Cargando…

Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features

Heat shock proteins (HSPs) are ubiquitous in living organisms. HSPs are an essential component for cell growth and survival; the main function of HSPs is controlling the folding and unfolding process of proteins. According to molecular function and mass, HSPs are categorized into six different famil...

Descripción completa

Detalles Bibliográficos
Autores principales:	Jing, Xiao-Yang, Li, Feng-Min
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Hindawi 2020
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7530508/ https://www.ncbi.nlm.nih.gov/pubmed/33029195 http://dx.doi.org/10.1155/2020/8894478

Ejemplares similares

A Novel Feature Extraction Method with Feature Selection to Identify Golgi-Resident Protein Types from Imbalanced Data
por: Yang, Runtao, et al.
Publicado: (2016)

Iterative ensemble feature selection for multiclass classification of imbalanced microarray data
por: Yang, Junshan, et al.
Publicado: (2016)

Protein transfer learning improves identification of heat shock protein families
por: Min, Seonwoo, et al.
Publicado: (2021)

Imbalanced biomedical data classification using self-adaptive multilayer ELM combined with dynamic GAN
por: Zhang, Liyuan, et al.
Publicado: (2018)

Hellinger distance-based stable sparse feature selection for high-dimensional class-imbalanced data
por: Fu, Guang-Hui, et al.
Publicado: (2020)

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification
por: Haque, Mohammad Nazmul, et al.
Publicado: (2016)

An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data
por: Wang, Kung-Jeng, et al.
Publicado: (2013)

Classifying Extremely Imbalanced Data Sets
por: Britsch, M, et al.
Publicado: (2010)

Learning from imbalanced data sets
por: Fernández, Alberto, et al.
Publicado: (2018)

Advances of Heat Shock Family in Ulcerative Colitis
por: Gong, Min, et al.
Publicado: (2022)

Feature Ranking and Screening for Class-Imbalanced Metabolomics Data Based on Rank Aggregation Coupled with Re-Balance
por: Fu, Guang-Hui, et al.
Publicado: (2021)

JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method
por: Zhang, Lina, et al.
Publicado: (2015)

Heat Shock Protein 70 and 90 Family in Prostate Cancer
por: Fu, Xun, et al.
Publicado: (2022)

Efficient Selection of Gaussian Kernel SVM Parameters for Imbalanced Data
por: Tsai, Chen-An, et al.
Publicado: (2023)

Heat Shock Protein 70 Family in Response to Multiple Abiotic Stresses in the Silkworm
por: Fang, Shou-Min, et al.
Publicado: (2021)

Adaptive Fusion Based Method for Imbalanced Data Classification
por: Liang, Zefeng, et al.
Publicado: (2022)

SMOTE for high-dimensional class-imbalanced data
por: Blagus, Rok, et al.
Publicado: (2013)

Selective oversampling approach for strongly imbalanced data
por: Gnip, Peter, et al.
Publicado: (2021)

Heat shock proteins and heat shock protein-antibody complexes in placental tissues.
por: Ziegert, M, et al.
Publicado: (1999)

Targeting Heat-Shock Protein 90 in Cancer: An Update on Combination Therapy
por: Ren, Xiude, et al.
Publicado: (2022)

High Expression of Heat Shock Protein Family D Member 1 Predicts Poor Prognosis of Esophageal Cancer
por: Lv, Jing, et al.
Publicado: (2022)

Iterative Nearest Neighborhood Oversampling in Semisupervised Learning from Imbalanced Data
por: Li, Fengqi, et al.
Publicado: (2013)

Imbalanced classification for protein subcellular localization with multilabel oversampling
por: Rana, Priyanka, et al.
Publicado: (2022)

The heat shock factor family from Triticum aestivum in response to heat and other major abiotic stresses and their role in regulation of heat shock protein genes
por: Xue, Gang-Ping, et al.
Publicado: (2014)

Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric
por: Boughorbel, Sabri, et al.
Publicado: (2017)

Predicting defects in imbalanced data using resampling methods: an empirical investigation
por: Malhotra, Ruchika, et al.
Publicado: (2022)

Hybrid Feature Selection Framework for the Parkinson Imbalanced Dataset Prediction Problem
por: Qasim, Hayder Mohammed, et al.
Publicado: (2021)

Statistical methods for imbalanced data in ecological and biological studies
por: Komori, Osamu, et al.
Publicado: (2019)

Class prediction for high-dimensional class-imbalanced data
por: Blagus, Rok, et al.
Publicado: (2010)

An empirical evaluation of sampling methods for the classification of imbalanced data
por: Kim, Misuk, et al.
Publicado: (2022)

Heat-Shock Proteins in Autoimmunity
por: Moudgil, Kamal D., et al.
Publicado: (2013)

Heat Shock Proteins as Immunomodulants
por: Zininga, Tawanda, et al.
Publicado: (2018)

Heat-Shock Proteins in Neuroinflammation
por: Dukay, Brigitta, et al.
Publicado: (2019)

Heat Shock Proteins and Inflammasomes
por: Martine, Pierre, et al.
Publicado: (2019)

Heat Shock Proteins in Inflammation
por: Bromberg, Z., et al.
Publicado: (2007)

Heat Shock Proteins and Ferroptosis
por: Liu, Ying, et al.
Publicado: (2022)

Heat Shock Proteins in Cancer Immunotherapy
por: Das, Jugal Kishore, et al.
Publicado: (2019)

Exploiting the Diversity of the Heat-Shock Protein Family for Primary and Secondary Tauopathy Therapeutics
por: Abisambra, Jose F, et al.
Publicado: (2011)

Four Members of Heat Shock Protein 70 Family in Korean Rose Bitterling (Rhodeus uyekii)
por: Kim, Jung Hyun, et al.
Publicado: (2015)

iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets
por: Jia, Jianhua, et al.
Publicado: (2016)

Cannot write session to /tmp/vufind_sessions/sess_nfium97mdnsbsm01de6u5nmf4s