Predicting LncRNA Subcellular Localization Using Unbalanced Pseudo-k Nucleotide Compositions

2020 ◽  
Vol 15 (6) ◽  
pp. 554-562
Author(s):  
Xiao-Fei Yang ◽  
Yuan-Ke Zhou ◽  
Lin Zhang ◽  
Yang Gao ◽  
Pu-Feng Du
Keyword(s):  
Subcellular Localization ◽  
Regulation Of Gene Expression ◽  
Nucleotide Composition ◽  
Support Vector ◽  
Sequence Information ◽  
Feature Subset ◽  
Feature Selection Technique ◽  
Optimal Feature Subset ◽  
Leave One Out ◽  
Correlated Factors

Background: Long non-coding RNAs (lncRNAs) are transcripts with a length more than 200 nucleotides, functioning in the regulation of gene expression. More evidence has shown that the biological functions of lncRNAs are intimately related to their subcellular localizations. Therefore, it is very important to confirm the lncRNA subcellular localization. Methods: In this paper, we proposed a novel method to predict the subcellular localization of lncRNAs. To more comprehensively utilize lncRNA sequence information, we exploited both kmer nucleotide composition and sequence order correlated factors of lncRNA to formulate lncRNA sequences. Meanwhile, a feature selection technique which was based on the Analysis Of Variance (ANOVA) was applied to obtain the optimal feature subset. Finally, we used the support vector machine (SVM) to perform the prediction. Results: The AUC value of the proposed method can reach 0.9695, which indicated the proposed predictor is an efficient and reliable tool for determining lncRNA subcellular localization. Furthermore, the predictor can reach the maximum overall accuracy of 90.37% in leave-one-out cross validation, which clearly outperforms the existing state-of- the-art method. Conclusion: It is demonstrated that the proposed predictor is feasible and powerful for the prediction of lncRNA subcellular. To facilitate subsequent genetic sequence research, we shared the source code at https://github.com/NicoleYXF/lncRNA.

scholarly journals MpsLDA-ProSVM: predicting multi-label protein subcellular localization by wMLDAe dimensionality reduction and ProSVM classifier

2020 ◽  
Author(s):  
Qi Zhang ◽  
Shan Li ◽  
Bin Yu ◽  
Yang Li ◽  
Yandan Zhang ◽  
...  
Keyword(s):  
Subcellular Localization ◽  
Nearest Neighbor ◽  
Chemical Information ◽  
Sequence Information ◽  
Feature Subset ◽  
Protein Subcellular Localization ◽  
K Nearest Neighbor ◽  
Entropy Weight ◽  
Linear Discriminant ◽  
Optimal Feature Subset

ABSTRACTProteins play a significant part in life processes such as cell growth, development, and reproduction. Exploring protein subcellular localization (SCL) is a direct way to better understand the function of proteins in cells. Studies have found that more and more proteins belong to multiple subcellular locations, and these proteins are called multi-label proteins. They not only play a key role in cell life activities, but also play an indispensable role in medicine and drug development. This article first presents a new prediction model, MpsLDA-ProSVM, to predict the SCL of multi-label proteins. Firstly, the physical and chemical information, evolution information, sequence information and annotation information of protein sequences are fused. Then, for the first time, use a weighted multi-label linear discriminant analysis framework based on entropy weight form (wMLDAe) to refine and purify features, reduce the difficulty of learning. Finally, input the optimal feature subset into the multi-label learning with label-specific features (LIFT) and multi-label k-nearest neighbor (ML-KNN) algorithms to obtain a synthetic ranking of relevant labels, and then use Prediction and Relevance Ordering based SVM (ProSVM) classifier to predict the SCLs. This method can rank and classify related tags at the same time, which greatly improves the efficiency of the model. Tested by jackknife method, the overall actual accuracy (OAA) on virus, plant, Gram-positive bacteria and Gram-negative bacteria datasets are 98.06%, 98.97%, 99.81% and 98.49%, which are 0.56%-9.16%, 5.37%-30.87%, 3.51%-6.91% and 3.99%-8.59% higher than other advanced methods respectively. The source codes and datasets are available at https://github.com/QUST-AIBBDRC/MpsLDA-ProSVM/.

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

Genes ◽  
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.

Abstract 473: Identification of Apolipoproteins Using Feature Selection Technique

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

FEATURE SELECTION BASED ON MINIMUM ERROR MINIMAX PROBABILITY MACHINE

2007 ◽  
Vol 21 (08) ◽  
pp. 1279-1292 ◽  
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.

scholarly journals Identification of Bacterial Cell Wall Lyases via Pseudo Amino Acid Composition

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
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.

scholarly journals A Parallel Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Zhi Chen ◽  
Tao Lin ◽  
Ningjiu Tang ◽  
Xin Xia
Keyword(s):  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Coarse Grained ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Subset ◽  
Parallel Genetic Algorithm ◽  
Support Vectors ◽  
Optimal Feature Subset ◽  
Optimal Feature

The extensive applications of support vector machines (SVMs) require efficient method of constructing a SVM classifier with high classification ability. The performance of SVM crucially depends on whether optimal feature subset and parameter of SVM can be efficiently obtained. In this paper, a coarse-grained parallel genetic algorithm (CGPGA) is used to simultaneously optimize the feature subset and parameters for SVM. The distributed topology and migration policy of CGPGA can help find optimal feature subset and parameters for SVM in significantly shorter time, so as to increase the quality of solution found. In addition, a new fitness function, which combines the classification accuracy obtained from bootstrap method, the number of chosen features, and the number of support vectors, is proposed to lead the search of CGPGA to the direction of optimal generalization error. Experiment results on 12 benchmark datasets show that our proposed approach outperforms genetic algorithm (GA) based method and grid search method in terms of classification accuracy, number of chosen features, number of support vectors, and running time.

scholarly journals Insect Mass Estimation Based on Radar Cross Section Parameters and Support Vector Regression Algorithm

2020 ◽  
Vol 12 (11) ◽  
pp. 1903
Author(s):  
Cheng Hu ◽  
Shaoyang Kong ◽  
Rui Wang ◽  
Fan Zhang ◽  
Lianjun Wang
Keyword(s):  
Support Vector Regression ◽  
Cross Section ◽  
Radar Cross Section ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Feature Subset ◽  
Mass Estimation ◽  
Optimal Feature Subset ◽  
Optimal Feature

Radar cross section (RCS) parameters of insect targets contain information related to their morphological parameters, which are helpful for the identification of migratory insects. Several morphological parameter estimation methods have been presented. However, most of these estimations are performed based on polynomial fitting methods, using only one or two parameters, which may limit the estimation accuracy. In this paper, a new insect mass estimation method is proposed based on support vector regression (SVR). Several RCS parameters were extracted for the estimation of insect mass. Support vector regression based on recursive feature elimination (SVRRFE) was used to obtain the optimal feature subset. Specifically, a dataset including 367 specimens was included to evaluate the performance of the proposed method. Fifteen features were extracted and ranked. The optimal feature subset contained six features and the optimal mass estimation accuracy was 78%. Additionally, traditional insect mass estimation methods were analyzed for comparison. The results prove that the proposed method is more effective and accurate for insect mass estimation. It needs to be emphasized that the poor number of experimental insects available may limit the further improvement of estimation accuracy.

scholarly journals Text Classification of Cornell Movie Data using Data Mining with Feature Selection

2019 ◽  
Vol 9 (2) ◽  
pp. 2950-2955
Keyword(s):  
Feature Selection ◽  
Text Mining ◽  
Text Classification ◽  
Feature Subset Selection ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Subset ◽  
Chi Square ◽  
Feature Selection Technique ◽  
Data Set

Text Classification is branch of text mining through which we can analyze the sentiment of the movie data. In this research paper we have applied different preprocessing techniques to reduce the features from cornell movie data set. We have also applied the Correlation-based feature subset selection and chi-square feature selection technique for gathering most valuable words of each category in text mining processes. The new cornell movie data set formed after applying the preprocessing steps and feature selection techniques. We have classified the cornell movie data as positive or negative using various classifiers like Support Vector Machine (SVM), Multilayer Perceptron (MLP), Naive Bayes (NB), Bays Net (BN) and Random Forest (RF) classifier. We have also compared the classification accuracy among classifiers and achieved better accuracy i. e. 87% in case of SVM classifier with reduced number of features. The suggested classifier can be useful in opinion of movie review, analysis of any blog and documents etc.

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

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.

Highlighted Document Image Classification

2021 ◽  
Vol 2021 (29) ◽  
pp. 154-159
Author(s):  
Yafei Mao ◽  
Yufang Sun ◽  
Peter Bauer ◽  
Todd Harris ◽  
Mark Shaw ◽  
...  
Keyword(s):  
Image Classification ◽  
Cross Validation ◽  
Document Image ◽  
Classification Model ◽  
Support Vector ◽  
Feature Subset ◽  
Color Distribution ◽  
Low Level ◽  
Color Features ◽  
Leave One Out

There are many existing document image classification researches, but most of them are not designed for use in constrained computer resources, like printers, or focused on documents with highlighter pen marks. To enable printers to better discriminate highlighted documents, we designed a set of features in CIE Lch(a* b*) space to use along with the support vector machine. The features include two gamut-based features and six low-level color features. By first identifying the highlight pixels, and then computing the distance from the highlight pixels to the boundary of the printer gamut, the gamut-based features can be obtained. The low-level color features are built upon the color distribution information of the image blocks. The best feature subset of the existing and new features is constructed by sequential forward floating selection (SFFS) feature selection. Leave-one-out cross-validation is performed on a dataset with 400 document images to evaluate the effectiveness of the classification model. The cross-validation results indicate significant improvements over the baseline highlighted document classification model.

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