Cargando…

Prediction of post-translational modification sites using multiple kernel support vector machine

Protein post-translational modification (PTM) is an important mechanism that is involved in the regulation of protein function. Considering the high-cost and labor-intensive of experimental identification, many computational prediction methods are currently available for the prediction of PTM sites...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wang, BingHua, Wang, Minghui, Li, Ao
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	PeerJ Inc. 2017
Materias:	Bioinformatics
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5410141/ https://www.ncbi.nlm.nih.gov/pubmed/28462053 http://dx.doi.org/10.7717/peerj.3261

Ejemplares similares

ksrMKL: a novel method for identification of kinase–substrate relationships using multiple kernel learning
por: Wang, Minghui, et al.
Publicado: (2017)

Deep learning and support vector machines for transcription start site identification
por: Barbero-Aparicio, José A., et al.
Publicado: (2023)

Classification of genomic components and prediction of genes of Begomovirus based on subsequence natural vector and support vector machine
por: Pei, Shaojun, et al.
Publicado: (2020)

miRBoost: boosting support vector machines for microRNA precursor classification
por: Tran, Van Du T., et al.
Publicado: (2015)

Prediction of endoplasmic reticulum resident proteins using fragmented amino acid composition and support vector machine
por: Kumar, Ravindra, et al.
Publicado: (2017)

Optimizing support vector machines and autoregressive integrated moving average methods for heart rate variability data correction
por: Svane, Jakob, et al.
Publicado: (2023)

MKL-GRNI: A parallel multiple kernel learning approach for supervised inference of large-scale gene regulatory networks
por: Wani, Nisar, et al.
Publicado: (2021)

Regularization, optimization, kernels, and support vector machines
por: Suykens, Johan A K, et al.
Publicado: (2015)

Support Vector Machines and Kernels for Computational Biology
por: Ben-Hur, Asa, et al.
Publicado: (2008)

Ranking Support Vector Machine with Kernel Approximation
por: Chen, Kai, et al.
Publicado: (2017)

SiteSeek: Post-translational modification analysis using adaptive locality-effective kernel methods and new profiles
por: Yoo, Paul D, et al.
Publicado: (2008)

PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information
por: Liu, Xia, et al.
Publicado: (2022)

Isolation of multiple electrocardiogram artifacts using independent vector analysis
por: Uddin, Zahoor, et al.
Publicado: (2023)

Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine
por: Li, Xiaoou, et al.
Publicado: (2014)

Structural landscape of base pairs containing post-transcriptional modifications in RNA
por: Seelam, Preethi P., et al.
Publicado: (2017)

PTM-ssMP: A Web Server for Predicting Different Types of Post-translational Modification Sites Using Novel Site-specific Modification Profile
por: Liu, Yu, et al.
Publicado: (2018)

Privacy Preserving RBF Kernel Support Vector Machine
por: Li, Haoran, et al.
Publicado: (2014)

Identification of MicroRNA Precursors with Support Vector Machine and String Kernel
por: Xu, Jian-Hua, et al.
Publicado: (2008)

Multivariate Pattern Analysis of Lifelong Premature Ejaculation Based on Multiple Kernel Support Vector Machine
por: Geng, Bowen, et al.
Publicado: (2022)

An analytical study on the identification of N-linked glycosylation sites using machine learning model
por: Akmal, Muhammad Aizaz, et al.
Publicado: (2022)

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO
por: Li, Yang, et al.
Publicado: (2018)

Are sites with multiple single nucleotide variants in cancer genomes a consequence of drivers, hypermutable sites or sequencing errors?
por: Smith, Thomas C.A., et al.
Publicado: (2016)

Fourier Ring Correlation and Anisotropic Kernel Density Estimation Improve Deep Learning Based SMLM Reconstruction of Microtubules
por: Berberich, Andreas, et al.
Publicado: (2021)

Learning with kernels: support vector machines, regularization, optimization, and beyond
por: Schölkopf, Bernhard, et al.
Publicado: (2002)

Support Vector Machine with Ensemble Tree Kernel for Relation Extraction
por: Liu, Xiaoyong, et al.
Publicado: (2016)

BERT-Kgly: A Bidirectional Encoder Representations From Transformers (BERT)-Based Model for Predicting Lysine Glycation Site for Homo sapiens
por: Liu, Yinbo, et al.
Publicado: (2022)

Integrative machine learning analysis of multiple gene expression profiles in cervical cancer
por: Tan, Mei Sze, et al.
Publicado: (2018)

Identification of rare alternative splicing events in MS/MS data reveals a significant fraction of alternative translation initiation sites
por: Kroll, José E., et al.
Publicado: (2014)

Towards a piRNA prediction using multiple kernel fusion and support vector machine
por: Brayet, Jocelyn, et al.
Publicado: (2014)

Modeling the binding specificity of the RNA-binding protein GLD-1 suggests a function of coding region–located sites in translational repression
por: Brümmer, Anneke, et al.
Publicado: (2013)

Identification of consistent post-translational regulatory triplets related to oncogenic and tumour suppressive modulators in childhood acute lymphoblastic leukemia
por: Wee, YongKiat, et al.
Publicado: (2021)

A multi-kernel and multi-scale learning based deep ensemble model for predicting recurrence of non-small cell lung cancer
por: Kim, Gihyeon, et al.
Publicado: (2023)

An introduction to support vector machines: and other kernel-based learning methods
por: Cristianini, Nello, et al.
Publicado: (2003)

Classifying DNA repair genes by kernel-based support vector machines
por: Jiang, Hao, et al.
Publicado: (2011)

Correlation Kernels for Support Vector Machines Classification with Applications in Cancer Data
por: Jiang, Hao, et al.
Publicado: (2012)

Prediction of Protein–Protein Interaction with Pairwise Kernel Support Vector Machine
por: Zhang, Shao-Wu, et al.
Publicado: (2014)

Churn prediction in telecommunication industry using kernel Support Vector Machines
por: Y., Nguyen Nhu, et al.
Publicado: (2022)

An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples
por: Bose, Shilpi, et al.
Publicado: (2021)

Predicting post-translational lysine acetylation using support vector machines
por: Gnad, Florian, et al.
Publicado: (2010)

A systematic computational analysis of the rRNA–3′ UTR sequence complementarity suggests a regulatory mechanism influencing post-termination events in metazoan translation
por: Pánek, Josef, et al.
Publicado: (2016)

Cannot write session to /tmp/vufind_sessions/sess_8ahrfbpou8o5qkccis015sg7el