machine learning-based method called DescFold was established by using Support Vector Machines (SVMs) to combine the following four descriptors: a profile-sequence-alignment-based descriptor using Psi-blast e-values and bit scores, a sequence-profile-alignment-based descriptor using Rps-blast e-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. In this work, we focus on the improvement of DescFold by incorporating more powerful descriptors and setting up a user-friendly ","dates":{"release":"2009-01-01T00:00:00Z","publication":"2009 Dec","modification":"2026-05-03T20:13:32.432Z","creation":"2026-04-07T19:38:30.191Z"},"accession":"S-EPMC2803855","cross_references":{"pubmed":["20003426"],"doi":["10.1186/1471-2105-10-416"]}}