Using deep neural networks and biological subwords to detect protein S-sulfenylation sites
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Using deep neural networks and biological subwords to detect protein S-sulfenylation sites
Authors
Keywords
-
Journal
BRIEFINGS IN BIOINFORMATICS
Volume -, Issue -, Pages -
Publisher
Oxford University Press (OUP)
Online
2020-05-27
DOI
10.1093/bib/bbaa128
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins
- (2019) Hongli Fu et al. BMC BIOINFORMATICS
- A deep learning method to more accurately recall known lysine acetylation sites
- (2019) Meiqi Wu et al. BMC BIOINFORMATICS
- iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding
- (2019) Nguyen Quoc Khanh Le et al. ANALYTICAL BIOCHEMISTRY
- iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou’s 5-step rule
- (2019) Nguyen Quoc Khanh Le MOLECULAR GENETICS AND GENOMICS
- Using word embedding technique to efficiently represent protein sequences for identifying substrate specificities of transporters
- (2019) Trinh-Trung-Duong Nguyen et al. ANALYTICAL BIOCHEMISTRY
- Fertility-GRU: Identifying fertility-related proteins by incorporating deep gated recurrent units and original PSSM profiles
- (2019) Nguyen Quoc Khanh Le JOURNAL OF PROTEOME RESEARCH
- Classifying Promoters by Interpreting the Hidden Information of DNA Sequences via Deep Learning and Combination of Continuous FastText N-Grams
- (2019) Nguyen Quoc Khanh Le et al. Frontiers in Bioengineering and Biotechnology
- Deep learning for mining protein data
- (2019) Qiang Shi et al. BRIEFINGS IN BIOINFORMATICS
- iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences
- (2018) Zhen Chen et al. BIOINFORMATICS
- Natural language processing in text mining for structural modeling of protein complexes
- (2018) Varsha D. Badal et al. BMC BIOINFORMATICS
- PrESOgenesis: A two-layer multi-label predictor for identifying fertility-related proteins using support vector machine and pseudo amino acid composition approach
- (2018) Mohammad Reza Bakhtiarizadeh et al. Scientific Reports
- Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm
- (2018) Zhe Ju et al. JOURNAL OF THEORETICAL BIOLOGY
- SVM-SulfoSite: A support vector machine based predictor for sulfenylation sites
- (2018) Hussam J. AL-barakati et al. Scientific Reports
- DeepNitro: Prediction of Protein Nitration and Nitrosylation Sites by Deep Learning
- (2018) Yubin Xie et al. GENOMICS PROTEOMICS & BIOINFORMATICS
- DeepPhos: prediction of protein phosphorylation sites with deep learning
- (2018) Fenglin Luo et al. BIOINFORMATICS
- Direct cysteine sulfenylation drives activation of the Src kinase
- (2018) David E. Heppner et al. Nature Communications
- S-SulfPred: A sensitive predictor to capture S-sulfenylation sites based on a resampling one-sided selection undersampling-synthetic minority oversampling technique
- (2017) Cangzhi Jia et al. JOURNAL OF THEORETICAL BIOLOGY
- Predicting S-sulfenylation Sites Using Physicochemical Properties Differences
- (2017) Guo-Cheng Lei et al. LETTERS IN ORGANIC CHEMISTRY
- Computational identification of protein S-sulfenylation sites by incorporating the multiple sequence features information
- (2017) Md. Mehedi Hasan et al. Molecular BioSystems
- PRESS: PRotEin S-Sulfenylation server
- (2016) Marianna Sakka et al. BIOINFORMATICS
- SOHSite: incorporating evolutionary information and physicochemical properties to identify protein S-sulfenylation sites
- (2016) Van-Minh Bui et al. BMC GENOMICS
- SOHPRED: a new bioinformatics tool for the characterization and prediction of human S-sulfenylation sites
- (2016) Xiaofeng Wang et al. Molecular BioSystems
- RF-Hydroxysite: a random forest based predictor for hydroxylation sites
- (2016) Hamid D. Ismail et al. Molecular BioSystems
- RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest
- (2016) Hamid D. Ismail et al. Biomed Research International
- iSulf-Cys: Prediction of S-sulfenylation Sites in Proteins with Physicochemical Properties of Amino Acids
- (2016) Yan Xu et al. PLoS One
- Global, in situ, site-specific analysis of protein S-sulfenylation
- (2015) Jing Yang et al. Nature Protocols
- Site-specific mapping and quantification of protein S-sulphenylation in cells
- (2014) Jing Yang et al. Nature Communications
- Sulfenic acid chemistry, detection and cellular lifetime
- (2013) Vinayak Gupta et al. BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS
- Cysteine Oxidative Posttranslational Modifications
- (2013) Heaseung S. Chung et al. CIRCULATION RESEARCH
- Redox regulation of SIRT1 in inflammation and cellular senescence
- (2013) Jae-woong Hwang et al. FREE RADICAL BIOLOGY AND MEDICINE
- Regulation of A20 and other OTU deubiquitinases by reversible oxidation
- (2013) Yogesh Kulathu et al. Nature Communications
- Deubiquitinases as a Signaling Target of Oxidative Stress
- (2012) Xiomaris M. Cotto-Rios et al. Cell Reports
- Protein sulfenic acid formation: From cellular damage to redox regulation
- (2011) Goedele Roos et al. FREE RADICAL BIOLOGY AND MEDICINE
- Peroxide-dependent sulfenylation of the EGFR catalytic site enhances kinase activity
- (2011) Candice E Paulsen et al. Nature Chemical Biology
- Thiol-Based Redox Switches and Gene Regulation
- (2010) Haike Antelmann et al. ANTIOXIDANTS & REDOX SIGNALING
- Inactivation of Peroxiredoxin I by Phosphorylation Allows Localized H2O2 Accumulation for Cell Signaling
- (2010) Hyun Ae Woo et al. CELL
- Discovering mechanisms of signaling-mediated cysteine oxidation
- (2008) Leslie B Poole et al. CURRENT OPINION IN CHEMICAL BIOLOGY
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started