iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding

Le, Nguyen Quoc Khanh; Yapp, Edward Kien Yee; Ho, Quang‐Thai; Nagasundaram, Nagarajan; Ou, Yu‐Yen; Yeh, Hui‐Yuan

doi:10.1016/j.ab.2019.02.017

Cited by 121 publications

(88 citation statements)

References 96 publications

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“…Because of these advantages, SVM can be well-applied to pattern recognition, time series prediction, and regression estimation, among others. It is also widely used in many fields, such as handwritten character recognition, text classification, image classification, and recognition [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

et al. 2019

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The outcomes of hypertension refer to the death or serious complications (such as myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes of hypertension are very concerning for patients and doctors, and are ideally avoided. However, there is no satisfactory method for predicting the outcomes of hypertension. Therefore, this paper proposes a prediction method for outcomes based on physical examination indicators of hypertension patients. In this work, we divide the patients’ outcome prediction into two steps. The first step is to extract the key features from the patients’ many physical examination indicators. The second step is to use the key features extracted from the first step to predict the patients’ outcomes. To this end, we propose a model combining recursive feature elimination with a cross-validation method and classification algorithm. In the first step, we use the recursive feature elimination algorithm to rank the importance of all features, and then extract the optimal features subset using cross-validation. In the second step, we use four classification algorithms (support vector machine (SVM), C4.5 decision tree, random forest (RF), and extreme gradient boosting (XGBoost)) to accurately predict patient outcomes by using their optimal features subset. The selected model prediction performance evaluation metrics are accuracy, F1 measure, and area under receiver operating characteristic curve. The 10-fold cross-validation shows that C4.5, RF, and XGBoost can achieve very good prediction results with a small number of features, and the classifier after recursive feature elimination with cross-validation feature selection has better prediction performance. Among the four classifiers, XGBoost has the best prediction performance, and its accuracy, F1, and area under receiver operating characteristic curve (AUC) values are 94.36%, 0.875, and 0.927, respectively, using the optimal features subset. This article’s prediction of hypertension outcomes contributes to the in-depth study of hypertension complications and has strong practical significance.

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Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

et al. 2019

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“…Whereas EnhancerPred [27] achieved 80.82% accuracy and used SVMs which produced slightly better results in predicting strong enhancers with 62.06% accuracy. Similarly, iEnhancer-2L-Hybrid [71]and iEnhancer-5Step [29] improved the accuracy results with their prediction model and acquired 77.86% and 82.3% accuracies respectively with identifying the strong enhancers with 65.83% and 68.1% accuracies respectively. In contrast, 91.68% and 84.53% accuracy was achieved in predicting enhancers and their strength respectively by the currently proposed method after utilizing obscure features from statistical moments and random forest classifications using 5-Fold cross validation tests (see Table 5 and Figure.3 for ROC).…”

Section: Resultsmentioning

confidence: 84%

“…Furthermore, the independent dataset used by a recent study [29] was utilized to enhance the effectiveness and performance of the proposed model. The independent dataset included 400 DNA enhancer sequences from which 200 (100 strong and 100 weak enhancers) are enhancers and 200 are non-enhancers.…”

Section: Benchmark Datasetmentioning

confidence: 99%

“…Furthermore, many other methods, such as EnhancerPred [27] and EnhancerPred_2.0 [28], were introduced to improve the performance by incorporating other features based on DNA sequences. iEnhancer-5Step [29] was recently developed using the hidden information of DNA sequences infused with Support Vector Machine (SVM) based predictions. However, improvement in the performance of the aforementioned predictors is still required.…”

Section: Introductionmentioning

confidence: 99%

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EnhancerP-2L: A Gene regulatory site identification tool for DNA enhancer region using CREs motifs

Ah¹,

Alkhalaf

Iqbal

et al. 2020

Preprint

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Enhancers are DNA fragments that do not encode RNA molecules and proteins, but they act critically in the production of RNAs and proteins by controlling gene expression. Prediction of enhancers and their strength plays significant role in regulating gene expression. Prediction of enhancer regions, in sequences of DNA, is considered a difficult task due to the fact that they are not close to the target gene, have less common motifs and are mostly tissue/cell specific. In recent past, several bioinformatics tools were developed to discriminate enhancers from other regulatory elements and to identify their strengths as well.However the need for improvement in the quality of its prediction method requires enhancements in its application value practically. In this study, we proposed a new method that builds on nucleotide composition and statistical moment based features to distinguish between enhancers and non-enhancers and additionally determine their strength. Our proposed method achieved accuracy better than current state-of-the-art methods using 5-fold and 10-fold cross-validation. The outcomes from our proposed method suggest that the use of statistical moments based features could bear more efficient and effective results. For the accessibility of the scientific community, we have developed a user-friendly web server for EnhancerP-2L which will increase the impact of bioinformatics on medicinal chemistry and drive medical science into an unprecedented resolution. Web server is freely accessible at http://www.biopred.org/enpred.

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“…Many studies in bioinformatics show that [28][29][30][31][32][33][34][35][36][37] to build a helpful statistical predictor from primary sequences to help biologists solve a certain problem, researchers ought to obey the rules from 5-step rule which is restated here for clarity: (1) the accurate procedure to compose training and independent dataset to build and evaluate the predictive model; (2) the way to represent amino acid chain samples in mathematical forms that can genuinely mirror their characteristic interconnection with the predicted target; (3) the best approach to present or build up an effective and robust predictive algorithm; (4) the method to correctly conduct cross-validation tests to justly evaluate the predictor's unseen accuracy; (5) the way to set up a convenient and accessible webserver for the predictor that is helpful to general users. Herein, we would strictly follow all the above-mentioned steps.…”

Section: Introductionmentioning

confidence: 99%

TNFPred: Identifying tumor necrosis factors using hybrid features based on word embeddings

Nguyen

et al. 2019

Preprint

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BackgroundCytokines are a class of small proteins that act as chemical messengers and play a significant role in essential cellular processes including immunity regulation, hematopoiesis, and inflammation.As one important family of cytokines, tumor necrosis factors have association with the regulation of a various biological processes such as proliferation and differentiation of cells, apoptosis, lipid metabolism, and coagulation. The implication of these cytokines can also be seen in various diseases such as insulin resistance, autoimmune diseases, and cancer. Considering the interdependence between this kind of cytokine and others, classifying tumor necrosis factors from other cytokines is a challenge for biological scientists. In this research, we employed a word embedding technique to create hybrid features which was proved to efficiently identify tumor necrosis factors given cytokine sequences. We segmented each protein sequence into protein words and created corresponding word embedding for each word. Then, word embedding-based vector for each sequence was created and input into machine learning classification models. When extracting feature sets, we not only diversified segmentation sizes of protein sequence but also conducted different combinations among split grams to find the best features which generated the optimal prediction. Furthermore, our methodology follows Chou's 5-step rules to build a reliable classification tool. ResultsWith our proposed hybrid features, prediction models obtain more promising performance compared to seven prominent sequenced-based feature kinds. Results from 10 independent runs on the surveyed dataset show that on an average, our optimal models obtain an area under the curve of 0.984 and 0.998 on 5-fold cross-validation and independent test, respectively. ConclusionsThese results show that biologists can use our model to identify tumor necrosis factors from other cytokines efficiently. Moreover, this study proves that natural language processing techniques can be applied reasonably to help biologists solve bioinformatics problems efficiently.

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iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding

Cited by 121 publications

References 96 publications

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

EnhancerP-2L: A Gene regulatory site identification tool for DNA enhancer region using CREs motifs

TNFPred: Identifying tumor necrosis factors using hybrid features based on word embeddings

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