Prediction of protein subcellular locations by GO-FunD-PseAA predictor.
about
Protein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multiple support vector machines.pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties.Prediction of antimicrobial peptides based on sequence alignment and feature selection methodsAn ensemble method with hybrid features to identify extracellular matrix proteinsmGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machinesPrediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence.Efficacy of different protein descriptors in predicting protein functional families.ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins.MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction.Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition.CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation.Gene ontology based transfer learning for protein subcellular localizationExploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone.Multi-label multi-kernel transfer learning for human protein subcellular localization.Classification of lung cancer tumors based on structural and physicochemical properties of proteins by bioinformatics models.An ensemble method for predicting subnuclear localizations from primary protein structures.Comparison study on statistical features of predicted secondary structures for protein structural class prediction: From content to position.PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence.Comparative proteomics analysis of proteins expressed in the I-1 and I-2 internodes of strawberry stolonsSparse regressions for predicting and interpreting subcellular localization of multi-label proteinsMethodology development for predicting subcellular localization and other attributes of proteins.Prediction of functional class of proteins and peptides irrespective of sequence homology by support vector machines.PseAAC-General: fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets.Subcellular fractionation methods and strategies for proteomics.Recent progress in predicting protein sub-subcellular locations.Predicting multisite protein subcellular locations: progress and challenges.Predicting protein submitochondria locations by combining different descriptors into the general form of Chou's pseudo amino acid composition.Gpos-PLoc: an ensemble classifier for predicting subcellular localization of Gram-positive bacterial proteins.Screening and identification of resistance related proteins from apple leaves inoculated with Marssonina coronaria (EII. & J. J. Davis).Protein Remote Homology Detection by Combining Chou's Pseudo Amino Acid Composition and Profile-Based Protein Representation.The effect of three novel feature extraction methods on the prediction of the subcellular localization of multi-site virus proteins.iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals.Prediction of drugs target groups based on ChEBI ontology.Prediction of lung tumor types based on protein attributes by machine learning algorithms.Protein subcellular localization based on PSI-BLAST and machine learning.A novel method for apoptosis protein subcellular localization prediction combining encoding based on grouped weight and support vector machine.Prediction of protein subcellular location using a combined feature of sequence.acACS: improving the prediction accuracy of protein subcellular locations and protein classification by incorporating the average chemical shifts composition.Discrimination of Golgi Type II Membrane Proteins Based on Their Hydropathy Profiles and the Amino Acid Propensities of Their Transmembrane Regions
P2860
Q24815173-B96672D0-F28E-4674-8184-7EE9300C0037Q24816257-C85E8D2C-170C-4429-9EED-B507DCCE4203Q28477742-F71CF62D-95C0-4083-82EE-013E5E989F7AQ28543516-0A4EF5A5-C05E-457E-9836-0F3F495BFF99Q31106500-AE246309-31AA-442C-9B6E-220B554AB507Q33264972-D52FAF6C-9A09-4A12-A6ED-DFEAC4B5B5D1Q33294520-F2238B95-893F-4631-9E4D-1E9DF2917CA1Q33387728-DBA020D1-1B96-40B7-A061-522F71ED702EQ33499113-881CEAD6-ECBE-4F9E-B552-6759823AF8FCQ33590986-9A285907-D2D5-46F3-84A9-09D9866F4358Q33728064-DE6D6E95-737E-4078-B6A5-26F1023E6031Q33810069-18304558-4A20-445B-97A1-604C6EACF7DDQ33922849-C1E6A826-4653-486F-B3D0-64D30BE0E9CEQ34311759-CD21E295-D63C-4F32-ADA5-9CCEA4CEAD34Q34351008-55F829A1-4782-4098-9880-5A8C1D1D6B41Q34608457-9EC6D1F5-3C52-4D20-951F-611700E58F41Q34704336-613A38A6-E036-4B5B-9910-F642DC9C2A7CQ34974203-CF616B35-F3AA-4EB6-84BC-3F4647ABB220Q35042061-D069EB06-57AA-4FCE-A35C-D8127BB7A4DCQ35934593-C4B31D86-FF5E-4FE4-A4CC-0EFF109C4095Q36913033-523C03AA-6CF7-4550-BE27-57EEACC48703Q37462980-2143C068-66F4-4B87-995E-0F35D4C775DBQ37683961-CB57DC89-91EE-4567-8341-0854678B87DEQ37809824-316E08E3-73B5-4A30-BAA4-7D6668E3E7C6Q37890426-52758B6E-373E-482B-9FF4-E3100A765F03Q38115328-5347AABE-585C-42FA-BD78-D5A4BAD307D9Q38499799-121D7DF3-1BEF-4446-935C-4551D83A6835Q38516896-34CF497D-9E4A-4E19-B216-E4CB72855477Q39435339-26EC3AD8-E448-4019-8743-5FF55FED94ACQ39537162-70C0201D-8489-42F0-8E31-34F42E81466BQ40050728-F1FFB9DF-A5E0-4B96-8D0C-929AEF1CCBAAQ41709408-FC69B5C1-DEE0-493C-A8D4-492EDADF93E5Q41835987-96FCF9AA-EC4B-44F8-96BB-645A4553830DQ43093168-01C640D3-A083-4766-8E98-C5D2EE4E537BQ45965564-7457A526-4862-4252-B3A8-C96704323BCAQ47315733-9699AC24-3054-4FA9-93F5-A3144F1DF5FCQ51971652-5CDE4FCB-023D-4706-96F9-F497DE9DD477Q55715951-59F2A384-CC14-48E0-B75B-610CC07C8058Q57810722-2E85E525-D9C9-4DEA-B788-508BBF5CDB4E
P2860
Prediction of protein subcellular locations by GO-FunD-PseAA predictor.
description
2004 nî lūn-bûn
@nan
2004年の論文
@ja
2004年論文
@yue
2004年論文
@zh-hant
2004年論文
@zh-hk
2004年論文
@zh-mo
2004年論文
@zh-tw
2004年论文
@wuu
2004年论文
@zh
2004年论文
@zh-cn
name
Prediction of protein subcellular locations by GO-FunD-PseAA predictor.
@en
type
label
Prediction of protein subcellular locations by GO-FunD-PseAA predictor.
@en
prefLabel
Prediction of protein subcellular locations by GO-FunD-PseAA predictor.
@en
P1476
Prediction of protein subcellular locations by GO-FunD-PseAA predictor.
@en
P2093
Yu-Dong Cai
P304
P356
10.1016/J.BBRC.2004.06.073
P407
P577
2004-08-01T00:00:00Z