Cargando…

IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning

BACKGROUND: Viral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions must be discovered. However, current computational...

Descripción completa

Detalles Bibliográficos
Autores principales:	Yan, Cheng, Duan, Guihua, Wu, Fang-Xiang, Wang, Jianxin
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2019
Materias:	Methodology
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6933616/ https://www.ncbi.nlm.nih.gov/pubmed/31881820 http://dx.doi.org/10.1186/s12859-019-3278-3

Ejemplares similares

Predicting the hosts of prokaryotic viruses using GCN-based semi-supervised learning
por: Shang, Jiayu, et al.
Publicado: (2021)

Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network
por: Chen, Jiarui, et al.
Publicado: (2021)

Robust identification of molecular phenotypes using semi-supervised learning
por: Roder, Heinrich, et al.
Publicado: (2019)

PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences
por: Yan, Cheng, et al.
Publicado: (2020)

Semi-supervised consensus clustering for gene expression data analysis
por: Wang, Yunli, et al.
Publicado: (2014)

Semi-supervised discovery of differential genes
por: Oba, Shigeyuki, et al.
Publicado: (2006)

MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning
por: Lin, Shenggeng, et al.
Publicado: (2022)

A semi–supervised tensor regression model for siRNA efficacy prediction
por: Thang, Bui Ngoc, et al.
Publicado: (2015)

Drug-target interaction prediction using semi-bipartite graph model and deep learning
por: Eslami Manoochehri, Hafez, et al.
Publicado: (2020)

Semi-supervised machine learning for automated species identification by collagen peptide mass fingerprinting
por: Gu, Muxin, et al.
Publicado: (2018)

Finding combinatorial histone code by semi-supervised biclustering
por: Teng, Li, et al.
Publicado: (2012)

Semi-supervised COVID-19 CT image segmentation using deep generative models
por: Zammit, Judah, et al.
Publicado: (2022)

Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes
por: Pourreza Shahri, Morteza, et al.
Publicado: (2021)

In-silico predictive mutagenicity model generation using supervised learning approaches
por: Seal, Abhik, et al.
Publicado: (2012)

Graph-Based Self-Training for Semi-Supervised Deep Similarity Learning
por: Wang, Yifan, et al.
Publicado: (2023)

Semi-supervised learning /
Publicado: (2010)

A novel semi-supervised algorithm for the taxonomic assignment of metagenomic reads
por: Le, Vinh Van, et al.
Publicado: (2016)

Generative Adversarial Training for Supervised and Semi-supervised Learning
por: Wang, Xianmin, et al.
Publicado: (2022)

DDIGIP: predicting drug-drug interactions based on Gaussian interaction profile kernels
por: Yan, Cheng, et al.
Publicado: (2019)

Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning
por: Zhang, Xun, et al.
Publicado: (2021)

Study on the semi-supervised learning-based patient similarity from heterogeneous electronic medical records
por: Wang, Ni, et al.
Publicado: (2021)

Active learning with point supervision for cost-effective panicle detection in cereal crops
por: Chandra, Akshay L., et al.
Publicado: (2020)

Self-supervised Learning for Semi-supervised Time Series Classification
por: Jawed, Shayan, et al.
Publicado: (2020)

Semi-supervised learning in cancer diagnostics
por: Eckardt, Jan-Niklas, et al.
Publicado: (2022)

Genome-wide prediction of cis-regulatory regions using supervised deep learning methods
por: Li, Yifeng, et al.
Publicado: (2018)

Prediction of enzyme function by combining sequence similarity and protein interactions
por: Espadaler, Jordi, et al.
Publicado: (2008)

Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins
por: Qi, Yanjun, et al.
Publicado: (2010)

Protein complex detection with semi-supervised learning in protein interaction networks
por: Shi, Lei, et al.
Publicado: (2011)

Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning
por: Zhao, Nan, et al.
Publicado: (2014)

Combining active learning and semi-supervised learning techniques to extract protein interaction sentences
por: Song, Min, et al.
Publicado: (2011)

Iteration method for predicting essential proteins based on orthology and protein-protein interaction networks
por: Peng, Wei, et al.
Publicado: (2012)

NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning
por: Chen, Xing, et al.
Publicado: (2016)

Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences
por: Mourad, Raphaël
Publicado: (2023)

MOST: most-similar ligand based approach to target prediction
por: Huang, Tao, et al.
Publicado: (2017)

Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning
por: Zhang, Yao, et al.
Publicado: (2019)

Comparing supervised and semi-supervised Machine Learning Models on Diagnosing Breast Cancer
por: Al-Azzam, Nosayba, et al.
Publicado: (2021)

Identifying cell types from single-cell data based on similarities and dissimilarities between cells
por: Li, Yuanyuan, et al.
Publicado: (2021)

Research Screener: a machine learning tool to semi-automate abstract screening for systematic reviews
por: Chai, Kevin E. K., et al.
Publicado: (2021)

Semi-supervised oblique predictive clustering trees
por: Stepišnik, Tomaž, et al.
Publicado: (2021)

An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images
por: Sun, Fei, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_hckntj0g0cj7ufimi5aue7f7vn