Effective Classification of Melting Curve in Real-time PCR Based on Dynamic Filter-based Convolutional Neural Network
- 14 September 2021
- journal article
- research article
- Published by Bentham Science Publishers Ltd. in Current Bioinformatics
- Vol. 16 (6), 820-828
- https://doi.org/10.2174/1574893616666210212084839
Abstract
Background: The effective classification of the melting curve is conducive to measure the specificity of the amplified products and the influence of invalid data on subsequent experiments is excluded. Objective: In this paper, a convolutional neural network (CNN) classification model based on dynamic filter is proposed, which can categorize the number of peaks in the melting curve image and distinguish the pollution data represented by the noise peaks. Method: The main advantage of the proposed model is that it adopts the filter which changes with the input and uses the dynamic filter to capture more information in the image, making the network learning more accurate. In addition, the residual module is used to extract the characteristics of the melting curve, and the pooling operation is replaced with an atrous convolution to prevent the loss of context information. Result: In order to train the proposed model, a novel melting curve dataset is created, which includes a balanced dataset and an unbalanced dataset. The proposed method uses six classification-based assessment criteria to compare with seven representative methods based on deep learning. Experimental results show that proposed method is not only markedly outperforms the other state-of-the-art methods in accuracy, but also has much less running time. Conclusion: It evidently proves that the proposed method is suitable for judging the specificity of amplification products according to the melting curve. Simultaneously, it overcomes the difficulties of manual selection with low efficiency and artificial bias.Keywords
Funding Information
- Jilin province science and technology development plan project (20190303134SF, 20180201064SF)
- National Natural Science Foundation of China (61672259, 61876070)
- Regional Joint Fund of NSFC (U19A2057)
- National Key Research and Development Program of China (2018YFB0804203)
This publication has 27 references indexed in Scilit:
- Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural NetworksRadiology, 2017
- Deep Learning for Health InformaticsIEEE Journal of Biomedical and Health Informatics, 2016
- Deep learning in bioinformaticsBriefings in Bioinformatics, 2016
- Rapid real-time PCR and high resolution melt analysis in a self-filling thermoplastic chipLab on a Chip, 2016
- Deep Residual Learning for Image RecognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- A Rapid Multiplex Real-Time PCR High-Resolution Melt Curve Assay for the Simultaneous Detection of Bacillus cereus, Listeria monocytogenes, and Staphylococcus aureus in FoodJournal of Food Protection, 2016
- Recent advances in quantitative PCR (qPCR) applications in food microbiologyFood Microbiology, 2011
- mRNA and microRNA quality control for RT-qPCR analysisMethods, 2010
- QPCR: Application for real-time PCR data management and analysisBMC Bioinformatics, 2009
- Selection of reference genes for gene expression studies in pig tissues using SYBR green qPCRBMC Molecular Biology, 2007