Pseudo K-tuple Nucleotide Composition Research Articles

Unified mRNA Subcellular Localization Predictor based on machine learning techniques

BackgroundThe mRNA subcellular localization bears substantial impact in the regulation of gene expression, cellular migration, and adaptation. However, the methods employed for experimental determination of this localization are arduous, time-intensive, and come with a high cost.MethodsIn this research article, we tackle the essential challenge of predicting the subcellular location of messenger RNAs (mRNAs) through Unified mRNA Subcellular Localization Predictor (UMSLP), a machine learning (ML) based approach. We embrace an in silico strategy that incorporate four distinct feature sets: kmer, pseudo k-tuple nucleotide composition, nucleotide physicochemical attributes, and the 3D sequence depiction achieved via Z-curve transformation for predicting subcellular localization in benchmark dataset across five distinct subcellular locales, encompassing nucleus, cytoplasm, extracellular region (ExR), mitochondria, and endoplasmic reticulum (ER).ResultsThe proposed ML model UMSLP attains cutting-edge outcomes in predicting mRNA subcellular localization. On independent testing dataset, UMSLP ahcieved over 87% precision, 94% specificity, and 94% accuracy. Compared to other existing tools, UMSLP outperformed mRNALocator, mRNALoc, and SubLocEP by 11%, 21%, and 32%, respectively on average prediction accuracy for all five locales. SHapley Additive exPlanations analysis highlights the dominance of k-mer features in predicting cytoplasm, nucleus, ER, and ExR localizations, while Z-curve based features play pivotal roles in mitochondria subcellular localization detection.AvailabilityWe have shared datasets, code, Docker API for users in GitHub at: https://github.com/smusleh/UMSLP.

Stacking-ac4C: an ensemble model using mixed features for identifying n4-acetylcytidine in mRNA.

N4-acetylcytidine (ac4C) is a modification of cytidine at the nitrogen-4 position, playing a significant role in the translation process of mRNA. However, the precise mechanism and details of how ac4C modifies translated mRNA remain unclear. Since identifying ac4C sites using conventional experimental methods is both labor-intensive and time-consuming, there is an urgent need for a method that can promptly recognize ac4C sites. In this paper, we propose a comprehensive ensemble learning model, the Stacking-based heterogeneous integrated ac4C model, engineered explicitly to identify ac4C sites. This innovative model integrates three distinct feature extraction methodologies: Kmer, electron-ion interaction pseudo-potential values (PseEIIP), and pseudo-K-tuple nucleotide composition (PseKNC). The model also incorporates the robust Cluster Centroids algorithm to enhance its performance in dealing with imbalanced data and alleviate underfitting issues. Our independent testing experiments indicate that our proposed model improves the Mcc by 15.61% and the ROC by 5.97% compared to existing models. To test our model's adaptability, we also utilized a balanced dataset assembled by the authors of iRNA-ac4C. Our model showed an increase in Sn of 4.1%, an increase in Acc of nearly 1%, and ROC improvement of 0.35% on this balanced dataset. The code for our model is freely accessible at https://github.com/louliliang/ST-ac4C.git, allowing users to quickly build their model without dealing with complicated mathematical equations.

Computational identification of promoters in Klebsiella aerogenes by using support vector machine.

Promoters are the basic functional cis-elements to which RNA polymerase binds to initiate the process of gene transcription. Comprehensive understanding gene expression and regulation depends on the precise identification of promoters, as they are the most important component of gene expression. This study aimed to develop a machine learning-based model to predict promoters in Klebsiella aerogenes (K. aerogenes). In the prediction model, the promoter sequences in K. aerogenes genome were encoded by pseudo k-tuple nucleotide composition (PseKNC) and position-correlation scoring function (PCSF). Numerical features were obtained and then optimized using mRMR by combining with support vector machine (SVM) and 5-fold cross-validation (CV). Subsequently, these optimized features were inputted into SVM-based classifier to discriminate promoter sequences from non-promoter sequences in K. aerogenes. Results of 10-fold CV showed that the model could yield the overall accuracy of 96.0% and the area under the ROC curve (AUC) of 0.990. We hope that this model will provide help for the study of promoter and gene regulation in K. aerogenes.

XGB5hmC: Identifier based on XGB model for RNA 5-hydroxymethylcytosine detection

One of the problems in bioinformatics that artificial intelligence can solve is RNA 5-hydroxymethylcytosine (5hmC) site detection, which has become increasingly important because of its benefits, such as cost savings in labor, materials, and time consumption. To create a reliable identifier, performance results must be as high as possible. In this study, we developed XGB5hmC, a high-performance identifier of RNA 5hmC. We use extreme gradient boosting (XGB) as the best model. In addition, we investigated other models, such as random forest (RF), ada boosting (AB), and gradient boosting (GB). First, IlearnPlus was used to run 15 different machine learning models using 35 different descriptors to select the best descriptors. Then, it was decided that the composition of k-spaced nucleic acid pairs (CKSNAP), pseudo-K-tuple nucleotide composition (PseKNC), and position-specific trinucleotide propensity single strand (PSTNPss) are the best descriptors. Subsequently, the features were combined and reduced in dimension using chi-squared test filtering. Using these filtered features and the XGB model, we obtained better performance than the state-of-the-art methods. The increases in accuracy, sensitivity, specificity, and MCC values were 11.43, 15.82, 8.94, and 24.58%, respectively. This implies that our model improved as a reliable identifier to detect 5hmC. All datasets and complete source codes can be accessed freely at https://github.com/asw1982/XGB5hmC.

MSLP: mRNA subcellular localization predictor based on machine learning techniques

BackgroundSubcellular localization of messenger RNA (mRNAs) plays a pivotal role in the regulation of gene expression, cell migration as well as in cellular adaptation. Experiment techniques for pinpointing the subcellular localization of mRNAs are laborious, time-consuming and expensive. Therefore, in silico approaches for this purpose are attaining great attention in the RNA community.MethodsIn this article, we propose MSLP, a machine learning-based method to predict the subcellular localization of mRNA. We propose a novel combination of four types of features representing k-mer, pseudo k-tuple nucleotide composition (PseKNC), physicochemical properties of nucleotides, and 3D representation of sequences based on Z-curve transformation to feed into machine learning algorithm to predict the subcellular localization of mRNAs.ResultsConsidering the combination of the above-mentioned features, ennsemble-based models achieved state-of-the-art results in mRNA subcellular localization prediction tasks for multiple benchmark datasets. We evaluated the performance of our method in ten subcellular locations, covering cytoplasm, nucleus, endoplasmic reticulum (ER), extracellular region (ExR), mitochondria, cytosol, pseudopodium, posterior, exosome, and the ribosome. Ablation study highlighted k-mer and PseKNC to be more dominant than other features for predicting cytoplasm, nucleus, and ER localizations. On the other hand, physicochemical properties and Z-curve based features contributed the most to ExR and mitochondria detection. SHAP-based analysis revealed the relative importance of features to provide better insights into the proposed approach.AvailabilityWe have implemented a Docker container and API for end users to run their sequences on our model. Datasets, the code of API and the Docker are shared for the community in GitHub at: https://github.com/smusleh/MSLP.

Deep intelligent predictive model for the identification of diabetes

<abstract> <p>Diabetes mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Many complications arise if diabetes remains untreated and unidentified. Early prediction of diabetes is the most high-quality way to forestall and manipulate diabetes and its complications. With the rising incidence of diabetes, machine learning and deep learning algorithms have been increasingly used to predict diabetes and its complications due to their capacity to care for massive and complicated facts sets. This research aims to develop an intelligent computational model that can accurately predict the probability of diabetes in patients at an early stage. The proposed predictor employs hybrid pseudo-K-tuple nucleotide composition (PseKNC) for sequence formulation, an unsupervised principal component analysis (PCA) algorithm for discriminant feature selection, and a deep neural network (DNN) as a classifier. The experimental results show that the proposed technique can perform better on benchmark datasets. Furthermore, overall assessment performance compared to existing predictors indicated that our predictor outperformed the cutting-edge predictors using 10-fold cross validation. It is anticipated that the proposed model could be a beneficial tool for diabetes diagnosis and precision medicine.</p> </abstract>

Identification of DNA modification sites based on elastic net and bidirectional gated recurrent unit with convolutional neural network

DNA N4-methylcytosine (4mC) and DNA N6-methyladenine (6mA) are significant epigenetic modifications. 4mC is closely related to the restriction modification system, and 6mA has a hand in the process of various cellular activities. In order to further explore their functional mechanisms and biological significance, and to overcome the bottleneck of narrow coverage in traditional experimental methods, it is needed to propose an efficient prediction method with a wide range of applications. In this work, we develop a prediction method named 4mCi6mA-BGC to predict 4mC sites and 6mA sites. First, we employ binary, K-mer nucleotide frequency (K-mer), pseudo K-tuple nucleotide composition (PseKNC), dinucleotide-based auto covariance (DAC) and monoDiKGap theoretical description (MonoDiKGap) to encode DNA sequences. Then, the elastic net is employed for feature selection, and the optimized feature space is put into a deep learning framework composed of bidirectional gated recurrent unit and convolutional neural network. The benchmark datasets include six datasets, which contain 14 328 4mC sites from different species. The results of 10-fold cross-validation indicate that the prediction accuracy significantly outperforms the existing prediction methods. Meanwhile, use independent datasets Rice and Arabidopsis thaliana to further confirm the predictive ability of 4mCi6mA-BGC. Compared with the existing prediction methods, 4mCi6mA-BGC shows the best prediction performance. These comprehensive results indicate that our method can identify DNA modification sites represented by 4mC and 6mA sites.

In silico screening of ssDNA aptamer against Escherichia coli O157:H7: A machine learning and the Pseudo K-tuple nucleotide composition based approach

This study was planned to in silico screening of ssDNA aptamer against Escherichia coli O157:H7 by combination of machine learning and the PseKNC approach. For this, firstly a total numbers of 47 validated ssDNA aptamers as well as 498 random DNA sequences were considered as positive and negative training data respectively. The sequences then converted to numerical vectors using PseKNC method through Pse-in-one 2.0 web server. After that, the numerical vectors were subjected to classification by the SVM, ANN and RF algorithms available in Orange 3.2.0 software. The performances of the tested models were evaluated using cross-validation, random sampling and ROC curve analyzes. The primary results demonstrated that the ANN and RF algorithms have appropriate performances for the data classification. To improve the performances of mentioned classifiers the positive training data was triplicated and re-training process was also performed. The results confirmed that data size improvement had significant effect on the accuracy of data classification especially about RF model. Subsequently, the RF algorithm with accuracy of 98% was selected for aptamer screening. The thermodynamics details of folding process as well as secondary structures of the screened aptamers were also considered as final evaluations. The results confirmed that the selected aptamers by the proposed method had appropriate structure properties and there is no thermodynamics limit for the aptamers folding.

MRNALocater: Enhance the prediction accuracy of eukaryotic mRNA subcellular localization by using model fusion strategy

The functions of mRNAs are closely correlated with their locations in cells. Knowledge about the subcellular locations of mRNA is helpful to understand their biological functions. In recent years, it has become a hot topic to develop effective computational models to predict eukaryotic mRNA subcellular localizations. However, existing state-of-the-art models still have certain deficiencies in terms of prediction accuracy and generalization ability. Therefore, it is urgent to develop novel methods to accurately predict mRNA subcellular localizations. In this study, a novel method called mRNALocater was proposed to detect the subcellular localization of eukaryotic mRNA by adopting the model fusion strategy. To fully extract information from mRNA sequences, the electron-ion interaction pseudopotential and pseudo k-tuple nucleotide composition were used to encode the sequences. Moreover, the correlation coefficient filtering algorithm and feature forward search technology were used to mine hidden feature information, which guarantees that mRNALocater can be more effectively applied to new sequences. The results based on the independent dataset tests demonstrate that mRNALocater yields promising performances for predicting eukaryotic mRNA subcellular localizations and is a powerful tool in practical applications. A freely available online web server for mRNALocater has been established at http://bio-bigdata.cn/mRNALocater.

IEnhancer-DHF: Identification of Enhancers and Their Strengths Using Optimize Deep Neural Network With Multiple Features Extraction Methods

Enhancers are short DNA regulatory elements which play a vital role in gene expression. Due to their important roles in genomics, several computational models have been proposed in the literature for identification of enhancers and their strengths using traditional machine learning algorithms, however, the proposed models are unable to identify enhancers and their strength with reasonable accuracy because of high non-linearity in DNA sequences. This article proposes a two-level intelligent model based on Deep Neural Network (DNN) along with multiple feature extraction methods. Firstly, the proposed model represents the given DNA sequences into feature vectors using Pseudo K-tuple Nucleotide Composition (PseKNC) and FastText methods. Secondly, the features vectors are fused to make a heterogeneous features vector that considered the local and global correlation amongst the given sequences along with internal structure information. Finally, the heterogeneous feature vector is given to a DNN model to make final predictions. The proposed iEnhancer-DHF is developed using two-layer approach. The first layer predicts whether the given DNA samples are enhancers or non-enhancers whereas the second layer identifies either the enhancers are strong enhancers or weak enhancers. The outcome of the proposed model was rigorously assessed using both training and independent datasets via 10-fold cross validation method. The validation outcome demonstrated that the iEnhancer-DHF model yielded accuracies 86.07% and 69.60% at first layer and second layer respectively utilizing the training dataset. Similarly, the model yielded accuracies 83.21% and 67.54% at first layer and at second layer respectively by using the independent dataset. Additionally, the outcomes of the proposed model was initially compared with widely applied classifiers such as Support Vector Machine, Random Forest and K-nearest Neighbor and subsequently the performance is compared with the existing models using both the training and independent datasets. The comparison results exhibited that the iEnhancer-DHF model performed superior than the recently published models.

Prediction of piRNAs and their function based on discriminative intelligent model using hybrid features into Chou’s PseKNC

Piwi interacting RNA (piRNA) is a recognized group of small non-coding RNA molecules. The piRNA molecules are associated with multiple tumors type diagnosis and drug development. It is also linked to regulate gene expression, suppressing transposon and maintains genome integrity. Due to a vital role of piRNAs in biology, the identification of piRNAs and their function has become an important area of research in computational biology. This paper proposes a robust two-layer predictor called “piRNA (2L)-PseKNC” to improve prediction of piRNAs and their function. The proposed predictor employing hybrid pseudo-K-tuple nucleotide composition (PseKNC) for sequence formulation, unsupervised principal component analysis (PCA) algorithm for discriminant feature selection and deep neural network (DNN) as a classifier. The proposed predictor was designed based on two layers approach. The first layer predicts either the encoded sequence belongs to piRNA or non-piRNA sequence and the second layer predict the selected piRNA sequence is functional piRNA or non-functional piRNA sequence. The overall accuracies of the proposed model using 5-fold cross validation test were 94.73% at first layer and 85.21% at second layer, surpassing the existing predictors with accuracies improvement 7.59% and 2.81% at the first layer and at the second layer respectively.

Some illuminating remarks on molecular genetics and genomics as well as drug development.

Facing the explosive growth of biological sequences unearthed in the post-genomic age, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, but still keep it with considerable sequence-order information or its special pattern. To deal with such a challenging problem, the ideas of "pseudo amino acid components" and "pseudo K-tuple nucleotide composition" have been proposed. The ideas and their approaches have further stimulated the birth for "distorted key theory", "wenxing diagram", and substantially strengthening the power in treating the multi-label systems, as well as the establishment of the famous "5-steps rule". All these logic developments are quite natural that are very useful not only for theoretical scientists but also for experimental scientists in conducting genetics/genomics analysis and drug development. Presented in this review paper are also their future perspectives; i.e., their impacts will become even more significant and propounding.

The Significant and Profound Impacts of Pseudo K-Tuple Nucleotide Composition

In this short review paper, the significant and profound impacts of the “pseudo K-tuple nucleotide composition” have been briefly presented with crystal clear convincingness.

Progresses in Predicting Post-translational Modification

Identification of the sites of post-translational modifications (PTMs) in protein, RNA, and DNA sequences is currently a very hot topic. This is because the information thus obtained is very useful for in-depth understanding the biological processes at the cellular level and for developing effective drugs against major diseases including cancers as well. Although this can be done by means of various experimental techniques, it is both time-consuming and costly to determine the PTM sites purely based on experiments. With the avalanche of biological sequences generated in the post-genomic age, it is highly desired to develop bioinformatics tools for rapidly and effectively identifying the PTM sites. In the last few years, many efforts have been made in this regard, and considerable progresses have been achieved. This review is focused on those prediction methods that have the following two features. (1) They have been developed by strictly observing the 5-steps rule so that they each have a user-friendly web-server for the majority of experimental scientists to easily get their desired data without the need to go through the detailed mathematics involved. (2) Their cornerstones have been based on Pseudo Amino Acid Composition (PseAAC) or Pseudo K-tuple Nucleotide Composition (PseKNC), and hence the prediction quality is generally higher than most of the other PTM prediction methods.

IProEP: A Computational Predictor for Predicting Promoter

Promoter is a fundamental DNA element located around the transcription start site (TSS) and could regulate gene transcription. Promoter recognition is of great significance in determining transcription units, studying gene structure, analyzing gene regulation mechanisms, and annotating gene functional information. Many models have already been proposed to predict promoters. However, the performances of these methods still need to be improved. In this work, we combined pseudo k-tuple nucleotide composition (PseKNC) with position-correlation scoring function (PCSF) to formulate promoter sequences of Homo sapiens (H. sapiens), Drosophila melanogaster (D. melanogaster), Caenorhabditis elegans (C. elegans), Bacillus subtilis (B. subtilis), and Escherichia coli (E. coli). Minimum Redundancy Maximum Relevance (mRMR) algorithm and increment feature selection strategy were then adopted to find out optimal feature subsets. Support vector machine (SVM) was used to distinguish between promoters and non-promoters. In the 10-fold cross-validation test, accuracies of 93.3%, 93.9%, 95.7%, 95.2%, and 93.1% were obtained for H. sapiens, D. melanogaster, C. elegans, B. subtilis, and E. coli, with the areas under receiver operating curves (AUCs) of 0.974, 0.975, 0.981, 0.988, and 0.976, respectively. Comparative results demonstrated that our method outperforms existing methods for identifying promoters. An online web server was established that can be freely accessed (http://lin-group.cn/server/iProEP/).

IEsGene-ZCPseKNC: Identify Essential Genes Based on Z Curve Pseudo $k$ -Tuple Nucleotide Composition

As an important technique for synthetic biology, computational identification of essential genes will facilitate the development of the related fields, such as genome analysis, drug design, etc. The identification of prokaryotic essential genes has been extensively studied, especially focusing on the essential genes in bacteria. Archaea as an important domain in prokaryote exists high variance of genome sizes. However, there is no predictor available for predicting essential genes in archaea. In this paper, we developed the first computational predictor for predicting essential genes in archaea called iEsGene-ZCPseKNC. With the purpose of capturing sequence patterns of the essential genes, a new feature called Z curve pseudo $k$ -tuple nucleotide composition (ZCPseKNC) was proposed, which incorporates the advantages of both Z curve and pseudo $k$ -tuple nucleotide composition (PseKNC). In order to overcome the problems caused by the imbalanced training set, the SMOTE algorithm was employed to further improve the predictive performance of iEsGene-ZCPseKNC. Evaluated by the rigorous jackknife test on a benchmark dataset, the experimental results showed that the iEsGene-ZCPseKNC predictor outperformed the predictors based on Z curve and PseKNC, indicating that iEsGene-ZCPseKNC is useful for identification of essential genes in archaea, and would be a powerful tool for genome analysis. A user friendly web server of the iEsGene-ZCPseKNC predictor was established and can be easily accessed from http://bliulab.net/iEsGene-ZCPseKNC/ .

IPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition

The promoter is a regulatory DNA region about 81–1000 base pairs long, usually located near the transcription start site (TSS) along upstream of a given gene. By combining a certain protein called transcription factor, the promoter provides the starting point for regulated gene transcription, and hence plays a vitally important role in gene transcriptional regulation. With explosive growth of DNA sequences in the post-genomic age, it has become an urgent challenge to develop computational method for effectively identifying promoters because the information thus obtained is very useful for both basic research and drug development. Although some prediction methods were developed in this regard, most of them were limited at merely identifying whether a query DNA sequence being of a promoter or not. However, based on their strength-distinct levels for transcriptional activation and expression, promoter should be divided into two categories: strong and weak types. Here a new two-layer predictor, called “iPSW(2L)-PseKNC”, was developed by fusing the physicochemical properties of nucleotides and their nucleotide density into PseKNC (pseudo K-tuple nucleotide composition). Its 1st-layer serves to predict whether a query DNA sequence sample is of promoter or not, while its 2nd-layer is able to predict the strength of promoters. It has been observed through rigorous cross-validations that the 1st-layer sub-predictor is remarkably superior to the existing state-of-the-art predictors in identifying the promoters and non-promoters, and that the 2nd-layer sub-predictor can do what is beyond the reach of the existing predictors. Moreover, the web-server for iPSW(2L)-PseKNC has been established at http://www.jci-bioinfo.cn/iPSW(2L)-PseKNC, by which the majority of experimental scientists can easily get the results they need.

ITerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators.

Transcription termination is an important regulatory step of gene expression. If there is no terminator in gene, transcription could not stop, which will result in abnormal gene expression. Detecting such terminators can determine the operon structure in bacterial organisms and improve genome annotation. Thus, accurate identification of transcriptional terminators is essential and extremely important in the research of transcription regulations. In this study, we developed a new predictor called 'iTerm-PseKNC' based on support vector machine to identify transcription terminators. The binomial distribution approach was used to pick out the optimal feature subset derived from pseudo k-tuple nucleotide composition (PseKNC). The 5-fold cross-validation test results showed that our proposed method achieved an accuracy of 95%. To further evaluate the generalization ability of 'iTerm-PseKNC', the model was examined on independent datasets which are experimentally confirmed Rho-independent terminators in Escherichia coli and Bacillus subtilis genomes. As a result, all the terminators in E. coli and 87.5% of the terminators in B. subtilis were correctly identified, suggesting that the proposed model could become a powerful tool for bacterial terminator recognition. For the convenience of most of wet-experimental researchers, the web-server for 'iTerm-PseKNC' was established at http://lin-group.cn/server/iTerm-PseKNC/, by which users can easily obtain their desired result without the need to go through the detailed mathematical equations involved.

ILoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC.

Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. They have important functions in cell development and metabolism, such as genetic markers, genome rearrangements, chromatin modifications, cell cycle regulation, transcription and translation. Their functions are generally closely related to their localization in the cell. Therefore, knowledge about their subcellular locations can provide very useful clues or preliminary insight into their biological functions. Although biochemical experiments could determine the localization of lncRNAs in a cell, they are both time-consuming and expensive. Therefore, it is highly desirable to develop bioinformatics tools for fast and effective identification of their subcellular locations. We developed a sequence-based bioinformatics tool called 'iLoc-lncRNA' to predict the subcellular locations of LncRNAs by incorporating the 8-tuple nucleotide features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach. Rigorous jackknife tests have shown that the overall accuracy achieved by the new predictor on a stringent benchmark dataset is 86.72%, which is over 20% higher than that by the existing state-of-the-art predictor evaluated on the same tests. A user-friendly webserver has been established at http://lin-group.cn/server/iLoc-LncRNA, by which users can easily obtain their desired results. Supplementary data are available at Bioinformatics online.

IEnhancer-EL: identifying enhancers and their strength with ensemble learning approach.

Identification of enhancers and their strength is important because they play a critical role in controlling gene expression. Although some bioinformatics tools were developed, they are limited in discriminating enhancers from non-enhancers only. Recently, a two-layer predictor called 'iEnhancer-2L' was developed that can be used to predict the enhancer's strength as well. However, its prediction quality needs further improvement to enhance the practical application value. A new predictor called 'iEnhancer-EL' was proposed that contains two layer predictors: the first one (for identifying enhancers) is formed by fusing an array of six key individual classifiers, and the second one (for their strength) formed by fusing an array of ten key individual classifiers. All these key classifiers were selected from 171 elementary classifiers formed by SVM (Support Vector Machine) based on kmer, subsequence profile and PseKNC (Pseudo K-tuple Nucleotide Composition), respectively. Rigorous cross-validations have indicated that the proposed predictor is remarkably superior to the existing state-of-the-art one in this area. A web server for the iEnhancer-EL has been established at http://bioinformatics.hitsz.edu.cn/iEnhancer-EL/, by which users can easily get their desired results without the need to go through the mathematical details. Supplementary data are available at Bioinformatics online.