Dataset Information

Im6A-TS-CNN: Identifying the N6-Methyladenine Site in Multiple Tissues by Using the Convolutional Neural Network.

ABSTRACT: N6-methyladenosine (m6A) is the most abundant post-transcriptional modification and involves a series of important biological processes. Therefore, accurate detection of the m6A site is very important for revealing its biological functions and impacts on diseases. Although both experimental and computational methods have been proposed for identifying m6A sites, few of them are able to detect m6A sites in different tissues. With the consideration of the spatial specificity of m6A modification, it is necessary to develop methods able to detect the m6A site in different tissues. In this work, by using the convolutional neural network (CNN), we proposed a new method, called im6A-TS-CNN, that can identify m6A sites in brain, liver, kidney, heart, and testis of Homo sapiens, Mus musculus, and Rattus norvegicus. In im6A-TS-CNN, the samples were encoded by using the one-hot encoding scheme. The results from both a 5-fold cross-validation test and independent dataset test demonstrate that im6A-TS-CNN is better than the existing method for the same purpose. The command-line version of im6A-TS-CNN is available at https://github.com/liukeweiaway/DeepM6A_cnn.

SUBMITTER: Liu K

PROVIDER: S-EPMC7473875 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

im6A-TS-CNN: Identifying the N6-Methyladenine Site in Multiple Tissues by Using the Convolutional Neural Network.

Liu Kewei K Cao Lei L Du Pufeng P Chen Wei W

Molecular therapy. Nucleic acids 20200731

N6-methyladenosine (m6A) is the most abundant post-transcriptional modification and involves a series of important biological processes. Therefore, accurate detection of the m6A site is very important for revealing its biological functions and impacts on diseases. Although both experimental and computational methods have been proposed for identifying m6A sites, few of them are able to detect m6A sites in different tissues. With the considera ...[more]

PMID: 32858457

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Project description:BACKGROUND:The ?-Lactamase (BL) enzyme family is an important class of enzymes that plays a key role in bacterial resistance to antibiotics. As the newly identified number of BL enzymes is increasing daily, it is imperative to develop a computational tool to classify the newly identified BL enzymes into one of its classes. There are two types of classification of BL enzymes: Molecular Classification and Functional Classification. Existing computational methods only address Molecular Classification and the performance of these existing methods is unsatisfactory. RESULTS:We addressed the unsatisfactory performance of the existing methods by implementing a Deep Learning approach called Convolutional Neural Network (CNN). We developed CNN-BLPred, an approach for the classification of BL proteins. The CNN-BLPred uses Gradient Boosted Feature Selection (GBFS) in order to select the ideal feature set for each BL classification. Based on the rigorous benchmarking of CCN-BLPred using both leave-one-out cross-validation and independent test sets, CCN-BLPred performed better than the other existing algorithms. Compared with other architectures of CNN, Recurrent Neural Network, and Random Forest, the simple CNN architecture with only one convolutional layer performs the best. After feature extraction, we were able to remove ~95% of the 10,912 features using Gradient Boosted Trees. During 10-fold cross validation, we increased the accuracy of the classic BL predictions by 7%. We also increased the accuracy of Class A, Class B, Class C, and Class D performance by an average of 25.64%. The independent test results followed a similar trend. CONCLUSIONS:We implemented a deep learning algorithm known as Convolutional Neural Network (CNN) to develop a classifier for BL classification. Combined with feature selection on an exhaustive feature set and using balancing method such as Random Oversampling (ROS), Random Undersampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE), CNN-BLPred performs significantly better than existing algorithms for BL classification.

Dataset Information

Im6A-TS-CNN: Identifying the N6-Methyladenine Site in Multiple Tissues by Using the Convolutional Neural Network.

Publications

im6A-TS-CNN: Identifying the N<sup>6</sup>-Methyladenine Site in Multiple Tissues by Using the Convolutional Neural Network.

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