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

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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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

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

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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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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FEATURE SELECTION BASED ON MINIMUM ERROR MINIMAX PROBABILITY MACHINE

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001407005958 ◽

2007 ◽

Vol 21 (08) ◽

pp. 1279-1292 ◽

Cited By ~ 5

Author(s):

ZENGLIN XU ◽

IRWIN KING ◽

MICHAEL R. LYU

Keyword(s):

Feature Selection ◽

Prediction Accuracy ◽

Support Vector ◽

Feature Subset ◽

Minimum Error ◽

Automatic Balance ◽

Classification Framework ◽

Data Partitions ◽

Minimax Probability Machine ◽

Optimal Feature Subset

Feature selection is an important task in pattern recognition. Support Vector Machine (SVM) and Minimax Probability Machine (MPM) have been successfully used as the classification framework for feature selection. However, these paradigms cannot automatically control the balance between prediction accuracy and the number of selected features. In addition, the selected feature subsets are also not stable in different data partitions. Minimum Error Minimax Probability Machine (MEMPM) has been proposed for classification recently. In this paper, we outline MEMPM to select the optimal feature subset with good stability and automatic balance between prediction accuracy and the size of feature subset. The experiments against feature selection with SVM and MPM show the advantages of the proposed MEMPM formulation in stability and automatic balance between the feature subset size and the prediction accuracy.

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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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Identification of Phage Virion Proteins by Using the g-gap Tripeptide Composition

Letters in Organic Chemistry ◽

10.2174/1570178615666180910112813 ◽

2019 ◽

Vol 16 (4) ◽

pp. 332-339

Author(s):

Liangwei Yang ◽

Hui Gao ◽

Zhen Liu ◽

Lixia Tang

Keyword(s):

Information Gain ◽

State Of The Art ◽

Support Vector ◽

Feature Subset ◽

Bacterial Cells ◽

Accurate Identification ◽

Incremental Feature Selection ◽

Optimal Feature Subset ◽

Phage Proteins ◽

Optimal Feature

Phages are widely distributed in locations populated by bacterial hosts. Phage proteins can be divided into two main categories, that is, virion and non-virion proteins with different functions. In practice, people mainly use phage virion proteins to clarify the lysis mechanism of bacterial cells and develop new antibacterial drugs. Accurate identification of phage virion proteins is therefore essential to understanding the phage lysis mechanism. Although some computational methods have been focused on identifying virion proteins, the result is not satisfying which gives more room for improvement. In this study, a new sequence-based method was proposed to identify phage virion proteins using g-gap tripeptide composition. In this approach, the protein features were firstly extracted from the ggap tripeptide composition. Subsequently, we obtained an optimal feature subset by performing incremental feature selection (IFS) with information gain. Finally, the support vector machine (SVM) was used as the classifier to discriminate virion proteins from non-virion proteins. In 10-fold crossvalidation test, our proposed method achieved an accuracy of 97.40% with AUC of 0.9958, which outperforms state-of-the-art methods. The result reveals that our proposed method could be a promising method in the work of phage virion proteins identification.

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Abstract 473: Identification of Apolipoproteins Using Feature Selection Technique

Arteriosclerosis Thrombosis and Vascular Biology ◽

10.1161/atvb.36.suppl_1.473 ◽

2016 ◽

Vol 36 (suppl_1) ◽

Author(s):

Hua Tang ◽

Hao Lin

Keyword(s):

Support Vector Machine ◽

Cross Validation ◽

Support Vector ◽

Feature Subset ◽

Risk Markers ◽

Dipeptide Composition ◽

Accurate Identification ◽

Feature Selection Technique ◽

Physiological Importance ◽

Fold Cross Validation

Objective: Apolipoproteins are of great physiological importance and are associated with different diseases such as dyslipidemia, thrombogenesis and angiocardiopathy. Apolipoproteins have therefore emerged as key risk markers and important research targets yet the types of apolipoproteins has not been fully elucidated. Accurate identification of the apoliproproteins is very crucial to the comprehension of cardiovascular diseases and drug design. The aim of this study is to develop a powerful model to precisely identify apolipoproteins. Approach and Results: We manually collected a non-redundant dataset of 53 apoliproproteins and 136 non-apoliproproteins with the sequence identify of less than 40% from UniProt. After formulating the protein sequence samples with g -gap dipeptide composition (here g =1~10), the analysis of various (ANOVA) was adopted to find out the best feature subset which can achieve the best accuracy. Support Vector Machine (SVM) was then used to perform classification. The predictive model was evaluated using a five-fold cross-validation which yielded a sensitivity of 96.2%, a specificity of 99.3%, and an accuracy of 98.4%. The study indicated that the proposed method could be a feasible means of conducting preliminary analyses of apoliproproteins. Conclusion: We demonstrated that apoliproproteins can be predicted from their primary sequences. Also we discovered the special dipeptide distribution in apoliproproteins. These findings open new perspectives to improve apoliproproteins prediction by considering the specific dipeptides. We expect that these findings will help to improve drug development in anti-angiocardiopathy disease. Key words: Apoliproproteins Angiocardiopathy Support Vector Machine

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A Measure Optimized Cost-Sensitive Learning Framework for Imbalanced Data Classification

Advances in Data Mining and Database Management - Biologically-Inspired Techniques for Knowledge Discovery and Data Mining ◽

10.4018/978-1-4666-6078-6.ch003 ◽

2014 ◽

pp. 48-75 ◽

Cited By ~ 2

Author(s):

Peng Cao ◽

Osmar Zaiane ◽

Dazhe Zhao

Keyword(s):

Real World ◽

Class Imbalance ◽

Imbalanced Data ◽

Support Vector ◽

Feature Subset ◽

Cost Sensitive Learning ◽

Intrinsic Parameters ◽

Real World Problem ◽

Benchmark Datasets ◽

Feed Forward Neural Networks

Class imbalance is one of the challenging problems for machine-learning in many real-world applications. Many methods have been proposed to address and attempt to solve the problem, including sampling and cost-sensitive learning. The latter has attracted significant attention in recent years to solve the problem, but it is difficult to determine the precise misclassification costs in practice. There are also other factors that influence the performance of the classification including the input feature subset and the intrinsic parameters of the classifier. This chapter presents an effective wrapper framework incorporating the evaluation measure (AUC and G-mean) into the objective function of cost sensitive learning directly to improve the performance of classification by simultaneously optimizing the best pair of feature subset, intrinsic parameters, and misclassification cost parameter. The optimization is based on Particle Swarm Optimization (PSO). The authors use two different common methods, support vector machine and feed forward neural networks, to evaluate the proposed framework. Experimental results on various standard benchmark datasets with different ratios of imbalance and a real-world problem show that the proposed method is effective in comparison with commonly used sampling techniques.

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Identification of Bacterial Cell Wall Lyases via Pseudo Amino Acid Composition

BioMed Research International ◽

10.1155/2016/1654623 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 44

Author(s):

Xin-Xin Chen ◽

Hua Tang ◽

Wen-Chao Li ◽

Hao Wu ◽

Wei Chen ◽

...

Keyword(s):

Cell Wall ◽

Bacterial Cell ◽

Pathogenic Bacteria ◽

Antimicrobial Agents ◽

Cell Structure ◽

Bacterial Cell Wall ◽

Computational Method ◽

Support Vector ◽

Feature Subset ◽

Feature Selection Technique

Owing to the abuse of antibiotics, drug resistance of pathogenic bacteria becomes more and more serious. Therefore, it is interesting to develop a more reasonable way to solve this issue. Because they can destroy the bacterial cell structure and then kill the infectious bacterium, the bacterial cell wall lyases are suitable candidates of antibacteria sources. Thus, it is urgent to develop an accurate and efficient computational method to predict the lyases. Based on the consideration, in this paper, a set of objective and rigorous data was collected by searching through the Universal Protein Resource (the UniProt database), whereafter a feature selection technique based on the analysis of variance (ANOVA) was used to acquire optimal feature subset. Finally, the support vector machine (SVM) was used to perform prediction. The jackknife cross-validated results showed that the optimal average accuracy of 84.82% was achieved with the sensitivity of 76.47% and the specificity of 93.16%. For the convenience of other scholars, we built a free online server calledLypred. We believe thatLypredwill become a practical tool for the research of cell wall lyases and development of antimicrobial agents.

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