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 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/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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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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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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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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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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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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Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods

BioMed Research International ◽

10.1155/2015/561547 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 7

Author(s):

Yan Liu ◽

Wenxiang Gu ◽

Wenyi Zhang ◽

Jianan Wang

Keyword(s):

Support Vector Machine ◽

Amino Acid ◽

Prediction Accuracy ◽

Support Vector Machine Classifier ◽

Biological Function ◽

Reducing Sugar ◽

Computational Method ◽

Protein Glycation ◽

Feature Analysis ◽

Support Vector

Glycation is a nonenzymatic process in which proteins react with reducing sugar molecules. The identification of glycation sites in protein may provide guidelines to understand the biological function of protein glycation. In this study, we developed a computational method to predict protein glycation sites by using the support vector machine classifier. The experimental results showed that the prediction accuracy was 85.51% and an overall MCC was 0.70. Feature analysis indicated that the composition ofk-spaced amino acid pairs feature contributed the most for glycation sites prediction.

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Colorimetric detection on paper analytical device using machine learning

10.33774/chemrxiv-2021-0zbwm ◽

2021 ◽

Author(s):

Bidur Khanal ◽

Pravin Pokhrel ◽

Bishesh Khanal ◽

Basant Giri

Keyword(s):

Machine Learning ◽

Enzyme Inhibition ◽

Prediction Accuracy ◽

Low Cost ◽

Colorimetric Detection ◽

Field Testing ◽

Support Vector ◽

Ann Model ◽

Fine Grained ◽

Imaging Conditions

Paper-based analytical devices (PADs) employing colorimetric detection and smartphone images have gained wider acceptance in a variety of measurement applications. The PADs are primarily meant to be used in field settings where assay and imaging conditions greatly vary resulting in less accurate results. Recently, machine learning (ML) assisted models have been used in image analysis. We evaluated a combinations of four ML models - logistic regression, support vector machine, random forest, and artificial neural network, and three image color spaces - RGB, HSV, and LAB for their ability to accurately predict analyte concentrations. We used images of PADs taken at varying lighting conditions, with different cameras, and users for food color and enzyme inhibition assays to create training and test datasets. Prediction accuracy was higher for food color than enzyme inhibition assays in most of the ML model and colorspace combinations. All models better predicted coarse level classification than fine grained concentration labels. ML models using sample color along with a reference color increased the models’ ability in predicting the result in which the reference color may have partially factored out the variation in ambient assay and imaging conditions. The best concentration label prediction accuracy obtained for food color was 0.966 when using ANN model and LAB colorspace. The accuracy for enzyme inhibition assay was 0.908 when using SVM model and LAB colorspace. Appropriate model and colorspace combinations can be useful to analyze large numbers of samples on PADs as a powerful low-cost quick field-testing tool.

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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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