标题
RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest
作者
关键词
-
出版物
Biomed Research International
Volume 2016, Issue -, Pages 1-12
出版商
Hindawi Limited
发表日期
2016-03-16
DOI
10.1155/2016/3281590
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- A novel method for predicting post-translational modifications on serine and threonine sites by using site-modification network profiles
- (2015) Minghui Wang et al. Molecular BioSystems
- A Grammar Inference Approach for Predicting Kinase Specific Phosphorylation Sites
- (2015) Sutapa Datta et al. PLoS One
- Predicting Protein-Protein Interactions from Primary Protein Sequences Using a Novel Multi-Scale Local Feature Representation Scheme and the Random Forest
- (2015) Zhu-Hong You et al. PLoS One
- PhosphoSVM: prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine
- (2014) Yongchao Dou et al. AMINO ACIDS
- Prediction of protein kinase-specific phosphorylation sites in hierarchical structure using functional information and random forest
- (2014) Wenwen Fan et al. AMINO ACIDS
- PhosphoPICK: modelling cellular context to map kinase-substrate phosphorylation events
- (2014) Ralph Patrick et al. BIOINFORMATICS
- Improving protein fold recognition by random forest
- (2014) Taeho Jo et al. BMC BIOINFORMATICS
- An ensemble method approach to investigate kinase-specific phosphorylation sites
- (2014) Sutapa Datta et al. International Journal of Nanomedicine
- Toward a systems-level view of dynamic phosphorylation networks
- (2014) Robert H. Newman et al. Frontiers in Genetics
- PhosphoNetworks: a database for human phosphorylation networks
- (2013) J. Hu et al. BIOINFORMATICS
- Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights
- (2013) B. Trost et al. BIOINFORMATICS
- propy: a tool to generate various modes of Chou’s PseAAC
- (2013) Dong-Sheng Cao et al. BIOINFORMATICS
- Construction of human activity-based phosphorylation networks
- (2013) R. H. Newman et al. Molecular Systems Biology
- L1pred: A Sequence-Based Prediction Tool for Catalytic Residues in Enzymes with the L1-logreg Classifier
- (2012) Yongchao Dou et al. PLoS One
- Computational prediction of eukaryotic phosphorylation sites
- (2011) B. Trost et al. BIOINFORMATICS
- Prediction of catalytic residues based on an overlapping amino acid classification
- (2010) Yongchao Dou et al. AMINO ACIDS
- Prediction of protein–RNA binding sites by a random forest method with combined features
- (2010) Zhi-Ping Liu et al. BIOINFORMATICS
- Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information
- (2010) Ashis Biswas et al. BMC BIOINFORMATICS
- A Tissue-Specific Atlas of Mouse Protein Phosphorylation and Expression
- (2010) Edward L. Huttlin et al. CELL
- Some remarks on protein attribute prediction and pseudo amino acid composition
- (2010) Kuo-Chen Chou JOURNAL OF THEORETICAL BIOLOGY
- Musite, a Tool for Global Prediction of General and Kinase-specific Phosphorylation Sites
- (2010) Jianjiong Gao et al. MOLECULAR & CELLULAR PROTEOMICS
- Phospho.ELM: a database of phosphorylation sites--update 2011
- (2010) H. Dinkel et al. NUCLEIC ACIDS RESEARCH
- A reexamination of information theory-based methods for DNA-binding site identification
- (2009) Ivan Erill et al. BMC BIOINFORMATICS
- Phosphopeptide fragmentation and analysis by mass spectrometry
- (2009) Paul J. Boersema et al. JOURNAL OF MASS SPECTROMETRY
- Similarity Analysis of Protein Sequences Based on the Normalized Relative-Entropy
- (2008) Chun Li et al. COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING
- GPS 2.0, a Tool to Predict Kinase-specific Phosphorylation Sites in Hierarchy
- (2008) Yu Xue et al. MOLECULAR & CELLULAR PROTEOMICS
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