位置：成果数据库 > 期刊 > 期刊详情页
Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripe
  • ISSN号：1742-206X
  • 期刊名称：Molecular Biosystems
  • 时间：2015
  • 页码：558-563
  • 相关项目：基于特征挖掘的离子通道功能类型预测与跨膜域识别
作者： Deng, En-Ze|Ding, Hui|Chen, Wei|Lin, Hao|
同期刊论文项目
基于特征挖掘的离子通道功能类型预测与跨膜域识别
期刊论文 39
同项目期刊论文
Using Over-Represented Tetrapeptides to Predict Protein Submitochondria Locations
Prediction of the types of ion channel-targeted conotoxins based on radial basis function network
Identification of Antioxidants from Sequence Information Using Naive Bayes
Naive Bayes Classifier with Feature Selection to Identify Phage Virion Proteins
Prediction of Golgi-resident protein types by using feature selection technique
Identification of mycobacterial membrane proteins and their types using over-represented tripeptide
Using deformation energy to analyze nucleosome positioning
Training sparse SVM on the core sets of fitting-planes
Predicting the types of J-proteins using clustered amino acids
Prediction of CpG island methylation status by intergrating DNA physiochemical properties
Sequence analysis of origins of replication in the Saccharomyces cerevisiae genomes
Identification of bacteriophage virion proteins with the ANOVA feature selection and analysis.
Identifying the subfamilies of voltage-gated potassium channels using feature selection technique
Prediction of protein structural classes based on feature selection technique
iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseud
iRNA-Methyl: Identifying N-6-methyladenosine sites using pseudo nucleotide composition
iORI-PseKNC: a predictor for identifying origin of replication with pseudo k-tuple nucleotide compos
Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences
Briefing in Application of Machine Learning Methods in Ion Channel Prediction
iCTX-Type: A Sequence-Based Predictor for Identifying the Types of Conotoxins in Targeting Ion Chann
？iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo redued amino acid alphabet
Predicting cancerlectins by the optimal g-gap dipeptides
基于特征筛选方法预测不同离子通道活性的芋螺毒素
细菌σ54启动子序列分析与预测