Cargando…

Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences

MOTIVATION: Genome-wide association studies have systematically identified thousands of single nucleotide polymorphisms (SNPs) associated with complex genetic diseases. However, the majority of those SNPs were found in non-coding genomic regions, preventing the understanding of the underlying causal...

Descripción completa

Detalles Bibliográficos
Autor principal:	Mourad, Raphaël
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2023
Materias:	Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163727/ https://www.ncbi.nlm.nih.gov/pubmed/37147561 http://dx.doi.org/10.1186/s12859-023-05303-2

Ejemplares similares

Semi-supervised prediction of protein interaction sites from unlabeled sample information
por: Wang, Ye, et al.
Publicado: (2019)

Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data
por: Ren, Zilin, et al.
Publicado: (2023)

Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences
por: Fries, Jason A., et al.
Publicado: (2019)

Correction: Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data
por: Ren, Zilin, et al.
Publicado: (2023)

A Semi-Supervised Approach to Bearing Fault Diagnosis under Variable Conditions towards Imbalanced Unlabeled Data
por: Chen, Xinan, et al.
Publicado: (2018)

Positive-Unlabeled Learning for Pupylation Sites Prediction
por: Jiang, Ming, et al.
Publicado: (2016)

Learning gene regulatory networks from only positive and unlabeled data
por: Cerulo, Luigi, et al.
Publicado: (2010)

Improving Chemical Reaction Prediction with Unlabeled Data
por: Xie, Yu, et al.
Publicado: (2022)

Semi-supervised learning /
Publicado: (2010)

Positive-unlabeled learning for the prediction of conformational B-cell epitopes
por: Ren, Jing, et al.
Publicado: (2015)

Genome-wide sequence-based prediction of peripheral proteins using a novel semi-supervised learning technique
por: Bhardwaj, Nitin, et al.
Publicado: (2010)

Supervised, semi-supervised and unsupervised inference of gene regulatory networks
por: Maetschke, Stefan R., et al.
Publicado: (2014)

Increasing the accuracy of single sequence prediction methods using a deep semi-supervised learning framework
por: Moffat, Lewis, et al.
Publicado: (2021)

Amino acid selective unlabeling for sequence specific resonance assignments in proteins
por: Krishnarjuna, B., et al.
Publicado: (2010)

PLUS: Predicting cancer metastasis potential based on positive and unlabeled learning
por: Zhou, Junyi, et al.
Publicado: (2022)

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

Positive-unlabeled learning for disease gene identification
por: Yang, Peng, et al.
Publicado: (2012)

Predicting HIV-1 Protease Cleavage Sites With Positive-Unlabeled Learning
por: Li, Zhenfeng, et al.
Publicado: (2021)

Efficient use of unlabeled data for protein sequence classification: a comparative study
por: Kuksa, Pavel, et al.
Publicado: (2009)

Ensemble Positive Unlabeled Learning for Disease Gene Identification
por: Yang, Peng, et al.
Publicado: (2014)

Positive-unlabelled learning of glycosylation sites in the human proteome
por: Li, Fuyi, et al.
Publicado: (2019)

Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach
por: Pio, Gianvito, et al.
Publicado: (2014)

Semi-supervised learning for an improved diagnosis of COVID-19 in CT images
por: Han, Chang Hee, et al.
Publicado: (2021)

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)

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

Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks
por: Wang, Juexin, et al.
Publicado: (2020)

Foundations for improved vaccine correlate of risk analysis using positive-unlabeled learning
por: Kelkar, Natasha S., et al.
Publicado: (2023)

Gene function prediction using labeled and unlabeled data
por: Zhao, Xing-Ming, et al.
Publicado: (2008)

EPuL: An Enhanced Positive-Unlabeled Learning Algorithm for the Prediction of Pupylation Sites
por: Nan, Xuanguo, et al.
Publicado: (2017)

Screening drug-target interactions with positive-unlabeled learning
por: Peng, Lihong, et al.
Publicado: (2017)

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

Improved semi-supervised autoencoder for deception detection
por: Fu, Hongliang, et al.
Publicado: (2019)

Improving Colonoscopy Lesion Classification Using Semi-Supervised Deep Learning
por: GOLHAR, MAYANK, et al.
Publicado: (2020)

SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing
por: Pan, Shaojun, et al.
Publicado: (2023)

Active semi-supervised learning for biological data classification
por: Camargo, Guilherme, et al.
Publicado: (2020)

Inheritance metrics feats in unsupervised learning to classify unlabeled datasets and clusters in fault prediction
por: Aziz, Syed Rashid, et al.
Publicado: (2021)

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

Classification of unlabeled online media
por: Prakash, Sakthi Kumar Arul, et al.
Publicado: (2021)

Comparing supervised and semi-supervised machine learning approaches in NTCP modeling to predict complications in head and neck cancer patients
por: Spiero, I., et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_og7m85q36jgbp95mjp39hu7d9o