title: A high performance prediction of HPV genotypes by Chaos game representation and singular value decomposition.
abstract: BACKGROUND
  Human 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.
  
  
  RESULTS
  The 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.
  
  
  CONCLUSIONS
  Our 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.
authors.affiliation: Advanced Virtual and Intelligent Computing Research Center (AVIC), Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Phayathai Road, Bangkok, Thailand. Watcharaporn.T@Student.chula.ac.th.
authors.forename: Watcharaporn
authors.initials: W
authors.lastname: Tanchotsrinon
authors.affiliation: Advanced Virtual and Intelligent Computing Research Center (AVIC), Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Phayathai Road, Bangkok, Thailand. lchidcha@gmail.com.
authors.forename: Chidchanok
authors.initials: C
authors.lastname: Lursinsap
authors.affiliation: Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Phayathai Road, Bangkok, Thailand. Yong.P@chula.ac.th.
authors.forename: Yong
authors.initials: Y
authors.lastname: Poovorawan
mesh_terms: D005260:Female; D016679:Genome, Viral; D005838:Genotype; D006801:Humans; D016571:Neural Networks, Computer; D027383:Papillomaviridae / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*; D002583:Uterine Cervical Neoplasms / Q000821:virology