Improving the accuracy of protein secondary structure prediction using structural alignment
about
PROTEUS2: a web server for comprehensive protein structure prediction and structure-based annotationGlobal analysis of lysine acetylation suggests the involvement of protein acetylation in diverse biological processes in rice (Oryza sativa)The Jpred 3 secondary structure prediction serverPrediction of protein secondary structure using probability based features and a hybrid system.PPT-DB: the protein property prediction and testing database.Template-based C8-SCORPION: a protein 8-state secondary structure prediction method using structural information and context-based featuresCSI 2.0: a significantly improved version of the Chemical Shift Index.Bayesian model of protein primary sequence for secondary structure prediction.SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.Protein contact order prediction from primary sequences.Prediction of protein secondary structures with a novel kernel density estimation based classifier.Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments.PCI-SS: MISO dynamic nonlinear protein secondary structure prediction.Template-based protein modeling: recent methodological advances.Positive selection differs between protein secondary structure elements in DrosophilaProtein secondary structure prediction using a small training set (compact model) combined with a Complex-valued neural network approach.Sixty-five years of the long march in protein secondary structure prediction: the final stretch?Using predicted shape string to enhance the accuracy of γ-turn prediction.Improving the performance of β-turn prediction using predicted shape strings and a two-layer support vector machine modelProtein folding: a problem with multiple solutions.Predicting β-turns in protein using kernel logistic regressionComparative sequence analysis of leucine-rich repeats (LRRs) within vertebrate toll-like receptors.Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information.Ab initio and homology based prediction of protein domains by recursive neural networksProteomic changes associated with deletion of the Magnaporthe oryzae conidial morphology-regulating gene COM1.Improving protein secondary structure prediction based on short subsequences with local structure similarityDistributions of amino acids suggest that certain residue types more effectively determine protein secondary structure.DIALIGN-TX and multiple protein alignment using secondary structure information at GOBICSSPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion anglesFast learning optimized prediction methodology (FLOPRED) for protein secondary structure predictionMapping membrane activity in undiscovered peptide sequence space using machine learning.Predicting beta-turns in proteins using support vector machines with fractional polynomials.From local structure to a global framework: recognition of protein folds.Towards more accurate prediction of protein folding rates: a review of the existing Web-based bioinformatics approaches.Machine learning-enabled discovery and design of membrane-active peptides.Genome wide identification of Acidithiobacillus ferrooxidans (ATCC 23270) transcription factors and comparative analysis of ArsR and MerR metal regulators.Comprehensively designed consensus of standalone secondary structure predictors improves Q3 by over 3%.A Structural Study of CESA1 Catalytic Domain of Arabidopsis Cellulose Synthesis Complex: Evidence for CESA Trimers.In silico drug exploration and discovery using DrugBank.Computational Prediction of Protein Secondary Structure from Sequence.
P2860
Q24646039-271A1054-AE17-4F7C-B05E-F7B1DFDF8D25Q28540007-030D2BA5-5066-4D7B-850F-4CE24DD58B17Q29547592-B24AAF85-F9C4-4560-B519-EF05E569741BQ30354579-ABD98A8E-37F4-4933-B554-86BACF648245Q30364988-C8E987BD-AE49-41E8-8052-5374E2378818Q30365334-D01A45AC-9F99-4C52-8CD4-8D67CDE64172Q30367356-88FF6C9D-2F11-4D34-A3F7-44A7A5FBBB77Q30367814-712A716D-94B0-4A24-83DB-2232E6FEB8EEQ30369218-CC30F8C8-717B-4A92-93D3-EBB2873E857CQ30369682-4545F201-75ED-43E6-A2AF-7DAFB8E5D015Q30371475-2C569D06-006F-4C0B-93F4-98661331A02AQ30372548-7EFF7DAC-C470-4114-8CE2-F4C27267DDABQ30379010-6D22F201-359D-4307-8A83-ABBEF10861A1Q30382761-B53FFF42-122A-4A8C-806E-80F0F3C21F41Q30391325-27C63EC6-FAA9-492B-931D-56EA183FC602Q30392787-2BD58FB7-7F03-47AB-AD7A-8ADE23B185D1Q30396966-4C981747-7D4C-40C4-BFA6-D6E2F2B1C9A1Q30401008-650F38E7-C56B-4C4C-8ADC-E9D945CA0F29Q30404702-A245FC41-05A8-4753-880E-12DAD2E679B9Q30420136-F59879F9-D1A3-4A91-99C0-B74AAB61BBFFQ30428443-8E0889EA-B177-4693-A68D-4584D0D1872FQ30444757-109BA9F1-1322-40E7-A6B5-B6461328BEA3Q33287800-5087AE5D-27B3-463D-BDC1-DA859216937DQ33475131-639393CE-CE9C-4BBA-9CDE-7F1EEB928943Q33735410-ED47E32C-72CD-46FC-8FA6-E9E9B19ED67CQ33766762-5BA94338-E013-4381-A426-55CCBFD174CFQ34178185-97835D06-2F5E-444C-AFC8-A0073F6E5A14Q34619858-23A4D6AA-7FD5-49D2-A012-FB6DF52A7494Q35618898-DE1B2220-2575-4F23-9590-142E9B6C0340Q36960833-2A174E8C-AEBC-4D08-9C13-16CD293DE8E9Q37473662-FD9A1668-0BA2-4276-AF25-E65B80D74C74Q37537902-F5A3F3EC-DA8A-40BC-8E39-53A51C7A2788Q37735364-DB94A943-5EC4-4930-9CF0-F4615A28127AQ38195369-116D055D-43D7-40C1-B8C7-25A3A18E32C2Q39448556-2BC5F540-69CC-4EEB-9FA2-F8966745DEBEQ44044696-4ADED83F-AD03-4582-96B1-5F1E2D150B9FQ44051720-2F617408-2327-4788-9AA3-6D2ADCED9E40Q44860763-FA3C9D0B-D3F8-4260-92BA-DAE2CB07E04CQ45023254-CB1140E0-A47F-4D8B-A88A-65D681FA1ABDQ45046272-CD4939CB-3B14-4394-874F-D3742D28FD09
P2860
Improving the accuracy of protein secondary structure prediction using structural alignment
description
2006 nî lūn-bûn
@nan
2006 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2006 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2006年の論文
@ja
2006年論文
@yue
2006年論文
@zh-hant
2006年論文
@zh-hk
2006年論文
@zh-mo
2006年論文
@zh-tw
2006年论文
@wuu
name
Improving the accuracy of prot ...... ion using structural alignment
@ast
Improving the accuracy of prot ...... ion using structural alignment
@en
type
label
Improving the accuracy of prot ...... ion using structural alignment
@ast
Improving the accuracy of prot ...... ion using structural alignment
@en
prefLabel
Improving the accuracy of prot ...... ion using structural alignment
@ast
Improving the accuracy of prot ...... ion using structural alignment
@en
P2093
P2860
P356
P1433
P1476
Improving the accuracy of prot ...... ion using structural alignment
@en
P2093
Scott Montgomerie
Shan Sundararaj
Warren J Gallin
P2860
P2888
P356
10.1186/1471-2105-7-301
P577
2006-06-14T00:00:00Z
P5875
P6179
1020981429