BTFBS: Binding Prediction of Bacterial Transcription Factors and Binding Sites Based on Deep Learning
The binding of transcription factors (TFs) to TF binding sites plays a vital role in the process of regulating gene expression and evolution. With the development of machine learning and deep learning, some successes have been achieved in predicting transcription factors and binding sites. In this p...
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MDPI AG
2025-02-01
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| author | Bingbing Jin Song Liang Xiaoqian Liu Rui Zhang Yun Zhu Yuanyuan Chen Guangjin Liu Tao Yang |
| author_facet | Bingbing Jin Song Liang Xiaoqian Liu Rui Zhang Yun Zhu Yuanyuan Chen Guangjin Liu Tao Yang |
| author_sort | Bingbing Jin |
| collection | DOAJ |
| description | The binding of transcription factors (TFs) to TF binding sites plays a vital role in the process of regulating gene expression and evolution. With the development of machine learning and deep learning, some successes have been achieved in predicting transcription factors and binding sites. In this paper, we develop a model, BTFBS, which predicts whether the bacterial transcription factors and binding sites combine or not. The model takes both the amino acid sequences of bacterial transcription factors and the nucleotide sequences of binding sites as inputs, and extracts features through convolutional neural network and MultiheadAttention. For the model inputs, we use two negative sample sampling methods: RS and EE. On the test dataset of RS, the accuracy, sensitivity, specificity, F1-score, and MCC of BTFBS are 0.91446, 0.89746, 0.93134, 0.91264, and 0.82946, respectively. Furthermore, on the test dataset of EE, the accuracy, sensitivity, specificity, F1-score and MCC of BTFBS are 0.87868, 0.89354, 0.86394, 0.87996, and 0.75796, respectively. Meanwhile, our findings indicate that the optimal approach for obtaining negative samples in the context of bacterial research is to utilize the whole genome sequences of the corresponding bacteria, as opposed to the shuffling method. The above results on the test dataset have shown that the proposed BTFBS model has a good performance and it can provide an experimental guide. |
| format | Article |
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| language | English |
| publishDate | 2025-02-01 |
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| spelling | doaj-art-10df92e40a2d426e994c2ceed71b3e282025-08-20T02:03:31ZengMDPI AGMathematics2227-73902025-02-0113458910.3390/math13040589BTFBS: Binding Prediction of Bacterial Transcription Factors and Binding Sites Based on Deep LearningBingbing Jin0Song Liang1Xiaoqian Liu2Rui Zhang3Yun Zhu4Yuanyuan Chen5Guangjin Liu6Tao Yang7College of Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Veterinary Medicine, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Veterinary Medicine, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaThe binding of transcription factors (TFs) to TF binding sites plays a vital role in the process of regulating gene expression and evolution. With the development of machine learning and deep learning, some successes have been achieved in predicting transcription factors and binding sites. In this paper, we develop a model, BTFBS, which predicts whether the bacterial transcription factors and binding sites combine or not. The model takes both the amino acid sequences of bacterial transcription factors and the nucleotide sequences of binding sites as inputs, and extracts features through convolutional neural network and MultiheadAttention. For the model inputs, we use two negative sample sampling methods: RS and EE. On the test dataset of RS, the accuracy, sensitivity, specificity, F1-score, and MCC of BTFBS are 0.91446, 0.89746, 0.93134, 0.91264, and 0.82946, respectively. Furthermore, on the test dataset of EE, the accuracy, sensitivity, specificity, F1-score and MCC of BTFBS are 0.87868, 0.89354, 0.86394, 0.87996, and 0.75796, respectively. Meanwhile, our findings indicate that the optimal approach for obtaining negative samples in the context of bacterial research is to utilize the whole genome sequences of the corresponding bacteria, as opposed to the shuffling method. The above results on the test dataset have shown that the proposed BTFBS model has a good performance and it can provide an experimental guide.https://www.mdpi.com/2227-7390/13/4/589transcription factorsbinding sitesdeep learningbacteria |
| spellingShingle | Bingbing Jin Song Liang Xiaoqian Liu Rui Zhang Yun Zhu Yuanyuan Chen Guangjin Liu Tao Yang BTFBS: Binding Prediction of Bacterial Transcription Factors and Binding Sites Based on Deep Learning Mathematics transcription factors binding sites deep learning bacteria |
| title | BTFBS: Binding Prediction of Bacterial Transcription Factors and Binding Sites Based on Deep Learning |
| title_full | BTFBS: Binding Prediction of Bacterial Transcription Factors and Binding Sites Based on Deep Learning |
| title_fullStr | BTFBS: Binding Prediction of Bacterial Transcription Factors and Binding Sites Based on Deep Learning |
| title_full_unstemmed | BTFBS: Binding Prediction of Bacterial Transcription Factors and Binding Sites Based on Deep Learning |
| title_short | BTFBS: Binding Prediction of Bacterial Transcription Factors and Binding Sites Based on Deep Learning |
| title_sort | btfbs binding prediction of bacterial transcription factors and binding sites based on deep learning |
| topic | transcription factors binding sites deep learning bacteria |
| url | https://www.mdpi.com/2227-7390/13/4/589 |
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