Additionally, we observed that all the NN models, including ours, achieved a high specificity and a low sensitivity when they were trained on the balanced dataset and tested on the imbalanced dataset. This suggested that, in future works, we need to consider using a dataset with the original distribution for training. This will train the models to recognize the favorisen Araması distribution of the methylation site prediction task, which has far more negative than positive samples, leading to better performance in practice.
This research is a collaboration between Bioinformatics and Data Science Research Center BDSRCBina Nusantara University and Department of Computer Science, Faculty of Mathematics and Natural Science, University of Lampung. The GPU Tesla P used to conduct the experiment was provided by NVIDIA - BINUS AIRDC. Competing Interests The authors declare there are no competing interests. Author Contributions Contributed by Favorisen Rosyking Lumbanraja conceived and designed the experiments, authored or reviewed drafts of the paper, supervised and guided the research, and approved the final draft.
Bens Pardamean analyzed the data, authored or reviewed drafts of the paper, supervised and guided the research, and approved the final draft. Data Availability The following information was supplied regarding data availability:. Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation.
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Learn more: PMC Disclaimer PMC Copyright Notice. PeerJ Comput Sci. Published online Aug doi: PMCID: PMC Favorisen Rosyking Lumbanraja1 Bharuno Mahesworo2, 3 Tjeng Wawan Cenggoro2, 4 Digdo Sudigyo2 and Bens Pardamean 2, 5. Favorisen Rosyking Lumbanraja 1 Department of Computer Science, Faculty of Mathematics and Natural Science, University of Lampung, Bandar Lampung, Lampung, Indonesia Find articles by Favorisen Rosyking Lumbanraja.
Bharuno Mahesworo 2 Bioinformatics and Data Science Research Center, Bina Nusantara University, West Jakarta, Jakarta, Indonesia 3 Statistics Departement, School of Computer Science, Bina Nusantara University, West Jakarta, Jakarta, Indonesia Find articles by Bharuno Mahesworo. Tjeng Wawan Cenggoro 2 Bioinformatics and Data Science Research Center, Bina Nusantara University, West Jakarta, Jakarta, Indonesia 4 Computer Science Departement, School of Computer Science, Bina Nusantara University, West Jakarta, Jakarta, Indonesia Find articles by Tjeng Wawan Cenggoro.
Digdo Favorisen Araması 2 Bioinformatics and Data Science Research Center, Bina Nusantara University, West Jakarta, Jakarta, Indonesia Find articles by Digdo Sudigyo. Bens Pardamean 2 Bioinformatics and Data Science Research Center, Bina Nusantara University, West Jakarta, Jakarta, Indonesia 5 Computer Science Department, BINUS Graduate Program - Master of Computer Science, Bina Nusantara University, West Jakarta, Jakarta, Indonesia Find articles by Bens Pardamean. Author information Article notes Copyright and License information PMC Disclaimer.
Corresponding author. Bharuno Mahesworo: ude. sunib orowseham. Received Apr 15; Accepted Jul Copyright © Lumbanraja et al. This is an open access article distributed under the terms of the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed.
For attribution, the original author stitle, publication source PeerJ Computer Science and either DOI or URL of the article must be cited. Associated Data Supplementary Materials Supplemental Information 1: The model implementation, training lotusbet Ödeme Hızını Faktörler testing codes The code is written in python and the file is in. ipynb type file.
rar 7. Supplemental Information 2: Datasets of possible location for methylation in protein sequences 1 positive training dataset, 5 negative training datasets, 1 positive validation independent dataset, 1 negative validation independent dataset, 1 positive testing dataset, 1 negative testing dataset.
rar K. Abstract Background Conventional in vivo methods for post-translational modification site prediction such as spectrophotometry, Western blotting, and chromatin immune precipitation can be very expensive and time-consuming.
How does a reverse phone lookup work?Method We designed our model to be able to extract spatial and sequential information from amino acid sequences. Results Our model appeared to be favorisen Araması in almost all measurement when trained on the balanced training dataset.
Conclusion Our models achieved the best performance across different environments in almost all measurements. Keywords: Methylation, Prediction, Spatial, Sequential, CNN, LSTM, Deep Learning. Introduction Methylation is a post-translational modification PTM process that modifies the functional and conformational changes of a protein. Methods Dataset The dataset in this study was obtained from the previous methylation site prediction study by Kumar et al.
Table 1 Protein sequence dataset example. No Sequence 1st 2nd 3rd. Open in a separate window. Table 2 Amino acids sequences dataset list.
Data class Label n sequences Training Positive 1, Negative 5, Balanced training Positive 1, Negative 1, Validation Positive 1, Negative 3, Balanced validation Asper casino Hizmeti favorisen Araması, Negative 1, Independent Test Positive Negative Experiment First, to understand the contribution of each element in the proposed model, we carried an ablation study on our proposed model.
Figure 1. Research workflow. Spatial and sequential methylation fusion network SSMFN Our proposed model, the Spatial and Sequential Methylation Fusion Network SSMFNwas designed with the motivation that a protein sequence can be perceived as both spatial and sequential data. Figure 2. Favorisen Araması 3 Hyperparameter settings. Parameter Settings Learning rate 0. Comparison to a standard multi-layer perceptron A standard multi-layer perceptron SMLP NN was developed to be compared to our proposed model.
Figure 3. Comparison to DeepRMethylSite For a fair comparison of our proposed model to other state-of-the-art methylation site prediction models, we re-conducted the experiment to train DeepRMethylSite Chaudhari et al. Evaluation To evaluate the performance of the proposed model and to compare it to the models from previous studies, we utilized Accuracy Eq.
Table 4 The first ablation study, trained on the balanced training dataset. Model Acc F1 Sens Spec MCC AUC Validated on the imbalanced validation dataset SSMFN CNN 0. Table 5 The second ablation study, trained on the imbalanced training dataset. Table 6 The first experiment, trained on the balanced training dataset. Model Acc F1 Sens Spec MCC Favorisen Araması Validated on the imbalanced validation dataset Favorisen Araması CNN 0.
Table 7 Second experiment, trained on the imbalanced training dataset. Conclusions In general, our proposed model, SSMFN, provided better performance compared to DeepRMethylSite. Supplemental Information Supplemental Information 1 The model implementation, training and testing codes: The code is written in python and the file is in. Click here for additional data file.
Supplemental Information 2 Datasets of possible location for methylation in protein sequences: 1 positive training dataset, 5 negative training datasets, 1 positive piabella casino Tv independent dataset, 1 negative validation independent dataset, 1 positive testing dataset, 1 negative testing dataset Click here for additional data file.
Acknowledgments This research is a collaboration between Bioinformatics and Data Science Research Center BDSRCBina Nusantara University and Department of Computer Science, Faculty of Mathematics and Natural Science, University of Lampung. Funding Favorisen Araması The authors received no funding for this work. Additional Information and Declarations Competing Interests The authors declare there are no competing interests.
Contributed by Bens Pardamean analyzed the data, authored or reviewed drafts of the paper, supervised and guided the research, and approved the final draft. References Apweiler et al. UniProt: the universal protein knowledge base. Nucleic Acids Research. Arginine methylation: an emerging regulator of protein function. Molecular Cell. Bradley Bradley AP. The use of the area under favorisen Araması ROC curve in the evaluation of machine learning algorithms.
Pattern Recognition. Chaudhari et al. DeepRMethylSite: a deep learning based bahispub Bingo Oyna for prediction of arginine methylation sites in proteins. Molecular Omics. Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.
Briefings in Bioinformatics. Deng et al. Computational prediction of methylation types of covalently modified lysine and arginine residues in proteins. Framewise phoneme classification with bidirectional lstm networks.
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