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Xin R, Zhang F, Zheng J, Zhang Y, Yu C, Feng X. SDBA: Score Domain-Based Attention for DNA N4-Methylcytosine Site Prediction from Multiperspectives. J Chem Inf Model 2024; 64:2839-2853. [PMID: 37646411 DOI: 10.1021/acs.jcim.3c00688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
In tasks related to DNA sequence classification, choosing the appropriate encoding methods is challenging. Some of the methods encode sequences based on prior knowledge that limits the ability of the model to obtain multiperspective information from the sequences. We introduced a new trainable ensemble method based on the attention mechanism SDBA, which stands for Score Domain-Based Attention. Unlike other methods, we fed the task-independent encoding results into the models and dynamically ensembled features from different perspectives using the SDBA mechanism. This approach allows the model to acquire and weight sequence features voluntarily. SDBA is conceptually general and empirically powerful. It has achieved new state-of-the-art results on the benchmark data sets associated with DNA N4-methylcytosine site prediction.
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Affiliation(s)
- Ruihao Xin
- College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 130000, P.R. China
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, P.R. China
| | - Fan Zhang
- College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 130000, P.R. China
| | - Jiaxin Zheng
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, P.R. China
| | - Yangyi Zhang
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, University of Melbourne, Parkville, Victoria 3050, Australia
| | - Cuinan Yu
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, P.R. China
| | - Xin Feng
- School of Science, Jilin Institute of Chemical Technology, Jilin 130000, P.R. China
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130012, P.R. China
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Lei Y, Meng Y, Guo X, Ning K, Bian Y, Li L, Hu Z, Anashkina AA, Jiang Q, Dong Y, Zhu X. Overview of structural variation calling: Simulation, identification, and visualization. Comput Biol Med 2022; 145:105534. [DOI: 10.1016/j.compbiomed.2022.105534] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 12/11/2022]
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Cai J, Xiao G, Su R. GC6mA-Pred: A deep learning approach to identify DNA N6-methyladenine sites in the rice genome. Methods 2022; 204:14-21. [PMID: 35149214 DOI: 10.1016/j.ymeth.2022.02.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 01/31/2022] [Accepted: 02/05/2022] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION DNA N6-methyladenine (6mA) is a pivotal DNA modification for various biological processes. More accurate prediction of 6mA methylation sites plays an irreplaceable part in grasping the internal rationale of related biological activities. However, the existing prediction methods only extract information from a single dimension, which has some limitations. Therefore, it is very necessary to obtain the information of 6mA sites from different dimensions, so as to establish a reliable prediction method. RESULTS In this study, a neural network based bioinformatics model named GC6mA-Pred is proposed to predict N6-methyladenine modifications in DNA sequences. GC6mA-Pred extracts significant information from both sequence level and graph level. In the sequence level, GC6mA-Pred uses a three-layer convolution neural network (CNN) model to represent the sequence. In the graph level, GC6mA-Pred employs graph neural network (GNN) method to integrate various information contained in the chemical molecular formula corresponding to DNA sequence. In our newly built dataset, GC6mA-Pred shows better performance than other existing models. The results of comparative experiments have illustrated that GC6mA-Pred is capable of producing a marked effect in accurately identifying DNA 6mA modifications.
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Affiliation(s)
- Jianhua Cai
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China; College of Mathematics and Computer Science, Fuzhou University, Fuzhou, PR China
| | - Guobao Xiao
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China.
| | - Ran Su
- College of Intelligence and Computing, Tianjin University, Tianjin, China.
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Li H, Gong Y, Liu Y, Lin H, Wang G. Detection of transcription factors binding to methylated DNA by deep recurrent neural network. Brief Bioinform 2021; 23:6484512. [PMID: 34962264 DOI: 10.1093/bib/bbab533] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/23/2021] [Accepted: 11/19/2021] [Indexed: 12/13/2022] Open
Abstract
Transcription factors (TFs) are proteins specifically involved in gene expression regulation. It is generally accepted in epigenetics that methylated nucleotides could prevent the TFs from binding to DNA fragments. However, recent studies have confirmed that some TFs have capability to interact with methylated DNA fragments to further regulate gene expression. Although biochemical experiments could recognize TFs binding to methylated DNA sequences, these wet experimental methods are time-consuming and expensive. Machine learning methods provide a good choice for quickly identifying these TFs without experimental materials. Thus, this study aims to design a robust predictor to detect methylated DNA-bound TFs. We firstly proposed using tripeptide word vector feature to formulate protein samples. Subsequently, based on recurrent neural network with long short-term memory, a two-step computational model was designed. The first step predictor was utilized to discriminate transcription factors from non-transcription factors. Once proteins were predicted as TFs, the second step predictor was employed to judge whether the TFs can bind to methylated DNA. Through the independent dataset test, the accuracies of the first step and the second step are 86.63% and 73.59%, respectively. In addition, the statistical analysis of the distribution of tripeptides in training samples showed that the position and number of some tripeptides in the sequence could affect the binding of TFs to methylated DNA. Finally, on the basis of our model, a free web server was established based on the proposed model, which can be available at https://bioinfor.nefu.edu.cn/TFPM/.
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Affiliation(s)
- Hongfei Li
- College of Information and Computer Engineering at Northeast Forestry University of China
| | - Yue Gong
- College of Information and Computer Engineering at Northeast Forestry University of China
| | - Yifeng Liu
- School of management at Henan Institute of Technology of China
| | - Hao Lin
- Center for Informational Biology at University of Electronic Science and Technology of China
| | - Guohua Wang
- College of Information and Computer Engineering at Northeast Forestry University of China
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Zhao D, Teng Z, Li Y, Chen D. iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest. Front Genet 2021; 12:773202. [PMID: 34917130 PMCID: PMC8669811 DOI: 10.3389/fgene.2021.773202] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 10/08/2021] [Indexed: 12/25/2022] Open
Abstract
Recently, several anti-inflammatory peptides (AIPs) have been found in the process of the inflammatory response, and these peptides have been used to treat some inflammatory and autoimmune diseases. Therefore, identifying AIPs accurately from a given amino acid sequences is critical for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics and the acceleration of their application in therapy. In this paper, a random forest-based model called iAIPs for identifying AIPs is proposed. First, the original samples were encoded with three feature extraction methods, including g-gap dipeptide composition (GDC), dipeptide deviation from the expected mean (DDE), and amino acid composition (AAC). Second, the optimal feature subset is generated by a two-step feature selection method, in which the feature is ranked by the analysis of variance (ANOVA) method, and the optimal feature subset is generated by the incremental feature selection strategy. Finally, the optimal feature subset is inputted into the random forest classifier, and the identification model is constructed. Experiment results showed that iAIPs achieved an AUC value of 0.822 on an independent test dataset, which indicated that our proposed model has better performance than the existing methods. Furthermore, the extraction of features for peptide sequences provides the basis for evolutionary analysis. The study of peptide identification is helpful to understand the diversity of species and analyze the evolutionary history of species.
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Affiliation(s)
- Dongxu Zhao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Zhixia Teng
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Yanjuan Li
- College of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Dong Chen
- College of Electrical and Information Engineering, Quzhou University, Quzhou, China
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Brewster LR, Ibrahim AK, DeGroot BC, Ostendorf TJ, Zhuang H, Chérubin LM, Ajemian MJ. Classifying Goliath Grouper ( Epinephelus itajara) Behaviors from a Novel, Multi-Sensor Tag. SENSORS 2021; 21:s21196392. [PMID: 34640710 PMCID: PMC8512029 DOI: 10.3390/s21196392] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/17/2021] [Accepted: 09/19/2021] [Indexed: 01/23/2023]
Abstract
Inertial measurement unit sensors (IMU; i.e., accelerometer, gyroscope and magnetometer combinations) are frequently fitted to animals to better understand their activity patterns and energy expenditure. Capable of recording hundreds of data points a second, these sensors can quickly produce large datasets that require methods to automate behavioral classification. Here, we describe behaviors derived from a custom-built multi-sensor bio-logging tag attached to Atlantic Goliath grouper (Epinephelus itajara) within a simulated ecosystem. We then compared the performance of two commonly applied machine learning approaches (random forest and support vector machine) to a deep learning approach (convolutional neural network, or CNN) for classifying IMU data from this tag. CNNs are frequently used to recognize activities from IMU data obtained from humans but are less commonly considered for other animals. Thirteen behavioral classes were identified during ethogram development, nine of which were classified. For the conventional machine learning approaches, 187 summary statistics were extracted from the data, including time and frequency domain features. The CNN was fed absolute values obtained from fast Fourier transformations of the raw tri-axial accelerometer, gyroscope and magnetometer channels, with a frequency resolution of 512 data points. Five metrics were used to assess classifier performance; the deep learning approach performed better across all metrics (Sensitivity = 0.962; Specificity = 0.996; F1-score = 0.962; Matthew’s Correlation Coefficient = 0.959; Cohen’s Kappa = 0.833) than both conventional machine learning approaches. Generally, the random forest performed better than the support vector machine. In some instances, a conventional learning approach yielded a higher performance metric for particular classes (e.g., the random forest had a F1-score of 0.971 for backward swimming compared to 0.955 for the CNN). Deep learning approaches could potentially improve behavioral classification from IMU data, beyond that obtained from conventional machine learning methods.
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Affiliation(s)
- Lauran R. Brewster
- Harbor Branch Oceanographic Institute, Florida Atlantic University, Fort Pierce, FL 34946, USA; (A.K.I.); (B.C.D.); (T.J.O.); (L.M.C.); (M.J.A.)
- Correspondence: ; Tel.: +1-772-242-2638
| | - Ali K. Ibrahim
- Harbor Branch Oceanographic Institute, Florida Atlantic University, Fort Pierce, FL 34946, USA; (A.K.I.); (B.C.D.); (T.J.O.); (L.M.C.); (M.J.A.)
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA;
| | - Breanna C. DeGroot
- Harbor Branch Oceanographic Institute, Florida Atlantic University, Fort Pierce, FL 34946, USA; (A.K.I.); (B.C.D.); (T.J.O.); (L.M.C.); (M.J.A.)
| | - Thomas J. Ostendorf
- Harbor Branch Oceanographic Institute, Florida Atlantic University, Fort Pierce, FL 34946, USA; (A.K.I.); (B.C.D.); (T.J.O.); (L.M.C.); (M.J.A.)
| | - Hanqi Zhuang
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA;
| | - Laurent M. Chérubin
- Harbor Branch Oceanographic Institute, Florida Atlantic University, Fort Pierce, FL 34946, USA; (A.K.I.); (B.C.D.); (T.J.O.); (L.M.C.); (M.J.A.)
| | - Matthew J. Ajemian
- Harbor Branch Oceanographic Institute, Florida Atlantic University, Fort Pierce, FL 34946, USA; (A.K.I.); (B.C.D.); (T.J.O.); (L.M.C.); (M.J.A.)
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Hu J, Zheng LL, Bai YS, Zhang KW, Yu DJ, Zhang GJ. Accurate prediction of protein-ATP binding residues using position-specific frequency matrix. Anal Biochem 2021; 626:114241. [PMID: 33971164 DOI: 10.1016/j.ab.2021.114241] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/27/2021] [Accepted: 05/01/2021] [Indexed: 10/21/2022]
Abstract
Knowledge of protein-ATP interaction can help for protein functional annotation and drug discovery. Accurately identifying protein-ATP binding residues is an important but challenging task to gain the knowledge of protein-ATP interactions, especially for the case where only protein sequence information is given. In this study, we propose a novel method, named DeepATPseq, to predict protein-ATP binding residues without using any information about protein three-dimension structure or sequence-derived structural information. In DeepATPseq, the HHBlits-generated position-specific frequency matrix (PSFM) profile is first employed to extract the feature information of each residue. Then, for each residue, the PSFM-based feature is fed into two prediction models, which are generated by the algorithms of deep convolutional neural network (DCNN) and support vector machine (SVM) separately. The final ATP-binding probability of the corresponding residue is calculated by the weighted sum of the outputted values of DCNN-based and SVM-based models. Experimental results on the independent validation data set demonstrate that DeepATPseq could achieve an accuracy of 77.71%, covering 57.42% of all ATP-binding residues, while achieving a Matthew's correlation coefficient value (0.655) that is significantly higher than that of existing sequence-based methods and comparable to that of the state-of-the-art structure-based predictors. Detailed data analysis show that the major advantage of DeepATPseq lies at the combination utilization of DCNN and SVM that helps dig out more discriminative information from the PSFM profiles. The online server and standalone package of DeepATPseq are freely available at: https://jun-csbio.github.io/DeepATPseq/for academic use.
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Affiliation(s)
- Jun Hu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.
| | - Lin-Lin Zheng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Yan-Song Bai
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Ke-Wen Zhang
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology,Xiaolingwei 200, Nanjing, 210094, China.
| | - Gui-Jun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.
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