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。repRNA: a web server for generating various feature vectors of RNA sequences
Bin Liu et al.
MOLECULAR GENETICS AND GENOMICS (2016)
Protein Folds Prediction with Hierarchical Structured SVM
Dapeng Li et al.
CURRENT PROTEOMICS (2016)
Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection technique
Hua Tang et al.
MOLECULAR BIOSYSTEMS (2016)
Recombination spot identification Based on gapped k-mers
Rong Wang et al.
SCIENTIFIC REPORTS (2016)
iMiRNA-SSF: Improving the Identification of MicroRNA Precursors by Combining Negative Sets with Different Distributions
Junjie Chen et al.
SCIENTIFIC REPORTS (2016)
Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest
Zhijun Liao et al.
SCIENTIFICA (2016)
HAlign: Fast multiple similar DNA/RNA sequence alignment based on the centre star strategy
Quan Zou et al.
BIOINFORMATICS (2015)
Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition
Pan-Pan Zhu et al.
MOLECULAR BIOSYSTEMS (2015)
Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences
Bin Liu et al.
NUCLEIC ACIDS RESEARCH (2015)
repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects
Bin Liu et al.
BIOINFORMATICS (2015)
PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions
Wei Chen et al.
BIOINFORMATICS (2015)
PseKNC: A flexible web server for generating pseudo K-tuple nucleotide composition
Wei Chen et al.
ANALYTICAL BIOCHEMISTRY (2014)
SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines
Renzhi Cao et al.
BMC BIOINFORMATICS (2014)
Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment
Renzhi Cao et al.
BMC STRUCTURAL BIOLOGY (2014)
iCTX-Type: A Sequence-Based Predictor for Identifying the Types of Conotoxins in Targeting Ion Channels
Hui Ding et al.
BIOMED RESEARCH INTERNATIONAL (2014)
Position-specific scoring matrix and hidden Markov model complement each other for the prediction of conopeptide superfamilies
Dominique Koua et al.
BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS (2013)
Prediction of the types of ion channel-targeted conotoxins based on radial basis function network
Lu-Feng Yuan et al.
TOXICOLOGY IN VITRO (2013)
Identification and classification of conopeptides using profile Hidden Markov Models
Silja Laht et al.
BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS (2012)
ConoDictor: a tool for prediction of conopeptide superfamilies
Dominique Koua et al.
NUCLEIC ACIDS RESEARCH (2012)
Conotoxin protein classification using free scores of words and support vector machines
Nazar Zaki et al.
BMC BIOINFORMATICS (2011)
Conotoxin Superfamily Prediction Using Diffusion Maps Dimensionality Reduction and Subspace Classifier
Jiang-Bo Yin et al.
CURRENT PROTEIN & PEPTIDE SCIENCE (2011)
PredCSF: An Integrated Feature-Based Approach for Predicting Conotoxin Superfamily
Yong-Xian Fan et al.
PROTEIN AND PEPTIDE LETTERS (2011)
UniProt Knowledgebase: a hub of integrated protein data
Michele Magrane
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION (2011)
Structural Studies of Conotoxins
Norelle L. Daly et al.
IUBMB LIFE (2009)
Bioinformatic characterizations and prediction of K+ and Na+ ion channels effector toxins
Rima Soli et al.
BMC Pharmacology & Toxicology (2009)
Conus venoms -: A rich source of peptide-based therapeutics
Tiffany S. Han et al.
CURRENT PHARMACEUTICAL DESIGN (2008)
Probing peptide libraries from Conus achatinus using mass spectrometry and cDNA sequencing:: identification of δ and ω-conotoxins
Konkallu Hanumae Gowd et al.
JOURNAL OF MASS SPECTROMETRY (2008)
Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant
Hao Lin et al.
BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS (2007)
Pseudo amino acid composition and multi-class support vector machines approach for conotoxin superfamily classification
Sukanta Mondal et al.
JOURNAL OF THEORETICAL BIOLOGY (2006)
Tropical marine neurotoxins: Venoms to drugs
MR Watters
SEMINARS IN NEUROLOGY (2005)
Conus venoms: A rich source of novel ion channel-targeted peptides
H Terlau et al.
PHYSIOLOGICAL REVIEWS (2004)
Prediction of protein cellular attributes using pseudo-amino acid composition
KC Chou
PROTEINS-STRUCTURE FUNCTION AND GENETICS (2001)