Cargando…

Editorial: Machine Learning-Based Methods for RNA Data Analysis

Detalles Bibliográficos
Autores principales:	Peng, Lihong, Yang, Jialiang, Wang, Minxian, Zhou, Liqian
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2022
Materias:	Genetics
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9175173/ https://www.ncbi.nlm.nih.gov/pubmed/35692815 http://dx.doi.org/10.3389/fgene.2022.828575

Ejemplares similares

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

Editorial: Computational Methods in Predicting Complex Disease Associated Genes and Environmental Factors
por: Cai, Yudong, et al.
Publicado: (2021)

Probing lncRNA–Protein Interactions: Data Repositories, Models, and Algorithms
por: Peng, Lihong, et al.
Publicado: (2020)

Predicting Cancer Tissue-of-Origin by a Machine Learning Method Using DNA Somatic Mutation Data
por: Liu, Xiaojun, et al.
Publicado: (2020)

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

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

Identifying Effective Antiviral Drugs Against SARS-CoV-2 by Drug Repositioning Through Virus-Drug Association Prediction
por: Peng, Lihong, et al.
Publicado: (2020)

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

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

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

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

Screening Potential Drugs for COVID-19 Based on Bound Nuclear Norm Regularization
por: Wang, Juanjuan, 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)

Editorial: Bioinformatics Analysis of Single Cell Sequencing Data and Applications in Precision Medicine
por: Yang, Jialiang, et al.
Publicado: (2020)

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)

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

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

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

Editorial: Computational methods to analyze RNA data for human diseases
por: Ding, Pingjian, et al.
Publicado: (2023)

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

Editorial: Machine Learning in Genome-Wide Association Studies
por: Hu, Ting, et al.
Publicado: (2020)

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

Editorial: Data Mining and Statistical Methods for Knowledge Discovery in Diseases Based on Multimodal Omics
por: Wang, Tao, et al.
Publicado: (2022)

Editorial: Computational Methods in Inferring Cancer Tissue-of-Origin and Cancer Molecular Classification
por: Kui, Ling, et al.
Publicado: (2021)

Editorial: RNA-Seq Analysis: Methods, Applications and Challenges
por: Geraci, Filippo, et al.
Publicado: (2020)

BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network
por: Xiao, Xiaofang, et al.
Publicado: (2018)

An Integrated Analysis of Tumor Purity of Common Central Nervous System Tumors in Children Based on Machine Learning Methods
por: Yang, Jian, et al.
Publicado: (2021)

Editorial: Finding New Epigenomics and Epigenetics Biomarkers for Complex Diseases and Significant Developmental Events With Machine Learning Methods
por: Xie, Lu, et al.
Publicado: (2022)

Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods
por: Li, ZhanDong, et al.
Publicado: (2022)

Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures
por: Fu, Xiangzheng, et al.
Publicado: (2019)

Editorial: Graph Embedding Methods for Multiple-Omics Data Analysis
por: Lan, Wei, et al.
Publicado: (2021)

Editorial: Data mining and statistical methods for knowledge discovery in diseases based on multimodal omics, volume II
por: Wang, Tao, et al.
Publicado: (2023)

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

Benchmark of Data Processing Methods and Machine Learning Models for Gut Microbiome-Based Diagnosis of Inflammatory Bowel Disease
por: Kubinski, Ryszard, et al.
Publicado: (2022)

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

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: Finding new epigenomics and epigenetics biomarkers for complex diseases and significant developmental events with machine learning methods, Volume II
por: Cai, Yudong, et al.
Publicado: (2022)

Editorial: Methods for Single-Cell and Microbiome Sequencing Data
por: Mallick, Himel, et al.
Publicado: (2022)

Editorial: Biomedical Data Visualization: Methods and Applications
por: Wu, Tianzhi, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_168ssnq0360q05m3o0cblvdu4f