Corpus ID: 59872127

Application of Support Vector Machines in Bioinformatics

@inproceedings{Wang2002ApplicationOS,
  title={Application of Support Vector Machines in Bioinformatics},
  author={Jung-Ying Wang},
  year={2002}
}
Recently a new learning method called support vector machines (SVM) has shown comparable or better results than neural networks on some applications. In this thesis we exploit the possibility of using SVM for three important issues of bioinformatics: the prediction of protein secondary structure, multi-class protein fold recognition, and the prediction of human signal peptide cleavage sites. By using similar data, we demonstrate that SVM can easily achieve comparable accuracy as using neural… Expand
Protein Secondary Structure Prediction Using Support Vector Machines (SVMs)
  • Mayuri Patel, Hitesh B Shah
  • Computer Science
  • 2013 International Conference on Machine Intelligence and Research Advancement
  • 2013
TLDR
The intention is to use model based (i.e., supervised learning) approach for protein secondary structure prediction and the objective is to enhance the prediction of 2D protein structure problem using advance machine learning techniques like, linear and non-linear support vector machine with different kernel functions. Expand
Multi-class protein Structure Prediction Using Machine Learning Techniques
Protein structure prediction is the major problem in the field of bioinformatics or Computation biology. Recently many researchers used various dat a mining and machine learning tool for protein struExpand
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TLDR
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TLDR
It is shown that the RSVM formulation is already in a form of linear SVM and four RSVM implementations are discussed, which indicates that in general the test accuracy of RSVM are a little lower than that of the standard SVM. Expand
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TLDR
It is concluded that the proposed new turn classification is distinct and well‐defined, and machine learning classifiers are suited for sequence‐based turn prediction. Expand
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Abstract Support vector machines (SVMs) are initially designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research topic. A multiclassExpand
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Recently two kinds of reduction techniques which aimed at saving training time for SVM problems with nonlinear kernels were proposed. Instead of solving the standard SVM formulation, these methodsExpand
Bilinear Grid Search Strategy Based Support Vector Machines Learning Method
TLDR
A bilinear grid search method to achieve higher computation efficiency in choosing kernel parameters of SVM with RBF kernel and the protein secondary structure prediction can obtain a better learning accuracy compared with other related algorithms. Expand
A Review of Computational Intelligence Methods for Eukaryotic Promoter Prediction
TLDR
This paper reviews various computational methods for promoter prediction and several difficulties and pitfalls encountered by these methods are detailed, along with future research directions. Expand
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References

SHOWING 1-10 OF 92 REFERENCES
Multi-class protein fold recognition using support vector machines and neural networks
TLDR
This work investigated two new methods for protein fold prediction using the Support Vector Machine and the Neural Network learning methods as base classifiers, and examined many issues involved with large number of classes, including dependencies of prediction accuracy on the number of folds and on thenumber of representatives in a fold. Expand
Knowledge-based analysis of microarray gene expression data by using support vector machines.
  • M. Brown, W. Grundy, +5 authors D. Haussler
  • Computer Science, Medicine
  • Proceedings of the National Academy of Sciences of the United States of America
  • 2000
TLDR
A method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments, based on the theory of support vector machines (SVMs), to predict functional roles for uncharacterized yeast ORFs based on their expression data is introduced. Expand
Machine learning approaches for the prediction of signal peptides and other protein sorting signals.
TLDR
A hidden Markov model version of SignalP has been developed, making it possible to discriminate between cleaved signal peptides and uncleaved signal anchors, and it is shown how SignalP can be used to characterize putative signal peptide from an archaeon, Methanococcus jannaschii. Expand
Adaptive encoding neural networks for the recognition of human signal peptide cleavage sites
TLDR
An adaptive encoding artificial neural network (ACN) for recognition of sequence patterns is described and knowledge of physico-chemical properties is not necessary for training an ACN. Expand
A Neural Network Method for Identification of Prokaryotic and Eukaryotic Signal Peptides and Prediction of their Cleavage Sites
We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequences. The methodExpand
Recognition of a protein fold in the context of the SCOP classification
TLDR
A computational method has been developed for the assignment of a protein sequence to a folding class in the SCOP, using global descriptors of a primary protein sequence in terms of the physical, chemical, and structural properties of the constituent amino acids. Expand
Recognition of a protein fold in the context of the Structural Classification of Proteins (SCOP) classification.
A computational method has been developed for the assignment of a protein sequence to a folding class in the Structural Classification of Proteins (SCOP). This method uses global descriptors of aExpand
Molecular classification of multiple tumor types
TLDR
This work obtained 190 samples from 14 tumor classes and generated a combined expression dataset containing 16063 genes for each of those samples, and performed multi-class classification by combining the outputs of binary classifiers. Expand
Predicting the secondary structure of globular proteins using neural network models.
TLDR
It is concluded from computational experiments on real and artificial structures that no method based solely on local information in the protein sequence is likely to produce significantly better results for non-homologous proteins. Expand
Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.
We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequence. The methodExpand
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