1
|
Peng B, Sun G, Fan Y. iProL: identifying DNA promoters from sequence information based on Longformer pre-trained model. BMC Bioinformatics 2024; 25:224. [PMID: 38918692 PMCID: PMC11201334 DOI: 10.1186/s12859-024-05849-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 06/19/2024] [Indexed: 06/27/2024] Open
Abstract
Promoters are essential elements of DNA sequence, usually located in the immediate region of the gene transcription start sites, and play a critical role in the regulation of gene transcription. Its importance in molecular biology and genetics has attracted the research interest of researchers, and it has become a consensus to seek a computational method to efficiently identify promoters. Still, existing methods suffer from imbalanced recognition capabilities for positive and negative samples, and their recognition effect can still be further improved. We conducted research on E. coli promoters and proposed a more advanced prediction model, iProL, based on the Longformer pre-trained model in the field of natural language processing. iProL does not rely on prior biological knowledge but simply uses promoter DNA sequences as plain text to identify promoters. It also combines one-dimensional convolutional neural networks and bidirectional long short-term memory to extract both local and global features. Experimental results show that iProL has a more balanced and superior performance than currently published methods. Additionally, we constructed a novel independent test set following the previous specification and compared iProL with three existing methods on this independent test set.
Collapse
Affiliation(s)
- Binchao Peng
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Guicong Sun
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Yongxian Fan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
| |
Collapse
|
2
|
A new hybrid algorithm for three-stage gene selection based on whale optimization. Sci Rep 2023; 13:3783. [PMID: 36882446 PMCID: PMC9992521 DOI: 10.1038/s41598-023-30862-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 03/02/2023] [Indexed: 03/09/2023] Open
Abstract
In biomedical data mining, the gene dimension is often much larger than the sample size. To solve this problem, we need to use a feature selection algorithm to select feature gene subsets with a strong correlation with phenotype to ensure the accuracy of subsequent analysis. This paper presents a new three-stage hybrid feature gene selection method, that combines a variance filter, extremely randomized tree, and whale optimization algorithm. First, a variance filter is used to reduce the dimension of the feature gene space, and an extremely randomized tree is used to further reduce the feature gene set. Finally, the whale optimization algorithm is used to select the optimal feature gene subset. We evaluate the proposed method with three different classifiers in seven published gene expression profile datasets and compare it with other advanced feature selection algorithms. The results show that the proposed method has significant advantages in a variety of evaluation indicators.
Collapse
|
3
|
Liu J, Feng H, Tang Y, Zhang L, Qu C, Zeng X, Peng X. A novel hybrid algorithm based on Harris Hawks for tumor feature gene selection. PeerJ Comput Sci 2023; 9:e1229. [PMID: 37346505 PMCID: PMC10280456 DOI: 10.7717/peerj-cs.1229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/09/2023] [Indexed: 06/23/2023]
Abstract
Background Gene expression data are often used to classify cancer genes. In such high-dimensional datasets, however, only a few feature genes are closely related to tumors. Therefore, it is important to accurately select a subset of feature genes with high contributions to cancer classification. Methods In this article, a new three-stage hybrid gene selection method is proposed that combines a variance filter, extremely randomized tree and Harris Hawks (VEH). In the first stage, we evaluated each gene in the dataset through the variance filter and selected the feature genes that meet the variance threshold. In the second stage, we use extremely randomized tree to further eliminate irrelevant genes. Finally, we used the Harris Hawks algorithm to select the gene subset from the previous two stages to obtain the optimal feature gene subset. Results We evaluated the proposed method using three different classifiers on eight published microarray gene expression datasets. The results showed a 100% classification accuracy for VEH in gastric cancer, acute lymphoblastic leukemia and ovarian cancer, and an average classification accuracy of 95.33% across a variety of other cancers. Compared with other advanced feature selection algorithms, VEH has obvious advantages when measured by many evaluation criteria.
Collapse
Affiliation(s)
- Junjian Liu
- Department of Statistics, Hunan Normal University College of Mathematics and Statistics, Changsha, Hunan, China
| | - Huicong Feng
- Department of Pathology and Pathophysiology, Hunan Normal University School of Medicine, Changsha, Hunan, China
| | - Yifan Tang
- Department of Pathology and Pathophysiology, Hunan Normal University School of Medicine, Changsha, Hunan, China
| | - Lupeng Zhang
- Department of Biochemistry and Molecular Biology, Jishou University School of Medicine, Jishou, Hunan, China
| | - Chiwen Qu
- Department of Statistics, Hunan Normal University College of Mathematics and Statistics, Changsha, Hunan, China
| | - Xiaomin Zeng
- Department of Epidemiology and Health Statistics, Xiangya Public Health School, Central South University, Changsha, Hunan, China
| | - Xiaoning Peng
- Department of Statistics, Hunan Normal University College of Mathematics and Statistics, Changsha, Hunan, China
- Department of Pathology and Pathophysiology, Hunan Normal University School of Medicine, Changsha, Hunan, China
| |
Collapse
|
4
|
Yang TH, Yu YH, Wu SH, Zhang FY. CFA: An explainable deep learning model for annotating the transcriptional roles of cis-regulatory modules based on epigenetic codes. Comput Biol Med 2023; 152:106375. [PMID: 36502693 DOI: 10.1016/j.compbiomed.2022.106375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 11/07/2022] [Accepted: 11/27/2022] [Indexed: 11/30/2022]
Abstract
Metazoa gene expression is controlled by modular DNA segments called cis-regulatory modules (CRMs). CRMs can convey promoter/enhancer/insulator roles, generating additional regulation layers in transcription. Experiments for understanding CRM roles are low-throughput and costly. Large-scale CRM function investigation still depends on computational methods. However, existing in silico tools only recognize enhancers or promoters exclusively, thus accumulating errors when considering CRM promoter/enhancer/insulator roles altogether. Currently, no algorithm can concurrently consider these CRM roles. In this research, we developed the CRM Function Annotator (CFA) model. CFA provides complete CRM transcriptional role labeling based on epigenetic profiling interpretation. We demonstrated that CFA achieves high performance (test macro auROC/auPRC = 94.1%/90.3%) and outperforms existing tools in promoter/enhancer/insulator identification. CFA is also inspected to recognize explainable epigenetic codes consistent with previous findings when labeling CRM roles. By considering the higher-order combinations of the epigenetic codes, CFA significantly reduces false-positive rates in CRM transcriptional role annotation. CFA is available at https://github.com/cobisLab/CFA/.
Collapse
Affiliation(s)
- Tzu-Hsien Yang
- Department of Biomedical Engineering, National Cheng Kung University, No. 1, University Road, Tainan 701, Taiwan.
| | - Yu-Huai Yu
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan.
| | - Sheng-Hang Wu
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan.
| | - Fang-Yuan Zhang
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan.
| |
Collapse
|
5
|
Wang M, Li F, Wu H, Liu Q, Li S. PredPromoter-MF(2L): A Novel Approach of Promoter Prediction Based on Multi-source Feature Fusion and Deep Forest. Interdiscip Sci 2022; 14:697-711. [PMID: 35488998 DOI: 10.1007/s12539-022-00520-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 04/05/2022] [Accepted: 04/05/2022] [Indexed: 12/12/2022]
Abstract
Promoters short DNA sequences play vital roles in initiating gene transcription. However, it remains a challenge to identify promoters using conventional experiment techniques in a high-throughput manner. To this end, several computational predictors based on machine learning models have been developed, while their performance is unsatisfactory. In this study, we proposed a novel two-layer predictor, called PredPromoter-MF(2L), based on multi-source feature fusion and ensemble learning. PredPromoter-MF(2L) was developed based on various deep features learned by a pre-trained deep learning network model and sequence-derived features. Feature selection based on XGBoost was applied to reduce fused features dimensions, and a cascade deep forest model was trained on the selected feature subset for promoter prediction. The results both fivefold cross-validation and independent test demonstrated that PredPromoter-MF(2L) outperformed state-of-the-art methods.
Collapse
Affiliation(s)
- Miao Wang
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shanxi, China
| | - Fuyi Li
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, VIC, 3000, Australia
| | - Hao Wu
- School of Software, Shandong University, Jinan, 250100, Shandong, China
| | - Quanzhong Liu
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shanxi, China.
| | - Shuqin Li
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shanxi, China.
| |
Collapse
|
6
|
Qiao H, Zhang S, Xue T, Wang J, Wang B. iPro-GAN: A novel model based on generative adversarial learning for identifying promoters and their strength. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 215:106625. [PMID: 35038653 DOI: 10.1016/j.cmpb.2022.106625] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 12/13/2021] [Accepted: 01/06/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Promoter is a component of the gene, which can specifically bind with RNA polymerase and determine where transcription starts, and also determine the transcription efficiency of the gene. Promoters can be divided into strong promoters and weak promoters because their structures and the interaction time interval are quite different. The functional variation of the promoter can lead to a variety of diseases. Therefore, identifying promoters and their strength is necessary and has important biological significance. A novel and promising model based on deep learning is proposed to achieve it. METHODS In this work, we build a power model named iPro-GAN for identification of promoters and their strength. First, we collect benchmark datasets and independent datasets for training and testing. Then, Moran-based spatial auto-cross correlation method is used as feature extraction method. Finally, deep convolution generative adversarial network with 10-fold cross validation is applied for classifying. The first layer of the model is used to identify the promoter and the second layer is used to determine its type. RESULTS On the benchmark data set, the accuracy of the first layer predictor is 93.15%, and the accuracy of the second layer predictor is 92.30%. On the independent data set, the accuracy of the first layer predictor is 86.77%, and the accuracy of the second layer predictor is 91.66%. In particular, breakthrough progress has been made in the identification of promoters' strength. CONCLUSIONS These results are far higher than the existing best predictor, which indicate that our model is serviceable and practicable to identify promoters and their strength. Furthermore, the datasets and source codes are available from this link: https://github.com/Bovbene/iPro-GAN.
Collapse
Affiliation(s)
- Huijuan Qiao
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China.
| | - Tian Xue
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| | - Jinyue Wang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| | - Bowei Wang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| |
Collapse
|
7
|
Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota. Pharmaceutics 2021; 13:pharmaceutics13122001. [PMID: 34959282 PMCID: PMC8707855 DOI: 10.3390/pharmaceutics13122001] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 11/19/2021] [Accepted: 11/23/2021] [Indexed: 01/09/2023] Open
Abstract
Over 150 drugs are currently recognised as being susceptible to metabolism or bioaccumulation (together described as depletion) by gastrointestinal microorganisms; however, the true number is likely higher. Microbial drug depletion is often variable between and within individuals, depending on their unique composition of gut microbiota. Such variability can lead to significant differences in pharmacokinetics, which may be associated with dosing difficulties and lack of medication response. In this study, literature mining and unsupervised learning were used to curate a dataset of 455 drug-microbiota interactions. From this, 11 supervised learning models were developed that could predict drugs' susceptibility to depletion by gut microbiota. The best model, a tuned extremely randomised trees classifier, achieved performance metrics of AUROC: 75.1% ± 6.8; weighted recall: 79.2% ± 3.9; balanced accuracy: 69.0% ± 4.6; and weighted precision: 80.2% ± 3.7 when validated on 91 drugs. This machine learning model is the first of its kind and provides a rapid, reliable, and resource-friendly tool for researchers and industry professionals to screen drugs for susceptibility to depletion by gut microbiota. The recognition of drug-microbiome interactions can support successful drug development and promote better formulations and dosage regimens for patients.
Collapse
|