DNA Sequence Classification by Convolutional Neural Network

Nguyen, Nhat Minh; Vu, Tran Anh; Ngo, Duc Luu; Phan, Dau; Lumbanraja, Favorisen Rosyking; Faisal, Mohammad Reza; Abapihi, Bahriddin; Kubo, Mamoru; Satou, Kenji

doi:10.4236/jbise.2016.95021

Cited by 130 publications

(103 citation statements)

References 6 publications

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“…For Splice and Promoter datasets, we compared the performance of proposed model with the performance of previous model conducted by Nguyen et al [12]. The results from this research are known as the best performance prior to our research.…”

Section: Performance Evaluation Of the Methodsmentioning

confidence: 96%

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Combined Use of k-Mer Numerical Features and Position-Specific Categorical Features in Fixed-Length DNA Sequence Classification

Phan¹,

Nguyen²,

Lumbanraja³

et al. 2017

JBiSE

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To classify DNA sequences, k-mer frequency is widely used since it can convert variablelength sequences into fixed-length and numerical feature vectors. However, in case of fixedlength DNA sequence classification, subsequences starting at a specific position of the given sequence can also be used as categorical features. Through the performance evaluation on six datasets of fixed-length DNA sequences, our algorithm based on the above idea achieved comparable or better performance than other state-of-the art algorithms.

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Section: Performance Evaluation Of the Methodsmentioning

confidence: 96%

“…For predicting nuc- Table 6. Assessment of our model and model in [12] by two-sample t-test assuming equal variances.…”

Section: Discussionmentioning

confidence: 99%

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Combined Use of k-Mer Numerical Features and Position-Specific Categorical Features in Fixed-Length DNA Sequence Classification

Phan¹,

Nguyen²,

Lumbanraja³

et al. 2017

JBiSE

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“…Convolutional neural networks (CNNs) have been widely tested and successfully used for image analysis, especially in segmentation problems, such as differentiating between an object and the background. 8,9 With the development of more advanced CNN architectures (e.g., CNN models involving more layers, new activation functions, more options for objective functions to calculate error, more sophisticated model structures) and use of graphics processing units with higher computational speeds, CNNs are being developed to analyze a growing variety of data types, including medical images, 10-14 electron microscopy images, 13 DNA data, [15][16][17] spectra, [18][19][20] and chemical structures. 21,22 For example, CNN models have proven the ability to identify brain tumors in magnetic resonance images (MRI) faster and more accurately than the state of the art tools and can identify the pancreas in computerized tomography (CT) images, both of which are challenging analysis problems because of anatomical variability.…”

Section: Image Analysis Via Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Tap water fingerprinting using a convolutional neural network built from images of the coffee-ring effect

Sanderson

Allen

et al. 2020

Analyst

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A low-cost tap water fingerprinting technique was evaluated using the coffee-ring effect, a phenomenon by which tap water droplets leave distinguishable "fingerprint" residue patterns after water evaporates. Tap waters from communities across southern Michigan dried on aluminum and photographed with a cell phone camera and 30× loupe produced unique and reproducible images. A convolutional neural network (CNN) model was trained using the images from the Michigan tap waters, and despite the small size of the image dataset, the model assigned images into groups with similar water chemistry with 80% accuracy. Synthetic solutions containing only the majority species measured in Detroit, Lansing, and Michigan State University tap waters did not display the same residue patterns as collected waters; thus, the lower concentration species also influence the tap water "fingerprint". Residue pattern images from salt mixtures with an array of sodium, calcium, magnesium, chloride, bicarbonate, and sulfate concentrations were analyzed by measuring features observed in the photographs as well as using principal component analysis (PCA) on the image files and particles measurements. These analyses together highlighted differences in the residue patterns associated with the water chemistry in the sample. The results of these experiments suggest that the unique and reproducible residue patterns of tap water samples that can be imaged with a cell phone camera and a loupe contain a wealth of information about the overall composition of the tap water, and thus, the phenomenon should be further explored for potential use in lowcost tap water fingerprinting. † Data and CNN model are available at https://doi.org/10.5281/zenodo.3550247 ‡ Electronic supplementary information (ESI) available: Replicate images, synthetic tap water recipes, a trilinear plot for the collected tap waters, and detailed descriptions of residue patterns according to water chemistry. See

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“…An example is the correlation of the DNA methylation state with the age of a patient for forensic purposes (Vidaki et al, 2017). However, Nguyen et al (2016) applied a convolutional neural network (CNN) to treat DNA sequences as a string input and store the position of the nucleotides in the sequence. In the CNN described by Umarov and Solovyev (2017) the promoter regions of eu-and prokaryotes were successfully predicted, whereas in Khawaldeh et al (2017) DNA sequences were classified based on their taxonomy.…”

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confidence: 99%

An artificial neural network identifies glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

Janßen

Zabel

Lukas

et al. 2019

Preprint

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Artificial neural networks can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network to support environmental monitoring efforts in case of a contamination event by detecting induced changes towards the microbial communities. The neural net was trained on taxonomic cluster count tables obtained via next-generation amplicon sequencing of water column samples originating from a lab microcosm incubation experiment conducted over 140 days to determine the effects of the herbicide glyphosate on succession within brackish-water microbial communities. Glyphosate-treated assemblages were classified correctly; a subsetting approach identified the clusters primarily responsible for this, permitting the reduction of input features. This study demonstrates the potential of artificial neural networks to predict indicator species in cases of glyphosate contamination. The results could empower the development of environmental monitoring strategies with applications limited to neither glyphosate nor amplicon sequence data.

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DNA Sequence Classification by Convolutional Neural Network

Cited by 130 publications

References 6 publications

Combined Use of k-Mer Numerical Features and Position-Specific Categorical Features in Fixed-Length DNA Sequence Classification

Combined Use of k-Mer Numerical Features and Position-Specific Categorical Features in Fixed-Length DNA Sequence Classification

Tap water fingerprinting using a convolutional neural network built from images of the coffee-ring effect

An artificial neural network identifies glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

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