A Multi-Label Classifier for Predicting the Subcellular Localization of Gram-Negative Bacterial Proteins with Both Single and Multiple Sites
出版年份 2011 全文链接
标题
A Multi-Label Classifier for Predicting the Subcellular Localization of Gram-Negative Bacterial Proteins with Both Single and Multiple Sites
作者
关键词
-
出版物
PLoS One
Volume 6, Issue 6, Pages e20592
出版商
Public Library of Science (PLoS)
发表日期
2011-06-18
DOI
10.1371/journal.pone.0020592
参考文献
相关参考文献
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 feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction
- (2010) Sitanshu Sekhar Sahu et al. COMPUTATIONAL BIOLOGY AND CHEMISTRY
- Supersecondary structure prediction using Chou's pseudo amino acid composition
- (2010) Dongsheng Zou et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Knowledge-based computational mutagenesis for predicting the disease potential of human non-synonymous single nucleotide polymorphisms
- (2010) Majid Masso et al. JOURNAL OF THEORETICAL BIOLOGY
- Gneg-mPLoc: A top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins
- (2010) Hong-Bin Shen et al. JOURNAL OF THEORETICAL BIOLOGY
- Some remarks on protein attribute prediction and pseudo amino acid composition
- (2010) Kuo-Chen Chou JOURNAL OF THEORETICAL BIOLOGY
- Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization
- (2010) Kuo-Chen Chou et al. PLoS One
- Prediction of Protein Subcellular Locations with Feature Selection and Analysis
- (2010) Yudong Cai et al. PROTEIN AND PEPTIDE LETTERS
- Prediction of Cyclin Proteins Using Chous Pseudo Amino Acid Composition
- (2010) Hassan Mohabatkar PROTEIN AND PEPTIDE LETTERS
- Prediction of nuclear receptors with optimal pseudo amino acid composition
- (2009) Qing-Bin Gao et al. ANALYTICAL BIOCHEMISTRY
- SubChlo: Predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm
- (2009) Pufeng Du et al. JOURNAL OF THEORETICAL BIOLOGY
- γ-turn types prediction in proteins using the two-stage hybrid neural discriminant model
- (2009) Samad Jahandideh et al. JOURNAL OF THEORETICAL BIOLOGY
- Exploring the Function-Location Nexus: Using Multiple Lines of Evidence in Defining the Subcellular Location of Plant Proteins
- (2009) A. H. Millar et al. PLANT CELL
- Prediction of Cell Wall Lytic Enzymes Using Chous Amphiphilic Pseudo Amino Acid Composition
- (2009) Hui Ding et al. PROTEIN AND PEPTIDE LETTERS
- Prediction of Protein Secondary Structure Content by Using the Concept of Chous Pseudo Amino Acid Composition and Support Vector Machine
- (2009) Chao Chen et al. PROTEIN AND PEPTIDE LETTERS
- Protein function annotation by homology-based inference
- (2009) Yaniv Loewenstein et al. GENOME BIOLOGY
- Prediction of protein structural class using novel evolutionary collocation-based sequence representation
- (2008) Ke Chen et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Predicting protein structural class based on multi-features fusion
- (2008) Chao Chen et al. JOURNAL OF THEORETICAL BIOLOGY
- The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition
- (2008) Hao Lin JOURNAL OF THEORETICAL BIOLOGY
- Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms
- (2008) Kuo-Chen Chou et al. Nature Protocols
- Function Prediction of Hypothetical Proteins Without Sequence Similarity to Proteins of Known Function (SUPPLEMENTARY MATERIALS)
- (2008) S. Kannan et al. PROTEIN AND PEPTIDE LETTERS
- Predicting Protein Subcellular Location Using Chous Pseudo Amino Acid Composition and Improved Hybrid Approach
- (2008) Feng-Min Li et al. PROTEIN AND PEPTIDE LETTERS
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started