DROP: an SVM domain linker predictor trained with optimal features selected by random forest.
about
Prediction of protein domain with mRMR feature selection and analysisPlant-PrAS: a database of physicochemical and structural properties and novel functional regions in plant proteomes.Domain position prediction based on sequence information by using fuzzy mean operator.PDP-CON: prediction of domain/linker residues in protein sequences using a consensus approach.DomHR: accurately identifying domain boundaries in proteins using a hinge region strategy.A Novel Feature Extraction Method with Feature Selection to Identify Golgi-Resident Protein Types from Imbalanced DataDetermining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.Alga-PrAS (Algal Protein Annotation Suite): A Database of Comprehensive Annotation in Algal ProteomesFunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest modelAn integrative computational framework based on a two-step random forest algorithm improves prediction of zinc-binding sites in proteinsPROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites.Protein inter-domain linker prediction using Random Forest and amino acid physiochemical propertiesExtending Protein Domain Boundary Predictors to Detect Discontinuous DomainsThreaDom: extracting protein domain boundary information from multiple threading alignments.DPClass: An Effective but Concise Discriminative Patterns-Based Classification Framework.ThreaDomEx: a unified platform for predicting continuous and discontinuous protein domains by multiple-threading and segment assembly.iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian naïve Bayes.IS-Dom: a dataset of independent structural domains automatically delineated from protein structures.Fast H-DROP: A thirty times accelerated version of H-DROP for interactive SVM-based prediction of helical domain linkers.H-DROP: an SVM based helical domain linker predictor trained with features optimized by combining random forest and stepwise selection.
P2860
Q28480439-B8B90642-4A4A-461E-8F48-A71CA182FA35Q30369021-422ED2D5-A9ED-4DE3-97EC-C72C184B6507Q30374966-4243B0F9-1924-4C5F-A9CB-D3DEE537BCC9Q30385580-60D8B5A1-A078-4451-804B-8625853F05B0Q30429496-ACE0AA02-5A17-43D4-A2C7-234B201B3AA5Q31043932-6FC1B558-E307-4051-B98A-6D7577A9CD3EQ33552939-E63D7032-D6A2-476F-8DE4-098D93F561C0Q33727727-D3D39C58-8CDC-498D-987A-29AE00D84361Q34399466-911AF1FA-60F0-41DF-A674-D5707E498139Q34482705-FAD02920-2CDF-4061-B6A8-2E9BF4947208Q34500105-BA004340-BC24-445E-9E88-6F5724371A92Q34913656-C50C1ECE-B0E1-4491-AC6A-F0B7F5298D11Q35821441-0CC14A2D-95AC-41E8-B94A-EE157E0F8192Q36960736-2635E7C9-6A4B-424C-846A-CA8272FE2BABQ37619193-90038888-7C63-4EA5-A384-5076FB8D43E8Q38789142-917F6EC1-D9B3-4D23-972C-83261BEFE759Q40593974-7437DFF1-3675-4A05-A71B-0AE620809CFBQ41840491-01C4E02F-0C51-48BB-A10A-1CF274C59831Q44779922-D126526C-DD5A-4EF9-A7B6-4C15AFA7FB39Q47845562-3EB735BA-E982-4480-8538-87ED9872DBC2Q51074171-1ADC54E0-3838-496E-9566-0CB2F8179447
P2860
DROP: an SVM domain linker predictor trained with optimal features selected by random forest.
description
2010 nî lūn-bûn
@nan
2010年の論文
@ja
2010年学术文章
@wuu
2010年学术文章
@zh-cn
2010年学术文章
@zh-hans
2010年学术文章
@zh-my
2010年学术文章
@zh-sg
2010年學術文章
@yue
2010年學術文章
@zh
2010年學術文章
@zh-hant
name
DROP: an SVM domain linker pre ...... res selected by random forest.
@en
DROP: an SVM domain linker pre ...... res selected by random forest.
@nl
type
label
DROP: an SVM domain linker pre ...... res selected by random forest.
@en
DROP: an SVM domain linker pre ...... res selected by random forest.
@nl
prefLabel
DROP: an SVM domain linker pre ...... res selected by random forest.
@en
DROP: an SVM domain linker pre ...... res selected by random forest.
@nl
P356
P1433
P1476
DROP: an SVM domain linker pre ...... res selected by random forest.
@en
P2093
Hiroyuki Toh
Yutaka Kuroda
P304
P356
10.1093/BIOINFORMATICS/BTQ700
P407
P50
P577
2010-12-17T00:00:00Z