csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou’s 5-step rule

Liu, Ze; Dong, Wei; Jiang, Wei; He, Zhi Min

doi:10.1038/s41598-019-49430-4

Cited by 30 publications

(23 citation statements)

References 56 publications

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“…We performed 5-fold cross-validation. In 5-fold cross validation, each fold was iteratively selected to test the model, and the remaining four folds were used to train the model [ 59 , 60 ]. For group normalization, we fix the number of groups to 8.…”

Section: Resultsmentioning

confidence: 99%

Fusing Visual Attention CNN and Bag of Visual Words for Cross-Corpus Speech Emotion Recognition

Seo

2020

Sensors

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Speech emotion recognition (SER) classifies emotions using low-level features or a spectrogram of an utterance. When SER methods are trained and tested using different datasets, they have shown performance reduction. Cross-corpus SER research identifies speech emotion using different corpora and languages. Recent cross-corpus SER research has been conducted to improve generalization. To improve the cross-corpus SER performance, we pretrained the log-mel spectrograms of the source dataset using our designed visual attention convolutional neural network (VACNN), which has a 2D CNN base model with channel- and spatial-wise visual attention modules. To train the target dataset, we extracted the feature vector using a bag of visual words (BOVW) to assist the fine-tuned model. Because visual words represent local features in the image, the BOVW helps VACNN to learn global and local features in the log-mel spectrogram by constructing a frequency histogram of visual words. The proposed method shows an overall accuracy of 83.33%, 86.92%, and 75.00% in the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), the Berlin Database of Emotional Speech (EmoDB), and Surrey Audio-Visual Expressed Emotion (SAVEE), respectively. Experimental results on RAVDESS, EmoDB, SAVEE demonstrate improvements of 7.73%, 15.12%, and 2.34% compared to existing state-of-the-art cross-corpus SER approaches.

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Section: Resultsmentioning

confidence: 99%

Fusing Visual Attention CNN and Bag of Visual Words for Cross-Corpus Speech Emotion Recognition

Seo

2020

Sensors

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“…To develop a useful bioinformatics predictor, a series of recent publications [43], [44]- [50] and review papers [51,52] demonstrate that one needs to follow Chou's 5-steps rule [51] that includes the following five steps: (1) construction of a gold standard dataset to train and test the model; (2) encoding samples with effective formulations; (3) conducting the prediction model with a powerful classifier; (4) evaluating model performance by using cross-validation tests and standard measures; (5) establishing a user-friendly tool for the predictor that can be accessible to the public. A new bioinformatics predictor presented according to the five-step rules [51] would have the following advantages: (i) clear logic deduction; (ii) better demonstration in stimulating other relevant tools; (iii) useful in practical application [56]. We followed this, five-step procedures to develop our new prediction method.…”

Section: Methodsmentioning

confidence: 99%

A Deep Learning Model for Predicting DNA N6-Methyladenine (6mA) Sites in Eukaryotes

Roland

Wannige

2020

IEEE Access

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DNA N6-methyladenine (6mA) is an epigenetic modification, which is involved in many biological regulation processes like DNA replication, DNA repair, transcription, and gene expression regulation. The widespread presence of this 6mA modification in eukaryotes has been unclear until recently. Studying the genome-wide distribution of 6mA can provide a deeper understanding of the epigenetic modification process and the biological processes it involves. Existing experimental techniques are time-consuming and computational machine learning methods have room for performance improvement. DNA N6-methyladenine prediction in eukaryotic cross-species shows low performance. Hence, there is a need for a more accurate, time-efficient method to predict the distribution of 6mA sites in eukaryotes. Since deep learning architectures have shown higher accuracy, we develop a customized VGG16 architecture-based model named 6mAVGG using convolution neural networks for the prediction of DNA 6mA sites in eukaryotes. We introduce a novel 3-dimensional encoding mechanism extending the one-hot encoding method to support the input of the VGG16 model. Specifically, the 10-fold crossvalidation on the benchmark datasets for the proposed model achieves higher accuracies of 98.01%, 97.44%, 99.56% respectively for cross-species, Rice, and M. musculus genomes. The proposed model outperforms existing tools for the prediction of 6mA sites and has enhanced accuracies by 2.88%, 4.2%, 0.9% respectively for cross-species, Rice, and M. musculus genomes compared to the state of the art method SNNRice6mA. The model trained with benchmark data predicts 6mA sites of other species ArabidopsisThaliana, RosaChinensis, Drosophila, and Yeast with prediction accuracy over 70%. Thus, this model can be used for the genome-wide prediction of 6mA sites in eukaryotes.

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“…The prediction is based on encoding the genomic DNA samples using dinucleotide composition and optimized dinucleotide-based DNA properties [85]. (4) csDMA: Identification and prediction of DNA 6mA modification in different species via Chou's 5-step rule using three encoding features and different algorithms to generate the feature matrix [86]. (5) iDNA6mA-Rice: Evaluation ofm6A sites in the rice genome using the machine learning random forest algorithm to formulate the sample as an input to discriminate from the methylated and non-methylated sites [87].…”

Section: Bioinformatic Analysis Tools For 6mamentioning

confidence: 99%

DNA N6-Methyladenine Modification in Plant Genomes—A Glimpse into Emerging Epigenetic Code

Karanthamalai

Chodon

Chauhan

et al. 2020

Plants

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N6-methyladenine (6mA) is a DNA base modification at the 6th nitrogen position; recently, it has been resurfaced as a potential reversible epigenetic mark in eukaryotes. Despite its existence, 6mA was considered to be absent due to its undetectable level. However, with the new advancements in methods, considerable 6mA distribution is identified across the plant genome. Unlike 5-methylcytosine (5mC) in the gene promoter, 6mA does not have a definitive role in repression but is exposed to have divergent regulation in gene expression. Though 6mA information is less known, the available evidences suggest its function in plant development, tissue differentiation, and regulations in gene expression. The current review article emphasizes the research advances in DNA 6mA modifications, identification, available databases, analysis tools and its significance in plant development, cellular functions and future perspectives of research.

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csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou’s 5-step rule

Cited by 30 publications

References 56 publications

Fusing Visual Attention CNN and Bag of Visual Words for Cross-Corpus Speech Emotion Recognition

Fusing Visual Attention CNN and Bag of Visual Words for Cross-Corpus Speech Emotion Recognition

A Deep Learning Model for Predicting DNA N6-Methyladenine (6mA) Sites in Eukaryotes

DNA N6-Methyladenine Modification in Plant Genomes—A Glimpse into Emerging Epigenetic Code

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