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

Abstract

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.

Predicting protein structural classes based on complex networks and recurrence analysis

“…CGR is a graphical tool which iteratively maps a genomic sequence into a 2-dimensional space. The map preserves all information of the input sequence and also provides an intuitive picture which helps to reveal hidden patterns and local structures more efficiently [35]. Several studies, for example [13,[35][36][37] have been showed that this graphical tool provides researchers with a number of novel features.…”

“…Next, the gained features are used by artificial neural network as a powerful machine learning approach for classification. In [35] the authors addressed the problem of predicting the Human Papillomavirus (HPV) genotypes from their genomes. For this purpose, two new feature extraction algorithms have been proposed based on CGR.…”

An investigation into inter- and intragenomic variations of graphic genomic signatures

“…These methods include statistical studies of word frequency within a DNA sequence [ 5 , 29 – 34 ], or employ k -words and the Markov model to obtain information about DNA sequences [ 35 – 39 ]. Iterated map methods for DNA sequence comparison include CGR-based analyses, see [ 3 , 40 – 46 ], and such alignment-free methods have been successfully applied for sequence comparison [ 4 , 11 , 12 , 47 – 53 ].…”

DCGR: feature extractions from protein sequences based on CGR via remodeling multiple information

“…The i-th (i > 1) point is placed halfway between the (i-1)-th point and the vertex corresponding to the i-th nucleotide. Being capable of discovering the inner pattern of gene sequences, CGR has been widely used in the investigation of DNA sequences [23][24][25][26][27][28]. Encouraged by the CGR of DNA sequences, the CGR of protein sequences has also been extensively studied by many researchers.…”