Cargando…

Predicting A-to-I RNA Editing by Feature Selection and Random Forest

RNA editing is a post-transcriptional RNA process that provides RNA and protein complexity for regulating gene expression in eukaryotes. It is challenging to predict RNA editing by computational methods. In this study, we developed a novel method to predict RNA editing based on a random forest metho...

Descripción completa

Detalles Bibliográficos
Autores principales:	Shu, Yang, Zhang, Ning, Kong, Xiangyin, Huang, Tao, Cai, Yu-Dong
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Public Library of Science 2014
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4206426/ https://www.ncbi.nlm.nih.gov/pubmed/25338210 http://dx.doi.org/10.1371/journal.pone.0110607

Ejemplares similares

Prediction of Protein Cleavage Site with Feature Selection by Random Forest
por: Li, Bi-Qing, et al.
Publicado: (2012)

Analysis and Prediction of Translation Rate Based on Sequence and Functional Features of the mRNA
por: Huang, Tao, et al.
Publicado: (2011)

Dysfunctions Associated with Methylation, MicroRNA Expression and Gene Expression in Lung Cancer
por: Huang, Tao, et al.
Publicado: (2012)

Distance Correlation-Based Feature Selection in Random Forest
por: Ratnasingam, Suthakaran, et al.
Publicado: (2023)

Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection
por: Ma, Xin, et al.
Publicado: (2015)

Unbiased Feature Selection in Learning Random Forests for High-Dimensional Data
por: Nguyen, Thanh-Tung, et al.
Publicado: (2015)

Identification of the core regulators of the HLA I-peptide binding process
por: Zhang, Yu-Hang, et al.
Publicado: (2017)

Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features
por: He, Zhisong, et al.
Publicado: (2010)

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest
por: Manavalan, Balachandran, et al.
Publicado: (2017)

Random forests for feature selection in QSPR Models - an application for predicting standard enthalpy of formation of hydrocarbons
por: Teixeira, Ana L, et al.
Publicado: (2013)

Random KNN feature selection - a fast and stable alternative to Random Forests
por: Li, Shengqiao, et al.
Publicado: (2011)

Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features, Subcellular Locations and Interaction Networks
por: Huang, Tao, et al.
Publicado: (2010)

Prediction of Protein-Protein Interaction Sites by Random Forest Algorithm with mRMR and IFS
por: Li, Bi-Qing, et al.
Publicado: (2012)

Efficient prediction of early-stage diabetes using XGBoost classifier with random forest feature selection technique
por: Gündoğdu, Serdar
Publicado: (2023)

Predicting Transcriptional Activity of Multiple Site p53 Mutants Based on Hybrid Properties
por: Huang, Tao, et al.
Publicado: (2011)

iPseU-NCP: Identifying RNA pseudouridine sites using random forest and NCP-encoded features
por: Nguyen-Vo, Thanh-Hoang, et al.
Publicado: (2019)

Forward variable selection for random forest models
por: Velthoen, Jasper, et al.
Publicado: (2022)

IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier
por: Zhu, Rong, et al.
Publicado: (2021)

Exploring Mouse Protein Function via Multiple Approaches
por: Huang, Guohua, et al.
Publicado: (2016)

Applications of random forest feature selection for fine‐scale genetic population assignment
por: Sylvester, Emma V. A., et al.
Publicado: (2017)

Using Recursive Feature Selection with Random Forest to Improve Protein Structural Class Prediction for Low-Similarity Sequences
por: Wang, Yaoxin, et al.
Publicado: (2021)

A network-based method using a random walk with restart algorithm and screening tests to identify novel genes associated with Menière's disease
por: Li, Lin, et al.
Publicado: (2017)

Feature selection and classification of urinary mRNA microarray data by iterative random forest to diagnose renal fibrosis: a two-stage study
por: Zhou, Le-Ting, et al.
Publicado: (2017)

Computational Analysis of HIV-1 Resistance Based on Gene Expression Profiles and the Virus-Host Interaction Network
por: Huang, Tao, et al.
Publicado: (2011)

Analysis of Tumor Suppressor Genes Based on Gene Ontology and the KEGG Pathway
por: Yang, Jing, et al.
Publicado: (2014)

Discovery of New Candidate Genes Related to Brain Development Using Protein Interaction Information
por: Chen, Lei, et al.
Publicado: (2015)

Predicting siRNA potency with random forests and support vector machines
por: Wang, Liangjiang, et al.
Publicado: (2010)

DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues
por: Ma, Xin, et al.
Publicado: (2016)

Guided regularized random forest feature selection for smartphone based human activity recognition
por: Thakur, Dipanwita, et al.
Publicado: (2022)

Binary Bamboo Forest Growth Optimization Algorithm for Feature Selection Problem
por: Pan, Jeng-Shyang, et al.
Publicado: (2023)

Prediction of Nucleosome Positioning Based on Transcription Factor Binding Sites
por: Yi, Xianfu, et al.
Publicado: (2010)

Adaptation of A-to-I RNA editing in bacteria, fungi, and animals
por: Duan, Yuange, et al.
Publicado: (2023)

Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest
por: Liao, Zhijun, et al.
Publicado: (2016)

Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions
por: Chen, Lei, et al.
Publicado: (2022)

A Hybrid Computational Method for the Discovery of Novel Reproduction-Related Genes
por: Chen, Lei, et al.
Publicado: (2015)

Predicting prognosis of endometrioid endometrial adenocarcinoma on the basis of gene expression and clinical features using Random Forest
por: Yin, Fufen, et al.
Publicado: (2019)

RFCell: A Gene Selection Approach for scRNA-seq Clustering Based on Permutation and Random Forest
por: Zhao, Yuan, et al.
Publicado: (2021)

Sequence Based Prediction of DNA-Binding Proteins Based on Hybrid Feature Selection Using Random Forest and Gaussian Naïve Bayes
por: Lou, Wangchao, et al.
Publicado: (2014)

Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods
por: Gómez-Ramírez, Jaime, et al.
Publicado: (2020)

Predictive Feature Generation and Selection Using Process Data From PISA Interactive Problem-Solving Items: An Application of Random Forests
por: Han, Zhuangzhuang, et al.
Publicado: (2019)

Cannot write session to /tmp/vufind_sessions/sess_fso95b4s9q23046n8g8rs64s22