Human Papillomavirus Risk Type Classification from Protein Sequences Using Support Vector Machines

Kim, Sun; Zhang, Byoung-Tak

doi:10.1007/11732242_6

Cited by 6 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They also further developed the better algorithm based on geometric moments of protein distance matrix images using a Fuzzy K nearest neighbor classifier [ 10 ]. In addition, classification of HPV risk types was also proposed through algorithms based on decision tree [ 11 ], text mining [ 12 ], genetic mining of DNA sequence structures [ 13 ], support vector machines [ 14 ], gap-spectrum kernels [ 15 ], and ensemble support vector machines with protein secondary structures [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

A high performance prediction of HPV genotypes by Chaos game representation and singular value decomposition

2015

View full text Add to dashboard Cite

BackgroundHuman Papillomavirus (HPV) genotyping is an important approach to fight cervical cancer due to the relevant information regarding risk stratification for diagnosis and the better understanding of the relationship of HPV with carcinogenesis. This paper proposed two new feature extraction techniques, i.e. ChaosCentroid and ChaosFrequency, for predicting HPV genotypes associated with the cancer. The additional diversified 12 HPV genotypes, i.e. types 6, 11, 16, 18, 31, 33, 35, 45, 52, 53, 58, and 66, were studied in this paper.In our proposed techniques, a partitioned Chaos Game Representation (CGR) is deployed to represent HPV genomes. ChaosCentroid captures the structure of sequences in terms of centroid of each sub-region with Euclidean distances among the centroids and the center of CGR as the relations of all sub-regions. ChaosFrequency extracts the statistical distribution of mono-, di-, or higher order nucleotides along HPV genomes and forms a matrix of frequency of dots in each sub-region. For performance evaluation, four different types of classifiers, i.e. Multi-layer Perceptron, Radial Basis Function, K-Nearest Neighbor, and Fuzzy K-Nearest Neighbor Techniques were deployed, and our best results from each classifier were compared with the NCBI genotyping tool.ResultsThe experimental results obtained by four different classifiers are in the same trend. ChaosCentroid gave considerably higher performance than ChaosFrequency when the input length is one but it was moderately lower than ChaosFrequency when the input length is two. Both proposed techniques yielded almost or exactly the best performance when the input length is more than three. But there is no significance between our proposed techniques and the comparative alignment method.ConclusionsOur proposed alignment-free and scale-independent method can successfully transform HPV genomes with 7,000 - 10,000 base pairs into features of 1 - 11 dimensions. This signifies that our ChaosCentroid and ChaosFrequency can be served as the effective feature extraction techniques for predicting the HPV genotypes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0493-4) contains supplementary material, which is available to authorized users.

show abstract

Section: Introductionmentioning

confidence: 99%

A high performance prediction of HPV genotypes by Chaos game representation and singular value decomposition

2015

View full text Add to dashboard Cite

show abstract

“…Support Vector Machines (SVM). SVM is a discriminant classifier, which is defined by the classification hyperplane [27]. In other words, the labeled training samples are used to train the model, and then the test sample classification is realized by outputting the best hyperplane [67].…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…Through systematic analysis, Park et al suggested using decision tree to construct the typing model of human papillomavirus [ 25 ]. Kim and Zhang calculated the distance distribution of amino acid pairs and further predicted the risk type of HPV through E6 protein [ 26 , 27 ]. Kim et al extracted the differential features of protein secondary structure and designed a set of support vector machine (GSVM) to classify HPV types [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling and Computational Prediction of High-Risk Types of Human Papillomaviruses

Zhang

Wang

2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Cervical cancer is one of the main causes of cancer death all over the world. Most diseases such as cervical epithelial atypical hyperplasia and invasive cervical cancer are closely related to the continuous infection of high-risk types of human papillomavirus. Therefore, the high-risk types of human papillomavirus are the key to the prevention and treatment of cervical cancer. With the accumulation of high-throughput and clinical data, the use of systematic and quantitative methods for mathematical modeling and computational prediction has become more and more important. This paper summarizes the mathematical models and prediction methods of the risk types of human papillomavirus, especially around the key steps such as feature extraction, feature selection, and prediction algorithms. We summarized and discussed the advantages and disadvantages of existing algorithms, which provides a theoretical basis for follow-up research.

show abstract

“…Human papillomaviruses are icosahedral, nonenveloped particles that contain a small, double-stranded circular DNA of approximately 8000 nucleotide base pairs [ 8 ] and belong to the Papillomavirus family (papilloma, polyoma, and simian vacuolating viruses) [ 9 ]. The diameter of circular DNA is approximately 55 nm [ 10 – 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Park et al proposed a classification of the risk type of human papillomavirus by decision tree [ 26 ]. Kim and Zhang introduced the string kernel and Gap-spectrum kernel to compute the distances of amino acids pairs and further used them to classify HPV risk types based on E6 protein sequences [ 7 , 9 ]. Kim et al proposed an Ensemble support vector machine to classify HPV risk types based on the differential subsequences of protein secondary structures [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of High-Risk Types of Human Papillomaviruses Using Statistical Model of Protein “Sequence Space”

Hai

Liu

et al. 2015

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Discrimination of high-risk types of human papillomaviruses plays an important role in the diagnosis and remedy of cervical cancer. Recently, several computational methods have been proposed based on protein sequence-based and structure-based information, but the information of their related proteins has not been used until now. In this paper, we proposed using protein “sequence space” to explore this information and used it to predict high-risk types of HPVs. The proposed method was tested on 68 samples with known HPV types and 4 samples without HPV types and further compared with the available approaches. The results show that the proposed method achieved the best performance among all the evaluated methods with accuracy 95.59% and F1-score 90.91%, which indicates that protein “sequence space” could potentially be used to improve prediction of high-risk types of HPVs.

show abstract

Human Papillomavirus Risk Type Classification from Protein Sequences Using Support Vector Machines

Cited by 6 publications

References 17 publications

A high performance prediction of HPV genotypes by Chaos game representation and singular value decomposition

A high performance prediction of HPV genotypes by Chaos game representation and singular value decomposition

Mathematical Modeling and Computational Prediction of High-Risk Types of Human Papillomaviruses

Prediction of High-Risk Types of Human Papillomaviruses Using Statistical Model of Protein “Sequence Space”

Contact Info

Product

Resources

About