Maps: An integrated system for protein sequence annotation using support vector machine

Journal of the Chinese Institute of Engineers ◽

10.1080/02533839.2008.9671432 ◽

2008 ◽

Vol 31 (5) ◽

pp. 781-790

Author(s):

Jung‐Ying Wang ◽

Cheng‐Kang Liu ◽

Hahn‐Ming Lee

Keyword(s):

Support Vector Machine ◽

Protein Sequence ◽

Integrated System ◽

Support Vector ◽

Sequence Annotation

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Identification of ligand-binding residues using protein sequence profile alignment and query-specific support vector machine model

Analytical Biochemistry ◽

10.1016/j.ab.2020.113799 ◽

2020 ◽

Vol 604 ◽

pp. 113799

Author(s):

Jun Hu ◽

Liang Rao ◽

Xueqiang Fan ◽

Guijun Zhang

Keyword(s):

Support Vector Machine ◽

Protein Sequence ◽

Support Vector Machine Model ◽

Support Vector ◽

Sequence Profile ◽

Machine Model ◽

Specific Support ◽

Binding Residues ◽

Protein Sequence Profile ◽

Profile Alignment

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PhosphoSVM: prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine

Amino Acids ◽

10.1007/s00726-014-1711-5 ◽

2014 ◽

Vol 46 (6) ◽

pp. 1459-1469 ◽

Author(s):

Yongchao Dou ◽

Bo Yao ◽

Chi Zhang

Keyword(s):

Support Vector Machine ◽

Protein Sequence ◽

Support Vector ◽

Phosphorylation Sites

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Prediction of transporter family from protein sequence by support vector machine approach

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.20605 ◽

2005 ◽

Vol 62 (1) ◽

pp. 218-231 ◽

Author(s):

H. H. Lin ◽

L. Y. Han ◽

C. Z. Cai ◽

Z. L. Ji ◽

Y. Z. Chen

Keyword(s):

Support Vector Machine ◽

Protein Sequence ◽

Support Vector ◽

Transporter Family

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Power Quality Analysis For A Solar-Grid Integrated System Using Support Vector Machine

2021 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) ◽

10.1109/saupec/robmech/prasa52254.2021.9377221 ◽

2021 ◽

Author(s):

K. Moloi ◽

B. A. Thango ◽

A. F. Nnnachi ◽

J. A. Jordaan ◽

Y. Hamam

Keyword(s):

Support Vector Machine ◽

Power Quality ◽

Integrated System ◽

Quality Analysis ◽

Support Vector ◽

Power Quality Analysis

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Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure

Bioinformatics ◽

10.1093/bioinformatics/btl475 ◽

2006 ◽

Vol 22 (22) ◽

pp. 2753-2760 ◽

Author(s):

Darrin P. Lewis ◽

Tony Jebara ◽

William Stafford Noble

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Empirical Analysis ◽

Protein Sequence ◽

Heterogeneous Data ◽

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Identifying protein arginine methylation sites using global features of protein sequence coupled with support vector machine optimized by particle swarm optimization algorithm

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2015.05.011 ◽

2015 ◽

Vol 146 ◽

pp. 102-107 ◽

Author(s):

Yan Zhang ◽

Lijuan Tang ◽

Hongyan Zou ◽

Qin Yang ◽

Xinliang Yu ◽

...

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Protein Sequence ◽

Particle Swarm Optimization Algorithm ◽

Arginine Methylation ◽

Support Vector ◽

Global Features ◽

Swarm Optimization ◽

Protein Arginine Methylation

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A Support Vector Machine Based Technique for Fault Detection in A Power Distribution Integrated System with Renewable Energy Distributed Generation

Advances in Science Technology and Engineering Systems Journal ◽

10.25046/aj050468 ◽

2020 ◽

Vol 5 (4) ◽

pp. 577-588

Author(s):

Katleho Moloi ◽

Yskandar Hamam ◽

Jacobus Andries Jordaan

Keyword(s):

Support Vector Machine ◽

Renewable Energy ◽

Fault Detection ◽

Distributed Generation ◽

Power Distribution ◽

Integrated System ◽

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Faculty Opinions recommendation of Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1047219.497143 ◽

2006 ◽

Author(s):

Burkhard Rost

Keyword(s):

Support Vector Machine ◽

Structural Properties ◽

Protein Sequence ◽

Support Vector ◽

Catalytic Residues

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Prediction of protein structural class using a combined representation of protein-sequence information and support vector machine

2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) ◽

10.1109/bibmw.2010.5703781 ◽

2010 ◽

Author(s):

Li Wu ◽

Qi Dai ◽

Bin Han ◽

Lei Zhu ◽

Lihua Li

Keyword(s):

Support Vector Machine ◽

Protein Sequence ◽

Support Vector ◽

Sequence Information ◽

Structural Class ◽

Protein Structural Class

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INS/WSN-Integrated Navigation Utilizing LS-SVM andH∞Filtering

Mathematical Problems in Engineering ◽

10.1155/2012/707326 ◽

2012 ◽

Vol 2012 ◽

pp. 1-19 ◽

Author(s):

Yuan Xu ◽

Xiyuan Chen ◽

Qinghua Li

Keyword(s):

Support Vector Machine ◽

Kalman Filter ◽

Back Propagation ◽

Sampling Period ◽

Integrated System ◽

Support Vector ◽

Integrated Navigation ◽

New Approach ◽

Stochastic Uncertainty ◽

Integrated Navigation System

In order to achieve continuous navigation capability in areas such as tunnels, urban canyons, and indoors a new approach using least squares support vector machine (LS-SVM) andH∞filter (HF) for integration of INS/WSN is proposed. In the integrated system, HF estimates the errors of position and velocity while the signals in WSNs are available. Meanwhile, the compensation model is trained by LS-SVM with corresponding HF states. Once outages of the signals in WSNs, the model is used to correct INS solution as HF does. Moreover, due to device reasons, there are slight fluctuations in sampling period in practice. For overcoming this problem of integrated navigation, the theoretical analysis and implementation of HF for an integrated navigation system with stochastic uncertainty are also given. Simulation shows the performance of HF is more robust compared with INS-only solution and Kalman filter (KF) solution, and the prediction of LS-SVM has the smallest error compared with INS-only and back propagation (BP), the improvement is particularly obvious.

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