Cargando…

Editorial: Machine Learning in Genome-Wide Association Studies

Detalles Bibliográficos
Autores principales:	Hu, Ting, Darabos, Christian, Urbanowicz, Ryan
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2020
Materias:	Genetics
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662671/ https://www.ncbi.nlm.nih.gov/pubmed/33193740 http://dx.doi.org/10.3389/fgene.2020.593958

Ejemplares similares

Editorial: Machine Learning and Network-Driven Integrative Genomics
por: Pirooznia, Mehdi, et al.
Publicado: (2021)

Editorial: Big Data and Machine Learning in Cancer Genomics
por: Chen, Lin, et al.
Publicado: (2021)

Editorial: Statistical methods for genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) and their applications
por: Shao, Mengting, et al.
Publicado: (2023)

Editorial: Statistical Methods, Computing and Resources for Genome-Wide Association Studies
por: Liu, Hailan, et al.
Publicado: (2021)

Editorial: Machine Learning for Big Data Analysis: Applications in Plant Breeding and Genomics
por: Esposito, Salvatore, et al.
Publicado: (2022)

Editorial: Multi-Layered Genome-Wide Association/Prediction in Animals
por: Xiang, Ruidong, et al.
Publicado: (2022)

Editorial: Genome Wide Association Studies and Genomic Selection for Crop Improvement in the Era of Big Data
por: Bentley, Alison R., et al.
Publicado: (2022)

A genome-wide association study coupled with machine learning approaches to identify influential demographic and genomic factors underlying Parkinson’s disease
por: Rahman, Md Asad, et al.
Publicado: (2023)

Editorial: Integrative Analysis of Genome-Wide Association Studies and Single-Cell Sequencing Studies
por: Sun, Shiquan, et al.
Publicado: (2021)

Editorial: Machine learning for biological sequence analysis
por: Lv, Zhibin, et al.
Publicado: (2023)

Editorial: Statistical methods, computing, and resources for genome-wide association studies, Volume II
por: Han, Lide, et al.
Publicado: (2022)

Editorial: Genome-wide association studies of COVID-19 among diverse human populations
por: Cheng, Zhong-Shan
Publicado: (2022)

Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene–Gene and Gene–Diet Interactions
por: Lee, Yu-Chi, et al.
Publicado: (2022)

Editorial: Machine Learning Techniques on Gene Function Prediction
por: Zou, Quan, et al.
Publicado: (2019)

Editorial: Machine learning for peptide structure, function, and design
por: Ge, Ruiquan, et al.
Publicado: (2022)

Editorial: Machine Learning-Based Methods for RNA Data Analysis
por: Peng, Lihong, et al.
Publicado: (2022)

Editorial: Machine Learning and Mathematical Models for Single-Cell Data Analysis
por: Ou-Yang, Le, et al.
Publicado: (2022)

Editorial: Machine Learning Techniques on Gene Function Prediction Volume II
por: Qi, Ren, et al.
Publicado: (2022)

Editorial: RNA machines
por: Shirokikh, Nikolay E., et al.
Publicado: (2023)

Evaluation of the lasso and the elastic net in genome-wide association studies
por: Waldmann, Patrik, et al.
Publicado: (2013)

Editorial: Machine Learning Advanced Dynamic Omics Data Analysis for Precision Medicine
por: Zeng, Tao, et al.
Publicado: (2020)

Genome-Wide Association Study of Piglet Uniformity and Farrowing Interval
por: Wang, Yuan, et al.
Publicado: (2017)

Editorial: Machine Learning Used in Biomedical Computing and Intelligence Healthcare, Volume I
por: Gao, Honghao, et al.
Publicado: (2021)

Editorial: Machine Learning Used in Biomedical Computing and Intelligence Healthcare, Volume II
por: Gao, Honghao, et al.
Publicado: (2022)

Editorial: Machine learning-based methods for RNA data analysis—Volume II
por: Peng, Lihong, et al.
Publicado: (2022)

Editorial: Machine learning methods in single-cell immune and drug response prediction
por: Qi, Ren, et al.
Publicado: (2023)

Corrigendum: Evaluation of the lasso and the elastic net in genome-wide association studies
por: Waldmann, Patrik, et al.
Publicado: (2014)

Editorial: Retrieving meaningful patterns from big biomedical data with machine learning approaches
por: Zheng, Yun, et al.
Publicado: (2023)

Genome-Wide Association Study of Kernel Traits in Aegilops tauschii
por: Wang, Qing, et al.
Publicado: (2021)

Deep learning methods may not outperform other machine learning methods on analyzing genomic studies
por: Dong, Yao, et al.
Publicado: (2022)

Editorial: Genomic Colocalization and Enrichment Analyses
por: Kanduri, Chakravarthi, et al.
Publicado: (2021)

Genome-Wide Association Study Identifies Genomic Loci Associated With Neurotransmitter Concentration in Cattle
por: Chen, Qiuming, et al.
Publicado: (2020)

Editorial: Unsupervised Learning Models for Unlabeled Genomic, Transcriptomic & Proteomic Data
por: Xi, Jianing, et al.
Publicado: (2021)

Editorial: Advanced Interpretable Machine Learning Methods for Clinical NGS Big Data of Complex Hereditary Diseases
por: Cai, Yudong, et al.
Publicado: (2020)

Double genomic control is not effective to correct for population stratification in meta-analysis for genome-wide association studies
por: Wang, Shudong, et al.
Publicado: (2012)

Genome-Wide Association Studies and Genomic Selection in Pearl Millet: Advances and Prospects
por: Srivastava, Rakesh K., et al.
Publicado: (2020)

Genome-wide association study for carcass weight in pasture-finished beef cattle in Hawai’i
por: Adhikari, Mandeep, et al.
Publicado: (2023)

Robust Reference Powered Association Test of Genome-Wide Association Studies
por: Wang, Yi, et al.
Publicado: (2019)

The Effect of Population Structure on Murine Genome-Wide Association Studies
por: Wang, Meiyue, et al.
Publicado: (2021)

Editorial: Application of Novel Statistical and Machine-Learning Methods to High-Dimensional Clinical Cancer and (Multi-)Omics Data
por: Xu, Chao, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_5r8lblpsrq6c7olgo8svv3cvto