Computational Prediction of Sigma-54 Promoters in Bacterial Genomes by Integrating Motif Finding and Machine Learning Strategies

Liu, Bingqiang; Han, Ling; Liu, Xiangrong; Wu, Jichang; Ma, Qin

doi:10.1109/tcbb.2018.2816032

Cited by 21 publications

(10 citation statements)

References 60 publications

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“…Four standard statistical indicators were used to measure the performance of each model namely Acc , Sn , Sp , and MCC , which are expressed as 67 , 68

where TP represents true positive, TN represents true negative, FP represents false positive, and FN represents false negative.…”

Section: Methodsmentioning

confidence: 99%

Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information

Liu

Wang

et al. 2020

Molecular Therapy - Nucleic Acids

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Transcription factors play key roles in cell-fate decisions by regulating 3D genome conformation and gene expression. The traditional view is that methylation of DNA hinders transcription factors binding to them, but recent research has shown that many transcription factors prefer to bind to methylated DNA. Therefore, identifying such transcription factors and understanding their functions is a stepping-stone for studying methylation-mediated biological processes. In this paper, a two-step discriminated method was proposed to recognize transcription factors and their preference for methylated DNA based only on sequences information. In the first step, the proposed model was used to discriminate transcription factors from non-transcription factors. The areas under the curve (AUCs) are 0.9183 and 0.9116, respectively, for the 5-fold cross-validation test and independent dataset test. Subsequently, for the classification of transcription factors that prefer methylated DNA and transcription factors that prefer non-methylated DNA, our model could produce the AUCs of 0.7744 and 0.7356, respectively, for the 5-fold cross-validation test and independent dataset test. Based on the proposed model, a user-friendly web server called TFPred was built, which can be freely accessed at http://lin-group.cn/server/TFPred/ .

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“…Four standard statistical indicators were used to measure the performance of each model namely Acc , Sn , Sp , and MCC , which are expressed as 67 , 68

where TP represents true positive, TN represents true negative, FP represents false positive, and FN represents false negative.…”

Section: Methodsmentioning

confidence: 99%

Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information

Liu

Wang

et al. 2020

Molecular Therapy - Nucleic Acids

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“…In this study, the four metrics,34, 35, 36, 37, 38, 39, 40 namely, Sn, Sp, Acc, and MCC, were used to measure the performance of the proposed methods, which are defined as follows:{Sn=1−N−+N+0≤Sn≤1Sp=1−N+−N−0≤Sp≤1Acc= 1−N−++N+−N++N−0≤Acc≤1MCC=1−(N−+N++N+−N−)(1+N+−−N−+N+)(1+N−+−N+−N−)−1≤MCC≤1…”

Section: Methodsmentioning

confidence: 99%

“…In this study, the four metrics, [34][35][36][37][38][39][40] namely, Sn, Sp, Acc, and MCC, were used to measure the performance of the proposed methods, which are defined as follows:…”

Section: Evaluation Metricsmentioning

confidence: 99%

iRNA-m7G: Identifying N7-methylguanosine Sites by Fusing Multiple Features

Chen

Feng

Song

et al. 2019

Molecular Therapy - Nucleic Acids

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As an essential post-transcriptional modification, N7-methylguanosine (m7G) regulates nearly every step of the life cycle of mRNA. Accurate identification of the m7G site in the transcriptome will provide insights into its biological functions and mechanisms. Although the m7G-methylated RNA immunoprecipitation sequencing (MeRIP-seq) method has been proposed in this regard, it is still cost-ineffective for detecting the m7G site. Therefore, it is urgent to develop new methods to identify the m7G site. In this work, we developed the first computational predictor called iRNA-m7G to identify m7G sites in the human transcriptome. The feature fusion strategy was used to integrate both sequence- and structure-based features. In the jackknife test, iRNA-m7G obtained an accuracy of 89.88%. The superiority of iRNA-m7G for identifying m7G sites was also demonstrated by comparing with other methods. We hope that iRNA-m7G can become a useful tool to identify m7G sites. A user-friendly web server for iRNA-m7G is freely accessible at http://lin-group.cn/server/iRNA-m7G/.

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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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Computational Prediction of Sigma-54 Promoters in Bacterial Genomes by Integrating Motif Finding and Machine Learning Strategies

Cited by 21 publications

References 60 publications

Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information

Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information

iRNA-m7G: Identifying N7-methylguanosine Sites by Fusing Multiple Features

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

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