Identifying multi-functional bioactive peptide functions using multi-label deep learning
出版年份 2021 全文链接
标题
Identifying multi-functional bioactive peptide functions using multi-label deep learning
作者
关键词
-
出版物
BRIEFINGS IN BIOINFORMATICS
Volume 23, Issue 1, Pages -
出版商
Oxford University Press (OUP)
发表日期
2021-09-14
DOI
10.1093/bib/bbab414
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。- Application of multi-label classification models for the diagnosis of diabetic complications
- (2021) Liang Zhou et al. BMC Medical Informatics and Decision Making
- PPTPP: A novel therapeutic peptide prediction method using physicochemical property encoding and adaptive feature representation learning
- (2020) Yu P Zhang et al. BIOINFORMATICS
- iATC-FRAKEL: a simple multi-label web server for recognizing anatomical therapeutic chemical classes of drugs with their fingerprints only
- (2020) Jian-Peng Zhou et al. BIOINFORMATICS
- AntiCP 2.0: an updated model for predicting anticancer peptides
- (2020) Piyush Agrawal et al. BRIEFINGS IN BIOINFORMATICS
- Anti-cancer peptides: classification, mechanism of action, reconstruction and modification
- (2020) Mingfeng Xie et al. Open Biology
- mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning
- (2019) Zhenzhen Zou et al. Frontiers in Genetics
- PreAIP: Computational Prediction of Anti-inflammatory Peptides by Integrating Multiple Complementary Features
- (2019) Mst. Shamima Khatun et al. Frontiers in Genetics
- PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning
- (2019) Leyi Wei et al. BIOINFORMATICS
- AMAP: Hierarchical multi-label prediction of biologically active and antimicrobial peptides
- (2019) Sadaf Gull et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Joint Ranking SVM and Binary Relevance with robust Low-rank learning for multi-label classification
- (2019) Guoqiang Wu et al. NEURAL NETWORKS
- DeepSimulator: a deep simulator for Nanopore sequencing
- (2018) Yu 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
- OUP accepted manuscript
- (2018) NUCLEIC ACIDS RESEARCH
- Cost-sensitive multi-label learning with positive and negative label pairwise correlations
- (2018) Guoqiang Wu et al. NEURAL NETWORKS
- mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation
- (2018) Balachandran Manavalan et al. BIOINFORMATICS
- Prediction of anti-inflammatory proteins/peptides: an insilico approach
- (2017) Sudheer Gupta et al. Journal of Translational Medicine
- iPTM-mLys: identifying multiple lysine PTM sites and their different types
- (2016) Wang-Ren Qiu et al. BIOINFORMATICS
- Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types
- (2016) Weizhong Lin et al. BIOINFORMATICS
- Lift: Multi-Label Learning with Label-Specific Features
- (2015) Min-Ling Zhang et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- CancerPPD: a database of anticancer peptides and proteins
- (2014) Atul Tyagi et al. NUCLEIC ACIDS RESEARCH
- Potential therapeutic applications of multifunctional host-defense peptides from frog skin as anti-cancer, anti-viral, immunomodulatory, and anti-diabetic agents
- (2014) J. Michael Conlon et al. PEPTIDES
- iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types
- (2013) Xuan Xiao et al. ANALYTICAL BIOCHEMISTRY
- A Review on Multi-Label Learning Algorithms
- (2013) Min-Ling Zhang et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- From antimicrobial to anticancer peptides. A review
- (2013) Diana Gaspar et al. Frontiers in Microbiology
- CD-HIT: accelerated for clustering the next-generation sequencing data
- (2012) Limin Fu et al. BIOINFORMATICS
- Classifier chains for multi-label classification
- (2011) Jesse Read et al. MACHINE LEARNING
- Multilabel classification via calibrated label ranking
- (2008) Johannes Fürnkranz et al. MACHINE LEARNING
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