Combined classifier for unknown genome classification using chaos game representation features

Nair, Vrinda V.; Nair, Achuthsankar S.

doi:10.1145/1722024.1722065

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

et al. 2019

BMC Bioinformatics

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Background Protein feature extraction plays an important role in the areas of similarity analysis of protein sequences and prediction of protein structures, functions and interactions. The feature extraction based on graphical representation is one of the most effective and efficient ways. However, most existing methods suffer limitations from their method design. Results We introduce DCGR, a novel method for extracting features from protein sequences based on the chaos game representation, which is developed by constructing CGR curves of protein sequences according to physicochemical properties of amino acids, followed by converting the CGR curves into multi-dimensional feature vectors by using the distributions of points in CGR images. Tested on five data sets, DCGR was significantly superior to the state-of-the-art feature extraction methods. Conclusion The DCGR is practically powerful for extracting effective features from protein sequences, and therefore important in similarity analysis of protein sequences, study of protein-protein interactions and prediction of protein functions. It is freely available at https://sourceforge.net/projects/transcriptomeassembly/files/Feature%20Extraction . Electronic supplementary material The online version of this article (10.1186/s12859-019-2943-x) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

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

Zhang

et al. 2019

BMC Bioinformatics

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“…We used k-mer frequency vectors to train supervised 88 classification algorithms. Similar approaches have previously been explored (with 89 different classifiers than those used here), for example to subtype Influenza and classify 90 Polyoma and Rhinovirus fragments [50], to predict HPV genotypes [51,52], to classify 91 whole bacterial genomes to their corresponding taxonomic groups at different levels [53], 92 to classify whole eukaryotic mitochondrial genomes [54][55][56][57], to classify microbial 93 metagenomic samples [58], to predict virus-host relationships for some bacterial 94 genera [59], and to identify viral sequences in metagenomic samples [60]. 95 To evaluate our method, we curated manually-validated testing sets of 'real-world' 96 HIV-1 data sets.…”

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confidence: 99%

An open-sourcek-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes

Solis-Reyes

Avino

Poon

et al. 2018

Preprint

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For many disease-causing virus species, global diversity is clustered into a taxonomy of subtypes with clinical significance. In particular, the classification of infections among the subtypes of human immunodeficiency virus type 1 (HIV-1) is a routine component of clinical management, and there are now many classification algorithms available for this purpose. Although several of these algorithms are similar in accuracy and speed, the majority are proprietary and require laboratories to transmit HIV-1 sequence data over the network to remote servers. This potentially exposes sensitive patient data to unauthorized access, and makes it impossible to determine how classifications are made and to maintain the data provenance of clinical bioinformatic workflows. We propose an open-source supervised and alignment-free subtyping method (Kameris) that operates on k-mer frequencies in HIV-1 sequences. We performed a detailed study of the accuracy and performance of subtype classification in comparison to four state-of-the-art programs. Based on our testing data set of manually curated real-world HIV-1 sequences (n = 2, 784), Kameris obtained an overall accuracy of 97%, which matches or exceeds all other tested software, with a processing rate of over 1,500 sequences per second. Furthermore, our fully standalone general-purpose software provides key advantages in terms of data security and privacy, transparency and reproducibility. Finally, we show that our method is readily adaptable to subtype classification of other viruses including dengue, influenza A, and hepatitis B and C virus.

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Classification of Eukaryotic Organisms Through Cepstral Analysis of Mitochondrial DNA

Adetiba

Olugbara

2016

Lecture Notes in Computer Science

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Combined classifier for unknown genome classification using chaos game representation features

Cited by 8 publications

References 23 publications

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

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

An open-sourcek-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes

Classification of Eukaryotic Organisms Through Cepstral Analysis of Mitochondrial DNA

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