Pippin: A random forest-based method for identifying presynaptic and postsynaptic neurotoxins
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Pippin: A random forest-based method for identifying presynaptic and postsynaptic neurotoxins
Authors
Keywords
-
Journal
Journal of Bioinformatics and Computational Biology
Volume 18, Issue 02, Pages 2050008
Publisher
World Scientific Pub Co Pte Lt
Online
2020-05-06
DOI
10.1142/s0219720020500080
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters
- (2019) Meng Zhang et al. BIOINFORMATICS
- Positive-unlabelled learning of glycosylation sites in the human proteome
- (2019) Fuyi Li et al. BMC BIOINFORMATICS
- DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites
- (2019) Fuyi Li et al. BIOINFORMATICS
- Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome
- (2018) Fuyi Li et al. BIOINFORMATICS
- iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences
- (2018) Zhen Chen et al. BIOINFORMATICS
- iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites
- (2018) Jiangning Song et al. BRIEFINGS IN BIOINFORMATICS
- Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou's general pseudo amino acid composition and motif features
- (2018) Juan Mei et al. JOURNAL OF THEORETICAL BIOLOGY
- Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods
- (2018) Fuyi Li et al. BRIEFINGS IN BIOINFORMATICS
- UniProt: a worldwide hub of protein knowledge
- (2018) NUCLEIC ACIDS RESEARCH
- Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components
- (2017) Haiyan Huo et al. Scientific Reports
- GlycoMine struct : a new bioinformatics tool for highly accurate mapping of the human N-linked and O-linked glycoproteomes by incorporating structural features
- (2016) Fuyi Li et al. Scientific Reports
- GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome
- (2015) Fuyi Li et al. BIOINFORMATICS
- Harnessing Computational Biology for Exact Linear B-Cell Epitope Prediction: A Novel Amino Acid Composition-Based Feature Descriptor
- (2015) Vijayakumar Saravanan et al. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY
- CD-HIT Suite: a web server for clustering and comparing biological sequences
- (2010) Ying Huang et al. BIOINFORMATICS
- Some remarks on protein attribute prediction and pseudo amino acid composition
- (2010) Kuo-Chen Chou JOURNAL OF THEORETICAL BIOLOGY
- MEME SUITE: tools for motif discovery and searching
- (2009) T. L. Bailey et al. NUCLEIC ACIDS RESEARCH
- Prediction of integral membrane protein type by collocated hydrophobic amino acid pairs
- (2008) Ke Chen et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search