iProEP: A Computational Predictor for Predicting Promoter

Lai, Hoang M.; Zhang, Zhao-Yue; Su, Zhixun; Su, Wei; Ding, Hui; Chen, Wei; Lin, Hao

doi:10.1016/j.omtn.2019.05.028

Cited by 139 publications

(79 citation statements)

References 77 publications

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“…The K-mer feature extraction strategy refers to calculating the frequency of the unit in the entire sequence with k adjacent nucleotides as a unit [ 30 , 31 ]. This paper uses 1-mer, 2-mer, 3-mer, and 4-mer feature extraction methods, which are stated by the following formulas:

…”

Section: Methodsmentioning

confidence: 99%

Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network

et al. 2020

Computational and Mathematical Methods in Medicine

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Enhancers are noncoding fragments in DNA sequences, which play an important role in gene transcription and translation. However, due to their high free scattering and positional variability, the identification and classification of enhancers have a higher level of complexity than those of coding genes. In order to solve this problem, many computer studies have been carried out in this field, but there are still some deficiencies in these prediction models. In this paper, we use various feature extraction strategies, dimension reduction technology, and a comprehensive application of machine model and recurrent neural network model to achieve an accurate prediction of enhancer identification and classification with the accuracy of was 76.7% and 84.9%, respectively. The model proposed in this paper is superior to the previous methods in performance index or feature dimension, which provides inspiration for the prediction of enhancers by computer technology in the future.

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…”

Section: Methodsmentioning

confidence: 99%

Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network

et al. 2020

Computational and Mathematical Methods in Medicine

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“…supervised learning; its decision boundary is the maximummargin hyperplane required to solve the learning sample. SVM has been widely used in a variety of fields (Xiong et al, 2012;Ding et al, 2017;Yu et al, 2017b;Fu et al, 2018;Fang et al, 2019;Lai et al, 2019;Meng et al, 2019;Shen et al, 2019;Tang et al, 2019b;Zhang et al, 2019;Zhu et al, 2019). Here, it was used for modeling comparisons.…”

Section: Algorithmmentioning

confidence: 99%

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

Zhang

Ding

et al. 2020

Front. Bioeng. Biotechnol.

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One of the ubiquitous chemical modifications in RNA, pseudouridine modification is crucial for various cellular biological and physiological processes. To gain more insight into the functional mechanisms involved, it is of fundamental importance to precisely identify pseudouridine sites in RNA. Several useful machine learning approaches have become available recently, with the increasing progress of next-generation sequencing technology; however, existing methods cannot predict sites with high accuracy. Thus, a more accurate predictor is required. In this study, a random forest-based predictor named RF-PseU is proposed for prediction of pseudouridylation sites. To optimize feature representation and obtain a better model, the light gradient boosting machine algorithm and incremental feature selection strategy were used to select the optimum feature space vector for training the random forest model RF-PseU. Compared with previous state-of-the-art predictors, the results on the same benchmark data sets of three species demonstrate that RF-PseU performs better overall. The integrated average leave-one-out cross-validation and independent testing accuracy scores were 71.4% and 74.7%, respectively, representing increments of 3.63% and 4.77% versus the best existing predictor. Moreover, the final RF-PseU model for prediction was built on leave-one-out cross-validation and provides a reliable and robust tool for identifying pseudouridine sites. A web server with a user-friendly interface is accessible at http://148.70.81.170:10228/rfpseu.

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“…Moreover, we also used the area under the ROC curve (AUC) is to quantitively measure the predictive performance of the model (Yang et al, 2018;Lv et al, 2019b;Niu et al, 2019). A higher AUC represents a better predictor (Hanley and McNeil, 1982;Liu et al, 2018;Feng et al, 2019;Lai et al, 2019).…”

Section: Performance Indicatorsmentioning

confidence: 99%

Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications

Zeng

Liao

2020

Front. Bioeng. Biotechnol.

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DNA N4-methylcytosine modification (4mC) plays an essential role in a variety of biological processes. Therefore, accurate identification the 4mC distribution in genome-scale is important for systematically understanding its biological functions. In this study, we present Deep4mcPred, a multi-layer deep learning based predictive model to identify DNA N4-methylcytosine modifications. In this predictor, we for the first time integrate residual network and recurrent neural network to build a multi-layer deep learning predictive system. As compared to existing predictors using traditional machine learning, our proposed method has two advantages. First, our deep learning framework does not need to specify the features when training the predictive model. It can automatically learn the high-level features and capture the characteristic specificity of 4mC sites, benefiting to distinguish true 4mC sites from non-4mC sites. On the other hand, our deep learning method outperforms the traditional machine learning predictors in performance by benchmarking comparison, demonstrating that the proposed Deep4mcPred is more effective in the DNA 4mC site prediction. Moreover, via experimental comparison, we found that attention mechanism introduced into the deep learning framework is useful to capture the critical features. Additionally, we develop a webserver implementing the proposed method for the academic use of research community, which is now available at http://server.malab.cn/Deep4mcPred.

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iProEP: A Computational Predictor for Predicting Promoter

Cited by 139 publications

References 77 publications

Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network

Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications

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