Cargando…

RNAlight: a machine learning model to identify nucleotide features determining RNA subcellular localization

Different RNAs have distinct subcellular localizations. However, nucleotide features that determine these distinct distributions of lncRNAs and mRNAs have yet to be fully addressed. Here, we develop RNAlight, a machine learning model based on LightGBM, to identify nucleotide k-mers contributing to t...

Descripción completa

Detalles Bibliográficos
Autores principales:	Yuan, Guo-Hua, Wang, Ying, Wang, Guang-Zhong, Yang, Li
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Oxford University Press 2022
Materias:	Problem Solving Protocol
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9851306/ https://www.ncbi.nlm.nih.gov/pubmed/36464487 http://dx.doi.org/10.1093/bib/bbac509

Ejemplares similares

GP(4): an integrated Gram-Positive Protein Prediction Pipeline for subcellular localization mimicking bacterial sorting
por: Grasso, Stefano, et al.
Publicado: (2020)

TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization
por: Jeon, Young-Jun, et al.
Publicado: (2022)

Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients
por: Wang, Zihao, et al.
Publicado: (2021)

RNAdegformer: accurate prediction of mRNA degradation at nucleotide resolution with deep learning
por: He, Shujun, et al.
Publicado: (2023)

Three-nucleotide periodicity of nucleotide diversity in a population enables the identification of open reading frames
por: Jiang, Mengyun, et al.
Publicado: (2022)

A new method to accurately identify single nucleotide variants using small FFPE breast samples
por: Fortunato, Angelo, et al.
Publicado: (2021)

Discovering trends and hotspots of biosafety and biosecurity research via machine learning
por: Guan, Renchu, et al.
Publicado: (2022)

KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis
por: Qin, Xinghu, et al.
Publicado: (2022)

Machine learning for RNA 2D structure prediction benchmarked on experimental data
por: Justyna, Marek, et al.
Publicado: (2023)

Characterization on the oncogenic effect of the missense mutations of p53 via machine learning
por: Pan, Qisheng, et al.
Publicado: (2023)

Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review
por: Sherkatghanad, Zeinab, et al.
Publicado: (2023)

Predicting protein–membrane interfaces of peripheral membrane proteins using ensemble machine learning
por: Chatzigoulas, Alexios, et al.
Publicado: (2022)

Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power
por: Yu, Tzu-Hui, et al.
Publicado: (2021)

ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning
por: Wang, Xiaoyu, et al.
Publicado: (2022)

Predicting chromosomal compartments directly from the nucleotide sequence with DNA-DDA
por: Lainscsek, Xenia, et al.
Publicado: (2023)

TP53_PROF: a machine learning model to predict impact of missense mutations in TP53
por: Ben-Cohen, Gil, et al.
Publicado: (2022)

iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities
por: Xu, Jing, et al.
Publicado: (2023)

Converting single nucleotide variants between genome builds: from cautionary tale to solution
por: Ormond, Cathal, et al.
Publicado: (2021)

Inpactor2: a software based on deep learning to identify and classify LTR-retrotransposons in plant genomes
por: Orozco-Arias, Simon, et al.
Publicado: (2022)

Machine-learning scoring functions trained on complexes dissimilar to the test set already outperform classical counterparts on a blind benchmark
por: Li, Hongjian, et al.
Publicado: (2021)

VIPPID: a gene-specific single nucleotide variant pathogenicity prediction tool for primary immunodeficiency diseases
por: Fang, Mingyan, et al.
Publicado: (2022)

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction
por: Zhang, Yaqi, et al.
Publicado: (2022)

DeepFeature: feature selection in nonimage data using convolutional neural network
por: Sharma, Alok, et al.
Publicado: (2021)

MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors
por: Bonidia, Robson P, et al.
Publicado: (2021)

DISMIR: Deep learning-based noninvasive cancer detection by integrating DNA sequence and methylation information of individual cell-free DNA reads
por: Li, Jiaqi, et al.
Publicado: (2021)

SWEET: a single-sample network inference method for deciphering individual features in disease
por: Chen, Hsin-Hua, et al.
Publicado: (2023)

EXPERT: transfer learning-enabled context-aware microbial community classification
por: Chong, Hui, et al.
Publicado: (2022)

HiC1Dmetrics: framework to extract various one-dimensional features from chromosome structure data
por: Wang, Jiankang, et al.
Publicado: (2021)

Drug repositioning based on weighted local information augmented graph neural network
por: Meng, Yajie, et al.
Publicado: (2023)

MIMIC: an optimization method to identify cell type-specific marker panel for cell sorting
por: Zou, Meng, et al.
Publicado: (2021)

Identifying and characterizing drug sensitivity-related lncRNA-TF-gene regulatory triplets
por: Hu, Congxue, et al.
Publicado: (2022)

Zero-shot-capable identification of phage–host relationships with whole-genome sequence representation by contrastive learning
por: Zhang, Yao-zhong, et al.
Publicado: (2023)

SMG: self-supervised masked graph learning for cancer gene identification
por: Cui, Yan, et al.
Publicado: (2023)

Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs
por: Li, Ying Hong, et al.
Publicado: (2019)

Self-supervised contrastive learning for integrative single cell RNA-seq data analysis
por: Han, Wenkai, et al.
Publicado: (2022)

FLAN: feature-wise latent additive neural models for biological applications
por: Nguyen, An-Phi, et al.
Publicado: (2023)

Multiomics dynamic learning enables personalized diagnosis and prognosis for pancancer and cancer subtypes
por: Lu, Yuxing, et al.
Publicado: (2023)

Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids
por: Wang, Yu Mei, et al.
Publicado: (2023)

Sample-specific perturbation of gene interactions identifies breast cancer subtypes
por: Chen, Yuanyuan, et al.
Publicado: (2020)

BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria
por: Bonidia, Robson P, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_5qast1gfec0aqucsvb4s48gl1h