StackRAM: a cross-species method for identifying RNA N6-methyladenosine sites based on stacked ensemble

ABSTRACTN6-methyladenosine is a prevalent RNA methylation modification, which plays an important role in various biological processes. Accurate identification of the m6A sites is fundamental to deeply understand the biological functions and mechanisms of the modification. However, the experimental methods for detecting m6A sites are usually time-consuming and expensive, and various computational methods have been developed to identify m6A sites in RNA. This paper proposes a novel cross-species computational method StackRAM using machine learning algorithms to identify the m6A sites in S. cerevisiae、H. sapiens and A. thaliana. First, the RNA sequences features are extracted through binary encoding, chemical property, nucleotide frequency, k-mer nucleotide frequency, pseudo dinucleotide composition, and position-specific trinucleotide propensity, and the initial feature set is obtained by feature fusion. Secondly, the Elastic Net is used for the first time to filter redundant and noisy information and retain important features for m6A sites classification. Finally, the base-classifiers output probabilities are combined with the optimal feature subset corresponding to the Elastic Net, and the combination feature input the second-stage meta-classifier SVM. The jackknife test on training dataset S. cerevisiae indicates that the prediction performance of StackRAM is superior to the current state-of-the-art methods. StackRAM prediction accuracy for independent test datasets H. sapiens and A. thaliana reach 92.30% and 87.06%, respectively. Therefore, StackRAM has development potential in cross-species prediction and can be a useful method for identifying m6A sites. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/StackRAM/.

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DNN-m6A: A Cross-Species Method for Identifying RNA N6-Methyladenosine Sites Based on Deep Neural Network with Multi-Information Fusion

Genes ◽

10.3390/genes12030354 ◽

2021 ◽

Vol 12 (3) ◽

pp. 354

Author(s):

Lu Zhang ◽

Xinyi Qin ◽

Min Liu ◽

Ziwei Xu ◽

Guangzhong Liu

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Area Under The Curve ◽

Nucleotide Composition ◽

Computational Method ◽

Feature Subset ◽

Accurate Identification ◽

Genome Wide ◽

Dinucleotide Composition ◽

Optimal Feature Subset

As a prevalent existing post-transcriptional modification of RNA, N6-methyladenosine (m6A) plays a crucial role in various biological processes. To better radically reveal its regulatory mechanism and provide new insights for drug design, the accurate identification of m6A sites in genome-wide is vital. As the traditional experimental methods are time-consuming and cost-prohibitive, it is necessary to design a more efficient computational method to detect the m6A sites. In this study, we propose a novel cross-species computational method DNN-m6A based on the deep neural network (DNN) to identify m6A sites in multiple tissues of human, mouse and rat. Firstly, binary encoding (BE), tri-nucleotide composition (TNC), enhanced nucleic acid composition (ENAC), K-spaced nucleotide pair frequencies (KSNPFs), nucleotide chemical property (NCP), pseudo dinucleotide composition (PseDNC), position-specific nucleotide propensity (PSNP) and position-specific dinucleotide propensity (PSDP) are employed to extract RNA sequence features which are subsequently fused to construct the initial feature vector set. Secondly, we use elastic net to eliminate redundant features while building the optimal feature subset. Finally, the hyper-parameters of DNN are tuned with Bayesian hyper-parameter optimization based on the selected feature subset. The five-fold cross-validation test on training datasets show that the proposed DNN-m6A method outperformed the state-of-the-art method for predicting m6A sites, with an accuracy (ACC) of 73.58%–83.38% and an area under the curve (AUC) of 81.39%–91.04%. Furthermore, the independent datasets achieved an ACC of 72.95%–83.04% and an AUC of 80.79%–91.09%, which shows an excellent generalization ability of our proposed method.

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m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

10.21203/rs.3.rs-39526/v2 ◽

2020 ◽

Author(s):

Xiao Chen ◽

Yi Xiong ◽

Yinbo Liu ◽

Yuqing Chen ◽

Shoudong Bi ◽

...

Keyword(s):

Cell Fate ◽

Prediction Accuracy ◽

Cytosine Methylation ◽

Low Cost ◽

Computational Method ◽

Selection Strategy ◽

Support Vector ◽

Feature Subset ◽

Biological Functions ◽

Accurate Identification

Abstract Background: As one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functions such as RNA metabolism and cell fate decision. Through accurate identification of 5-methylcytosine (m5C) sites on RNA, researchers can better understand the exact role of 5-cytosine-methylation in these biological functions. In recent years, computational methods of predicting m5C sites have attracted lots of interests because of its efficiency and low-cost. However, both the accuracy and efficiency of these methods are not satisfactory yet and need further improvement. Results: In this work, we have developed a new computational method, m5CPred-SVM, to identify m5C sites in three species, H. sapiens, M. musculus and A. thaliana. To build this model, we first collected benchmark datasets following three recently published methods. Then, six types of sequence-based features were generated based on RNA segments and the sequential forward feature selection strategy was used to obtain the optimal feature subset. After that, the performance of models based on different learning algorithms were compared, and the model based on the support vector machine provided the highest prediction accuracy. Finally, our proposed method, m5CPred-SVM was compared with several existing methods, and the result showed that m5CPred-SVM offered substantially higher prediction accuracy than previously published methods. It is expected that our method, m5CPred-SVM, can become a useful tool for accurate identification of m5C sites.Conclusion: In this study, by introducing position-specific propensity related features, we built a new model, m5CPred-SVM, to predict RNA m5C sites of three different species. The result shows that our model outperformed the existing state-of-art models. Our model is available for users through a web server at http://zhulab.ahu.edu.cn/m5CPred-SVM.

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m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

10.21203/rs.3.rs-39526/v3 ◽

2020 ◽

Author(s):

Xiao Chen ◽

Yi Xiong ◽

Yinbo Liu ◽

Yuqing Chen ◽

Shoudong Bi ◽

...

Keyword(s):

Cell Fate ◽

Prediction Accuracy ◽

Cytosine Methylation ◽

Low Cost ◽

Computational Method ◽

Selection Strategy ◽

Support Vector ◽

Feature Subset ◽

Biological Functions ◽

Accurate Identification

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Computational Method for Identifying Malonylation Sites by Using Random Forest Algorithm

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666181227144318 ◽

2020 ◽

Vol 23 (4) ◽

pp. 304-312

Author(s):

ShaoPeng Wang ◽

JiaRui Li ◽

Xijun Sun ◽

Yu-Hang Zhang ◽

Tao Huang ◽

...

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Random Forest ◽

Learning Algorithms ◽

Selection Procedure ◽

Machine Learning Algorithms ◽

Computational Method ◽

Feature Subset ◽

Random Forest Algorithm ◽

Post Translational Modification

Background: As a newly uncovered post-translational modification on the ε-amino group of lysine residue, protein malonylation was found to be involved in metabolic pathways and certain diseases. Apart from experimental approaches, several computational methods based on machine learning algorithms were recently proposed to predict malonylation sites. However, previous methods failed to address imbalanced data sizes between positive and negative samples. Objective: In this study, we identified the significant features of malonylation sites in a novel computational method which applied machine learning algorithms and balanced data sizes by applying synthetic minority over-sampling technique. Method: Four types of features, namely, amino acid (AA) composition, position-specific scoring matrix (PSSM), AA factor, and disorder were used to encode residues in protein segments. Then, a two-step feature selection procedure including maximum relevance minimum redundancy and incremental feature selection, together with random forest algorithm, was performed on the constructed hybrid feature vector. Results: An optimal classifier was built from the optimal feature subset, which featured an F1-measure of 0.356. Feature analysis was performed on several selected important features. Conclusion: Results showed that certain types of PSSM and disorder features may be closely associated with malonylation of lysine residues. Our study contributes to the development of computational approaches for predicting malonyllysine and provides insights into molecular mechanism of malonylation.

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i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome

Bioinformatics ◽

10.1093/bioinformatics/btz015 ◽

2019 ◽

Vol 35 (16) ◽

pp. 2796-2800 ◽

Cited By ~ 98

Author(s):

Wei Chen ◽

Hao Lv ◽

Fulei Nie ◽

Hao Lin

Keyword(s):

Dna Sequences ◽

Rice Genome ◽

Chemical Properties ◽

Computational Method ◽

Accurate Identification ◽

Feature Selection Technique ◽

Jackknife Test ◽

Genome Wide ◽

Wide Range ◽

User Friendly

Abstract Motivation DNA N6-methyladenine (6mA) is associated with a wide range of biological processes. Since the distribution of 6mA site in the genome is non-random, accurate identification of 6mA sites is crucial for understanding its biological functions. Although experimental methods have been proposed for this regard, they are still cost-ineffective for detecting 6mA site in genome-wide scope. Therefore, it is desirable to develop computational methods to facilitate the identification of 6mA site. Results In this study, a computational method called i6mA-Pred was developed to identify 6mA sites in the rice genome, in which the optimal nucleotide chemical properties obtained by the using feature selection technique were used to encode the DNA sequences. It was observed that the i6mA-Pred yielded an accuracy of 83.13% in the jackknife test. Meanwhile, the performance of i6mA-Pred was also superior to other methods. Availability and implementation A user-friendly web-server, i6mA-Pred is freely accessible at http://lin-group.cn/server/i6mA-Pred.

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BERT-m7G: A Transformer Architecture Based on BERT and Stacking Ensemble to Identify RNA N7-Methylguanosine Sites from Sequence Information

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/7764764 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Lu Zhang ◽

Xinyi Qin ◽

Min Liu ◽

Guangzhong Liu ◽

Yuxiao Ren

Keyword(s):

Regulation Of Gene Expression ◽

Ensemble Classifier ◽

Computational Method ◽

Sequence Information ◽

Feature Subset ◽

Rna Sequences ◽

Accurate Identification ◽

Selection Scheme ◽

Net Method ◽

Functional Mechanisms

As one of the most prevalent posttranscriptional modifications of RNA, N7-methylguanosine (m7G) plays an essential role in the regulation of gene expression. Accurate identification of m7G sites in the transcriptome is invaluable for better revealing their potential functional mechanisms. Although high-throughput experimental methods can locate m7G sites precisely, they are overpriced and time-consuming. Hence, it is imperative to design an efficient computational method that can accurately identify the m7G sites. In this study, we propose a novel method via incorporating BERT-based multilingual model in bioinformatics to represent the information of RNA sequences. Firstly, we treat RNA sequences as natural sentences and then employ bidirectional encoder representations from transformers (BERT) model to transform them into fixed-length numerical matrices. Secondly, a feature selection scheme based on the elastic net method is constructed to eliminate redundant features and retain important features. Finally, the selected feature subset is input into a stacking ensemble classifier to predict m7G sites, and the hyperparameters of the classifier are tuned with tree-structured Parzen estimator (TPE) approach. By 10-fold cross-validation, the performance of BERT-m7G is measured with an ACC of 95.48% and an MCC of 0.9100. The experimental results indicate that the proposed method significantly outperforms state-of-the-art prediction methods in the identification of m7G modifications.

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m5CPred-SVM: a novel method for predicting m5C sites of RNA

BMC Bioinformatics ◽

10.1186/s12859-020-03828-4 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Xiao Chen ◽

Yi Xiong ◽

Yinbo Liu ◽

Yuqing Chen ◽

Shoudong Bi ◽

...

Keyword(s):

Cell Fate ◽

Prediction Accuracy ◽

Cytosine Methylation ◽

Low Cost ◽

Computational Method ◽

Selection Strategy ◽

Support Vector ◽

Feature Subset ◽

Biological Functions ◽

Accurate Identification

Abstract Background As one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functions such as RNA metabolism and cell fate decision. Through accurate identification of 5-methylcytosine (m5C) sites on RNA, researchers can better understand the exact role of 5-cytosine-methylation in these biological functions. In recent years, computational methods of predicting m5C sites have attracted lots of interests because of its efficiency and low-cost. However, both the accuracy and efficiency of these methods are not satisfactory yet and need further improvement. Results In this work, we have developed a new computational method, m5CPred-SVM, to identify m5C sites in three species, H. sapiens, M. musculus and A. thaliana. To build this model, we first collected benchmark datasets following three recently published methods. Then, six types of sequence-based features were generated based on RNA segments and the sequential forward feature selection strategy was used to obtain the optimal feature subset. After that, the performance of models based on different learning algorithms were compared, and the model based on the support vector machine provided the highest prediction accuracy. Finally, our proposed method, m5CPred-SVM was compared with several existing methods, and the result showed that m5CPred-SVM offered substantially higher prediction accuracy than previously published methods. It is expected that our method, m5CPred-SVM, can become a useful tool for accurate identification of m5C sites. Conclusion In this study, by introducing position-specific propensity related features, we built a new model, m5CPred-SVM, to predict RNA m5C sites of three different species. The result shows that our model outperformed the existing state-of-art models. Our model is available for users through a web server at https://zhulab.ahu.edu.cn/m5CPred-SVM.

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m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

10.21203/rs.3.rs-39526/v1 ◽

2020 ◽

Author(s):

Xiao Chen ◽

Yi Xiong ◽

Yinbo Liu ◽

Yuqing Chen ◽

Shoudong Bi ◽

...

Keyword(s):

Cell Fate ◽

Prediction Accuracy ◽

Cytosine Methylation ◽

Computational Method ◽

Selection Strategy ◽

Support Vector ◽

Feature Subset ◽

Accurate Identification ◽

Benchmark Datasets ◽

Optimal Feature Subset

Abstract Background: As one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functionssuch as RNA metabolism and cell fate decision. Through accurate identification of 5-methylcytosine (m5C) sites on RNA,researcherscanbetter understandthe exact role of 5-cytosine-methylation in these biological functions. In recent years, computational methods of predicting m5C sites have attracted lots of interests because of its efficiency and low-cost.However, both the accuracy and efficiency of these methods are not satisfactory yet and need further improvement.Results: In this work, we have developed a new computational method, m5CPred-SVM, to identify m5C sites in three species, H. sapiens, M. musculus and A. thaliana. To build this model, we first collected benchmark datasets following three recently published methods. Then, six types of sequence-based features were generated based on RNA segments and the sequential forward feature selection strategy was used to obtain the optimal feature subset. After that, the performance of models based on different learning algorithms were compared, and the model based on the support vector machine provided the highest prediction accuracy. Finally, our proposed method, m5CPred-SVM was compared with several existing methods, and the result showed that m5CPred-SVMoffered substantially higher prediction accuracy thanpreviously published methods. It is expected that our method, m5CPred-SVM, can become a useful tool for accurate identification of m5C sites.Conclusion: In this study, by introducing position-specific propensity related features, we built a new model, m5CPred-SVM, to predict RNA m5C sites of three different species.The result shows that our model outperformed the existing state-of-art models.Our model is available for users through a web serverat http://zhulab.ahu.edu.cn/m5CPred-SVM.

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NeuroCS: A Tool to Predict Cleavage Sites of Neuropeptide Precursors

Protein and Peptide Letters ◽

10.2174/0929866526666191112150636 ◽

2020 ◽

Vol 27 (4) ◽

pp. 337-345 ◽

Cited By ~ 1

Author(s):

Ying Wang ◽

Juanjuan Kang ◽

Ning Li ◽

Yuwei Zhou ◽

Zhongjie Tang ◽

...

Keyword(s):

Support Vector Machine ◽

Characteristic Curve ◽

Training Dataset ◽

Support Vector ◽

Feature Subset ◽

Cleavage Sites ◽

Accurate Identification ◽

Testing Dataset ◽

Optimal Feature Subset ◽

Independent Testing Dataset

Background: Neuropeptides are a class of bioactive peptides produced from neuropeptide precursors through a series of extremely complex processes, mediating neuronal regulations in many aspects. Accurate identification of cleavage sites of neuropeptide precursors is of great significance for the development of neuroscience and brain science. Objective: With the explosive growth of neuropeptide precursor data, it is pretty much needed to develop bioinformatics methods for predicting neuropeptide precursors’ cleavage sites quickly and efficiently. Method : We started with processing the neuropeptide precursor data from SwissProt and NueoPedia into two sets of data, training dataset and testing dataset. Subsequently, six feature extraction schemes were applied to generate different feature sets and then feature selection methods were used to find the optimal feature subset of each. Thereafter the support vector machine was utilized to build models for different feature types. Finally, the performance of models were evaluated with the independent testing dataset. Results: Six models are built through support vector machine. Among them the enhanced amino acid composition-based model reaches the highest accuracy of 91.60% in the 5-fold cross validation. When evaluated with independent testing dataset, it also showed an excellent performance with a high accuracy of 90.37% and Area under Receiver Operating Characteristic curve up to 0.9576. Conclusion: The performance of the developed model was decent. Moreover, for users’ convenience, an online web server called NeuroCS is built, which is freely available at http://i.uestc.edu.cn/NeuroCS/dist/index.html#/. NeuroCS can be used to predict neuropeptide precursors’ cleavage sites effectively.

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Building an Effective Intrusion Detection System by Using Hybrid Data Optimization Based on Machine Learning Algorithms

Security and Communication Networks ◽

10.1155/2019/7130868 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 12

Author(s):

Jiadong Ren ◽

Jiawei Guo ◽

Wang Qian ◽

Huang Yuan ◽

Xiaobing Hao ◽

...

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Machine Learning Algorithms ◽

Training Dataset ◽

Feature Subset ◽

Data Sampling ◽

Hybrid Data ◽

Data Optimization

Intrusion detection system (IDS) can effectively identify anomaly behaviors in the network; however, it still has low detection rate and high false alarm rate especially for anomalies with fewer records. In this paper, we propose an effective IDS by using hybrid data optimization which consists of two parts: data sampling and feature selection, called DO_IDS. In data sampling, the Isolation Forest (iForest) is used to eliminate outliers, genetic algorithm (GA) to optimize the sampling ratio, and the Random Forest (RF) classifier as the evaluation criteria to obtain the optimal training dataset. In feature selection, GA and RF are used again to obtain the optimal feature subset. Finally, an intrusion detection system based on RF is built using the optimal training dataset obtained by data sampling and the features selected by feature selection. The experiment will be carried out on the UNSW-NB15 dataset. Compared with other algorithms, the model has obvious advantages in detecting rare anomaly behaviors.

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