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Jin YT, Tan Y, Gan ZH, Hao YD, Wang TY, Lin H, Tang B. Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors. Methods 2024; 229:125-132. [PMID: 38964595 DOI: 10.1016/j.ymeth.2024.06.012] [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: 05/02/2024] [Revised: 06/01/2024] [Accepted: 06/27/2024] [Indexed: 07/06/2024] Open
Abstract
DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.
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Affiliation(s)
- Yan-Ting Jin
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China.
| | - Yang Tan
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Zhong-Hua Gan
- Department of Pathology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Yu-Duo Hao
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China.
| | - Tian-Yu Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China.
| | - Bo Tang
- Department of Pathology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China.
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2
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Chen W, Zhang Y, Wu W, Yang H, Huang W. Machine learning-based predictive model for abdominal diseases using physical examination datasets. Comput Biol Med 2024; 173:108249. [PMID: 38531251 DOI: 10.1016/j.compbiomed.2024.108249] [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: 02/08/2024] [Revised: 02/21/2024] [Accepted: 03/06/2024] [Indexed: 03/28/2024]
Abstract
Abdominal ultrasound is a key non-invasive imaging method for diagnosing liver, kidney, and gallbladder diseases, despite its clinical significance, not all individuals can undergo abdominal ultrasonography during routine health check-ups due to limitations in equipment, cost, and time. This study aims to use basic physical examination data to predict the risk of diseases of the liver, kidney, and gallbladder that can be diagnosed via abdominal ultrasound. Basic physical examination data contain gender, age, height, weight, BMI, pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol, triglycerides, fasting blood glucose (FBG), and uric acid-we established seven single-label predictive models and one multi-label predictive model. These models were specifically designed to predict a range of abdominal diseases. The single-label models, utilizing the XGBoost algorithm, targeted diseases such as fatty liver (with an Area Under the Curve (AUC) of 0.9344), liver deposits (AUC: 0.8221), liver cysts (AUC: 0.7928), gallbladder polyps (AUC: 0.7508), kidney stones (AUC: 0.7853), kidney cysts (AUC: 0.8241), and kidney crystals (AUC: 0.7536). Furthermore, a comprehensive multi-label model, capable of predicting multiple conditions simultaneously, was established by FCN and achieved an AUC of 0.6344. We conducted interpretability analysis on these models to enhance their understanding and applicability in clinical settings. The insights gained from this analysis are crucial for the development of targeted disease prevention strategies. This study represents a significant advancement in utilizing physical examination data to predict ultrasound results, offering a novel approach to early diagnosis and prevention of abdominal diseases.
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Affiliation(s)
- Wei Chen
- Zhejiang Academy of Traditional Chinese Medicine Culture, Zhejiang Chinese Medical University, Hangzhou, China; Four Provincial Marginal Traditional Chinese Medicine Hospitals (Quzhou Traditional Chinese Medicine Hospital) Affiliated to Zhejiang University of Traditional Chinese Medicine, Quzhou, China
| | - YuJie Zhang
- Zhejiang Academy of Traditional Chinese Medicine Culture, Zhejiang Chinese Medical University, Hangzhou, China
| | - Weili Wu
- Four Provincial Marginal Traditional Chinese Medicine Hospitals (Quzhou Traditional Chinese Medicine Hospital) Affiliated to Zhejiang University of Traditional Chinese Medicine, Quzhou, China
| | - Hui Yang
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China.
| | - Wenxiu Huang
- Zhejiang Academy of Traditional Chinese Medicine Culture, Zhejiang Chinese Medical University, Hangzhou, China.
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3
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Wang M, Ali H, Xu Y, Xie J, Xu S. BiPSTP: Sequence feature encoding method for identifying different RNA modifications with bidirectional position-specific trinucleotides propensities. J Biol Chem 2024; 300:107140. [PMID: 38447795 PMCID: PMC10997841 DOI: 10.1016/j.jbc.2024.107140] [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: 12/21/2023] [Revised: 02/17/2024] [Accepted: 02/25/2024] [Indexed: 03/08/2024] Open
Abstract
RNA modification, a posttranscriptional regulatory mechanism, significantly influences RNA biogenesis and function. The accurate identification of modification sites is paramount for investigating their biological implications. Methods for encoding RNA sequence into numerical data play a crucial role in developing robust models for predicting modification sites. However, existing techniques suffer from limitations, including inadequate information representation, challenges in effectively integrating positional and sequential information, and the generation of irrelevant or redundant features when combining multiple approaches. These deficiencies hinder the effectiveness of machine learning models in addressing the performance challenges associated with predicting RNA modification sites. Here, we introduce a novel RNA sequence feature representation method, named BiPSTP, which utilizes bidirectional trinucleotide position-specific propensities. We employ the parameter ξ to denote the interval between the current nucleotide and its adjacent forward or backward dinucleotide, enabling the extraction of positional and sequential information from RNA sequences. Leveraging the BiPSTP method, we have developed the prediction model mRNAPred using support vector machine classifier to identify multiple types of RNA modification sites. We evaluate the performance of our BiPSTP method and mRNAPred model across 12 distinct RNA modification types. Our experimental results demonstrate the superiority of the mRNAPred model compared to state-of-art models in the domain of RNA modification sites identification. Importantly, our BiPSTP method enhances the robustness and generalization performance of prediction models. Notably, it can be applied to feature extraction from DNA sequences to predict other biological modification sites.
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Affiliation(s)
- Mingzhao Wang
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Haider Ali
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Yandi Xu
- School of Computer Science, Shaanxi Normal University, Xi'an, China; College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Juanying Xie
- School of Computer Science, Shaanxi Normal University, Xi'an, China.
| | - Shengquan Xu
- College of Life Sciences, Shaanxi Normal University, Xi'an, China.
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4
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Meng C, Yuan Y, Zhao H, Pei Y, Li Z. IIFS: An improved incremental feature selection method for protein sequence processing. Comput Biol Med 2023; 167:107654. [PMID: 37944304 DOI: 10.1016/j.compbiomed.2023.107654] [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: 09/01/2023] [Revised: 10/09/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023]
Abstract
MOTIVATION Discrete features can be obtained from protein sequences using a feature extraction method. These features are the basis of downstream processing of protein data, but it is necessary to screen and select some important features from them as they generally have data redundancy. RESULT Here, we report IIFS, an improved incremental feature selection method that exploits a new subset search strategy to find the optimal feature set. IIFS combines nonadjacent sorting features to prevent the drawbacks of data explosion and excessive reliance on feature sorting results. The comparative experimental results on 27 feature sorting data show that IIFS can find more accurate and important features compared to existing methods.The IIFS approach also handles data redundancy more efficiently and finds more representative and discriminatory features while ensuring minimal feature dimensionality and good evaluation metrics. Moreover, we wrap this method and deploy it on a web server for access at http://112.124.26.17:8005/.
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Affiliation(s)
- Chaolu Meng
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China; Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, China
| | - Ye Yuan
- Beidahuang Industry Group General Hospital, Harbin, 150001, China
| | - Haiyan Zhao
- College of Integration of Traditional Chinese and Western Medicine to Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Yue Pei
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100190, China
| | - Zhi Li
- Department of Spleen and Stomach Diseases, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China.
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Jia J, Cao X, Wei Z. DLC-ac4C: A Prediction Model for N4-acetylcytidine Sites in Human mRNA Based on DenseNet and Bidirectional LSTM Methods. Curr Genomics 2023; 24:171-186. [PMID: 38178985 PMCID: PMC10761336 DOI: 10.2174/0113892029270191231013111911] [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: 07/03/2023] [Revised: 09/13/2023] [Accepted: 09/21/2023] [Indexed: 01/06/2024] Open
Abstract
Introduction N4 acetylcytidine (ac4C) is a highly conserved nucleoside modification that is essential for the regulation of immune functions in organisms. Currently, the identification of ac4C is primarily achieved using biological methods, which can be time-consuming and labor-intensive. In contrast, accurate identification of ac4C by computational methods has become a more effective method for classification and prediction. Aim To the best of our knowledge, although there are several computational methods for ac4C locus prediction, the performance of the models they constructed is poor, and the network structure they used is relatively simple and suffers from the disadvantage of network degradation. This study aims to improve these limitations by proposing a predictive model based on integrated deep learning to better help identify ac4C sites. Methods In this study, we propose a new integrated deep learning prediction framework, DLC-ac4C. First, we encode RNA sequences based on three feature encoding schemes, namely C2 encoding, nucleotide chemical property (NCP) encoding, and nucleotide density (ND) encoding. Second, one-dimensional convolutional layers and densely connected convolutional networks (DenseNet) are used to learn local features, and bi-directional long short-term memory networks (Bi-LSTM) are used to learn global features. Third, a channel attention mechanism is introduced to determine the importance of sequence characteristics. Finally, a homomorphic integration strategy is used to limit the generalization error of the model, which further improves the performance of the model. Results The DLC-ac4C model performed well in terms of sensitivity (Sn), specificity (Sp), accuracy (Acc), Mathews correlation coefficient (MCC), and area under the curve (AUC) for the independent test data with 86.23%, 79.71%, 82.97%, 66.08%, and 90.42%, respectively, which was significantly better than the prediction accuracy of the existing methods. Conclusion Our model not only combines DenseNet and Bi-LSTM, but also uses the channel attention mechanism to better capture hidden information features from a sequence perspective, and can identify ac4C sites more effectively.
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Affiliation(s)
- Jianhua Jia
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China
| | - Xiaojing Cao
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China
| | - Zhangying Wei
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China
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6
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Fan R, Ding Y, Zou Q, Yuan L. Multi-view local hyperplane nearest neighbor model based on independence criterion for identifying vesicular transport proteins. Int J Biol Macromol 2023; 247:125774. [PMID: 37437677 DOI: 10.1016/j.ijbiomac.2023.125774] [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: 05/11/2023] [Revised: 06/30/2023] [Accepted: 07/07/2023] [Indexed: 07/14/2023]
Abstract
Vesicular transport proteins participate in various biological processes and play a significant role in the movement of substances within cells. These proteins are associated with numerous human diseases, making their identification particularly important. In this study, we developed a novel strategy for accurately identifying vesicular transport proteins. We developed a novel multi-view classifier called graph-regularized k-local hyperplane distance nearest neighbor model (HSIC-GHKNN), which combines the Hilbert-Schmidt independence criterion (HSIC)-based multi-view learning method with a local hyperplane distance nearest-neighbor classifier. We first extracted protein evolution information using two feature extraction methods, pseudo-position-specific scoring matrix (PsePSSM) and AATP, and addressed dataset imbalance using the Edited Nearest Neighbors (ENN) algorithm. Subsequently, we employed a local hyperplane distance nearest-neighbor classifier for each view identification and added an HSIC term to maintain independence between views. We then assessed the performance of our identification strategy and analyzed the PsePSSM and AATP feature sets to determine the influencing factors of the classification results. The experimental results demonstrate that the accurate and Matthew correlation coefficients of our strategy on the independent test set are 85.8 % and 0.548, respectively. Our approach outperformed existing methods in most evaluation metrics. In addition, the proposed multi-view classification model can easily be applied to similar identification tasks.
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Affiliation(s)
- Rui Fan
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324000, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324000, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324000, China.
| | - Lei Yuan
- Department of Hepatobiliary Surgery, Quzhou People's Hospital, Quzhou, Zhejiang 324000, China.
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Zulfiqar H, Ahmed Z, Kissanga Grace-Mercure B, Hassan F, Zhang ZY, Liu F. Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique. Front Microbiol 2023; 14:1170785. [PMID: 37125199 PMCID: PMC10133480 DOI: 10.3389/fmicb.2023.1170785] [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: 02/21/2023] [Accepted: 03/17/2023] [Indexed: 05/02/2023] Open
Abstract
Promotors are those genomic regions on the upstream of genes, which are bound by RNA polymerase for starting gene transcription. Because it is the most critical element of gene expression, the recognition of promoters is crucial to understand the regulation of gene expression. This study aimed to develop a machine learning-based model to predict promotors in Agrobacterium tumefaciens (A. tumefaciens) strain C58. In the model, promotor sequences were encoded by three different kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. These optimized features were inputted into a random forest (RF) classifier to discriminate promotor sequences from non-promotor sequences in A. tumefaciens strain C58. The examination of 10-fold cross-validation showed that the proposed model could yield an overall accuracy of 0.837. This model will provide help for the study of promoters in A. tumefaciens C58 strain.
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Affiliation(s)
- Hasan Zulfiqar
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zahoor Ahmed
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China
| | - Bakanina Kissanga Grace-Mercure
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Farwa Hassan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhao-Yue Zhang
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fen Liu
- Department of Radiation Oncology, Peking University Cancer Hospital (Inner Mongolia Campus), Affiliated Cancer Hospital of Inner Mongolia Medical University, Inner Mongolia Cancer Hospital, Hohhot, China
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8
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Zulfiqar H, Guo Z, Grace-Mercure BK, Zhang ZY, Gao H, Lin H, Wu Y. Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods. Comput Struct Biotechnol J 2023; 21:2253-2261. [PMID: 37035551 PMCID: PMC10073991 DOI: 10.1016/j.csbj.2023.03.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 03/15/2023] [Accepted: 03/16/2023] [Indexed: 03/19/2023] Open
Abstract
Hormone binding proteins (HBPs) belong to the group of soluble carrier proteins. These proteins selectively and non-covalently interact with hormones and promote growth hormone signaling in human and other animals. The HBPs are useful in many medical and commercial fields. Thus, the identification of HBPs is very important because it can help to discover more details about hormone binding proteins. Meanwhile, the experimental methods are time-consuming and expensive for hormone binding proteins recognition. Computational prediction methods have played significant roles in the correct recognition of hormone binding proteins with the use of sequence information and ML algorithms. In this review, we compared and assessed the implementation of ML-based tools in recognition of HBPs in a unique way. We hope that this study will give enough awareness and knowledge for research on HBPs.
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Affiliation(s)
- Hasan Zulfiqar
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang 313001, China
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
- School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhiling Guo
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Bakanina Kissanga Grace-Mercure
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhao-Yue Zhang
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Gao
- School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang 313001, China
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yun Wu
- College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
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9
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Wang H, Zhang Z, Li H, Li J, Li H, Liu M, Liang P, Xi Q, Xing Y, Yang L, Zuo Y. A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery. Cell Biosci 2023; 13:41. [PMID: 36849879 PMCID: PMC9972636 DOI: 10.1186/s13578-023-00991-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 02/15/2023] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND The placenta, as a unique exchange organ between mother and fetus, is essential for successful human pregnancy and fetal health. Preeclampsia (PE) caused by placental dysfunction contributes to both maternal and infant morbidity and mortality. Accurate identification of PE patients plays a vital role in the formulation of treatment plans. However, the traditional clinical methods of PE have a high misdiagnosis rate. RESULTS Here, we first designed a computational biology method that used single-cell transcriptome (scRNA-seq) of healthy pregnancy (38 wk) and early-onset PE (28-32 wk) to identify pathological cell subpopulations and predict PE risk. Based on machine learning methods and feature selection techniques, we observed that the Tuning ReliefF (TURF) score hybrid with XGBoost (TURF_XGB) achieved optimal performance, with 92.61% accuracy and 92.46% recall for classifying nine cell subpopulations of healthy placentas. Biological landscapes of placenta heterogeneity could be mapped by the 110 marker genes screened by TURF_XGB, which revealed the superiority of the TURF feature mining. Moreover, we processed the PE dataset with LASSO to obtain 497 biomarkers. Integration analysis of the above two gene sets revealed that dendritic cells were closely associated with early-onset PE, and C1QB and C1QC might drive preeclampsia by mediating inflammation. In addition, an ensemble model-based risk stratification card was developed to classify preeclampsia patients, and its area under the receiver operating characteristic curve (AUC) could reach 0.99. For broader accessibility, we designed an accessible online web server ( http://bioinfor.imu.edu.cn/placenta ). CONCLUSION Single-cell transcriptome-based preeclampsia risk assessment using an ensemble machine learning framework is a valuable asset for clinical decision-making. C1QB and C1QC may be involved in the development and progression of early-onset PE by affecting the complement and coagulation cascades pathway that mediate inflammation, which has important implications for better understanding the pathogenesis of PE.
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Affiliation(s)
- Hao Wang
- grid.411643.50000 0004 1761 0411The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070 China ,Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd., Hohhot, 010010 China
| | - Zhaoyue Zhang
- grid.54549.390000 0004 0369 4060School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054 China
| | - Haicheng Li
- grid.411643.50000 0004 1761 0411The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070 China ,Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd., Hohhot, 010010 China
| | - Jinzhao Li
- grid.411643.50000 0004 1761 0411The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070 China
| | - Hanshuang Li
- grid.411643.50000 0004 1761 0411The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070 China
| | - Mingzhu Liu
- grid.411643.50000 0004 1761 0411The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070 China ,Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd., Hohhot, 010010 China
| | - Pengfei Liang
- grid.411643.50000 0004 1761 0411The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070 China
| | - Qilemuge Xi
- grid.411643.50000 0004 1761 0411The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070 China
| | - Yongqiang Xing
- School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Yongchun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China. .,Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd., Hohhot, 010010, China.
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10
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Su W, Xie XQ, Liu XW, Gao D, Ma CY, Zulfiqar H, Yang H, Lin H, Yu XL, Li YW. iRNA-ac4C: A novel computational method for effectively detecting N4-acetylcytidine sites in human mRNA. Int J Biol Macromol 2023; 227:1174-1181. [PMID: 36470433 DOI: 10.1016/j.ijbiomac.2022.11.299] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/10/2022] [Accepted: 11/25/2022] [Indexed: 12/07/2022]
Abstract
RNA N4-acetylcytidine (ac4C) is the acetylation of cytidine at the nitrogen-4 position, which is a highly conserved RNA modification and involves a variety of biological processes. Hence, accurate identification of genome-wide ac4C sites is vital for understanding regulation mechanism of gene expression. In this work, a novel predictor, named iRNA-ac4C, was established to identify ac4C sites in human mRNA based on three feature extraction methods, including nucleotide composition, nucleotide chemical property, and accumulated nucleotide frequency. Subsequently, minimum-Redundancy-Maximum-Relevance combined with incremental feature selection strategies was utilized to select the optimal feature subset. According to the optimal feature subset, the best ac4C classification model was trained by gradient boosting decision tree with 10-fold cross-validation. The results of independent testing set indicated that our proposed method could produce encouraging generalization capabilities. For the convenience of other researchers, we established a user-friendly web server which is freely available at http://lin-group.cn/server/iRNA-ac4C/. We hope that the tool could provide guide for wet-experimental scholars.
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Affiliation(s)
- Wei Su
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xue-Qin Xie
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiao-Wei Liu
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Dong Gao
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Cai-Yi Ma
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hasan Zulfiqar
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hui Yang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hao Lin
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Xiao-Long Yu
- School of Materials Science and Engineering, Hainan University, Haikou 570228, China.
| | - Yan-Wen Li
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China; Key Laboratory of Intelligent Information Processing of Jilin Province, Northeast Normal University, Changchun 130117, China; Institute of Computational Biology, Northeast Normal University, Changchun 130117, China.
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11
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Zhang YF, Wang YH, Gu ZF, Pan XR, Li J, Ding H, Zhang Y, Deng KJ. Bitter-RF: A random forest machine model for recognizing bitter peptides. Front Med (Lausanne) 2023; 10:1052923. [PMID: 36778738 PMCID: PMC9909039 DOI: 10.3389/fmed.2023.1052923] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 01/05/2023] [Indexed: 01/27/2023] Open
Abstract
Introduction Bitter peptides are short peptides with potential medical applications. The huge potential behind its bitter taste remains to be tapped. To better explore the value of bitter peptides in practice, we need a more effective classification method for identifying bitter peptides. Methods In this study, we developed a Random forest (RF)-based model, called Bitter-RF, using sequence information of the bitter peptide. Bitter-RF covers more comprehensive and extensive information by integrating 10 features extracted from the bitter peptides and achieves better results than the latest generation model on independent validation set. Results The proposed model can improve the accurate classification of bitter peptides (AUROC = 0.98 on independent set test) and enrich the practical application of RF method in protein classification tasks which has not been used to build a prediction model for bitter peptides. Discussion We hope the Bitter-RF could provide more conveniences to scholars for bitter peptide research.
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Affiliation(s)
- Yu-Fei Zhang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu-Hao Wang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhi-Feng Gu
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xian-Run Pan
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jian Li
- School of Basic Medical Sciences, Chengdu University, Chengdu, China
| | - Hui Ding
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ke-Jun Deng
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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12
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Li H, Zhang J, Zhao Y, Yang W. Predicting Corynebacterium glutamicum promoters based on novel feature descriptor and feature selection technique. Front Microbiol 2023; 14:1141227. [PMID: 36937275 PMCID: PMC10018189 DOI: 10.3389/fmicb.2023.1141227] [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: 01/10/2023] [Accepted: 02/10/2023] [Indexed: 03/06/2023] Open
Abstract
The promoter is an important noncoding DNA regulatory element, which combines with RNA polymerase to activate the expression of downstream genes. In industry, artificial arginine is mainly synthesized by Corynebacterium glutamicum. Replication of specific promoter regions can increase arginine production. Therefore, it is necessary to accurately locate the promoter in C. glutamicum. In the wet experiment, promoter identification depends on sigma factors and DNA splicing technology, this is a laborious job. To quickly and conveniently identify the promoters in C. glutamicum, we have developed a method based on novel feature representation and feature selection to complete this task, describing the DNA sequences through statistical parameters of multiple physicochemical properties, filtering redundant features by combining analysis of variance and hierarchical clustering, the prediction accuracy of the which is as high as 91.6%, the sensitivity of 91.9% can effectively identify promoters, and the specificity of 91.2% can accurately identify non-promoters. In addition, our model can correctly identify 181 promoters and 174 non-promoters among 400 independent samples, which proves that the developed prediction model has excellent robustness.
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Affiliation(s)
- HongFei Li
- College of Life Science, Northeast Forestry University, Harbin, China
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Jingyu Zhang
- Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yuming Zhao
- College of Life Science, Northeast Forestry University, Harbin, China
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
- *Correspondence: Yuming Zhao, ; Wen Yang,
| | - Wen Yang
- International Medical Center, Shenzhen University General Hospital, Shenzhen, China
- *Correspondence: Yuming Zhao, ; Wen Yang,
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13
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Ao C, Jiao S, Wang Y, Yu L, Zou Q. Biological Sequence Classification: A Review on Data and General Methods. RESEARCH (WASHINGTON, D.C.) 2022; 2022:0011. [PMID: 39285948 PMCID: PMC11404319 DOI: 10.34133/research.0011] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 10/25/2022] [Indexed: 09/19/2024]
Abstract
With the rapid development of biotechnology, the number of biological sequences has grown exponentially. The continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence information. There are many branches of biological sequence classification research. In this review, we mainly focus on the function and modification classification of biological sequences based on machine learning. Sequence-based prediction and analysis are the basic tasks to understand the biological functions of DNA, RNA, proteins, and peptides. However, there are hundreds of classification models developed for biological sequences, and the quite varied specific methods seem dizzying at first glance. Here, we aim to establish a long-term support website (http://lab.malab.cn/~acy/BioseqData/home.html), which provides readers with detailed information on the classification method and download links to relevant datasets. We briefly introduce the steps to build an effective model framework for biological sequence data. In addition, a brief introduction to single-cell sequencing data analysis methods and applications in biology is also included. Finally, we discuss the current challenges and future perspectives of biological sequence classification research.
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Affiliation(s)
- Chunyan Ao
- School of Computer Science and Technology, Xidian University, Xi'an, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Shihu Jiao
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Yansu Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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14
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Identification of adaptor proteins using the ANOVA feature selection technique. Methods 2022; 208:42-47. [DOI: 10.1016/j.ymeth.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/01/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
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15
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Xiao J, Liu M, Huang Q, Sun Z, Ning L, Duan J, Zhu S, Huang J, Lin H, Yang H. Analysis and modeling of myopia-related factors based on questionnaire survey. Comput Biol Med 2022; 150:106162. [PMID: 36252365 DOI: 10.1016/j.compbiomed.2022.106162] [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: 07/20/2022] [Revised: 09/12/2022] [Accepted: 10/01/2022] [Indexed: 11/03/2022]
Abstract
With the rapid development of science and technology, the trend of low age myopia is becoming increasingly significant. The latest national survey done by the Chinese government found that more than 80% of Chinese teenagers suffer from myopia. Adolescent myopia is closely related to living environment, heredity, and living habits. Quantifying the relationship between myopia and living environment, heredity, and living habits is conductive to the prevention and intervention of adolescent myopia. In this study, we investigated the relationships between four main factors (environment, habits, parental vision, and demographic) and myopia status by analyzing the questionnaire data. Data were collected from Chengdu, China in 2021, including 2808 myopia samples and 5693 non-myopia samples, with a total of 22 features. Then, these 22 features were inputted into three machine learning algorithms to discriminate the two classes of samples. Results show that the computational model could produce an AUC of 0.768. To pick out the most important features which play important roles in classification, we used incremental feature selection strategy to screen the 22 features. As a result, we found that the 4 most influential features with XGBoost could achieve a competitive AUC of 0.764. To further investigate the risk and protective factors affecting adolescent myopia, we used OR values derived from MLE-LR to analyze the relationship between 22 features and adolescent myopia. Results showed that the age variable was the most significant risk factor for myopia, followed by the myopia status of parents. The most protective factor for eyesight is the measure taken by the children, followed by the distance between books and eyes when reading. These discoveries can guide the prevention and control of myopia in children and adolescents.
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Affiliation(s)
- Jianqiang Xiao
- Eye School, Chengdu University of Traditional Chinese Medicine, Ineye Hospital of Chengdu University of TCM, China
| | - Mujiexin Liu
- Eye School, Chengdu University of Traditional Chinese Medicine, Ineye Hospital of Chengdu University of TCM, China
| | - Qinlai Huang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Zijie Sun
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, 611844, China
| | - Junguo Duan
- Eye School, Chengdu University of Traditional Chinese Medicine, Ineye Hospital of Chengdu University of TCM, China
| | - Siquan Zhu
- Eye School, Chengdu University of Traditional Chinese Medicine, Ineye Hospital of Chengdu University of TCM, China
| | - Jian Huang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Hao Lin
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Hui Yang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Computer Science, Chengdu University of Information Technology, Chengdu, 611844, China.
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16
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Meng J, Zuo Z, Lee TY, Liu Z, Huang Y. Bioinformatics resources for understanding RNA modifications. Methods 2022; 206:53-55. [PMID: 35988901 DOI: 10.1016/j.ymeth.2022.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022] Open
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17
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Hu RS, Wu J, Zhang L, Zhou X, Zhang Y. CD8TCEI-EukPath: A Novel Predictor to Rapidly Identify CD8+ T-Cell Epitopes of Eukaryotic Pathogens Using a Hybrid Feature Selection Approach. Front Genet 2022; 13:935989. [PMID: 35937988 PMCID: PMC9354802 DOI: 10.3389/fgene.2022.935989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 05/24/2022] [Indexed: 12/02/2022] Open
Abstract
Computational prediction to screen potential vaccine candidates has been proven to be a reliable way to provide guarantees for vaccine discovery in infectious diseases. As an important class of organisms causing infectious diseases, pathogenic eukaryotes (such as parasitic protozoans) have evolved the ability to colonize a wide range of hosts, including humans and animals; meanwhile, protective vaccines are urgently needed. Inspired by the immunological idea that pathogen-derived epitopes are able to mediate the CD8+ T-cell-related host adaptive immune response and with the available positive and negative CD8+ T-cell epitopes (TCEs), we proposed a novel predictor called CD8TCEI-EukPath to detect CD8+ TCEs of eukaryotic pathogens. Our method integrated multiple amino acid sequence-based hybrid features, employed a well-established feature selection technique, and eventually built an efficient machine learning classifier to differentiate CD8+ TCEs from non-CD8+ TCEs. Based on the feature selection results, 520 optimal hybrid features were used for modeling by utilizing the LightGBM algorithm. CD8TCEI-EukPath achieved impressive performance, with an accuracy of 79.255% in ten-fold cross-validation and an accuracy of 78.169% in the independent test. Collectively, CD8TCEI-EukPath will contribute to rapidly screening epitope-based vaccine candidates, particularly from large peptide-coding datasets. To conduct the prediction of CD8+ TCEs conveniently, an online web server is freely accessible (http://lab.malab.cn/∼hrs/CD8TCEI-EukPath/).
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Affiliation(s)
- Rui-Si Hu
- Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Quzhou, China
| | - Jin Wu
- School of Management, Shenzhen Polytechnic, Shenzhen, China
| | - Lichao Zhang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Xun Zhou
- Beidahuang Industry Group General Hospital, Harbin, China
- *Correspondence: Xun Zhou, ; Ying Zhang,
| | - Ying Zhang
- Department of Anesthesiology, Hospital (T.C.M) Affiliated of Southwest Medical University, Luzhou, China
- *Correspondence: Xun Zhou, ; Ying Zhang,
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18
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Liu P, Ding Y, Rong Y, Chen D. Prediction of cell penetrating peptides and their uptake efficiency using random forest‐based feature selections. AIChE J 2022. [DOI: 10.1002/aic.17781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Peng Liu
- Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China
- Institute of Yangtze Delta Region (Quzhou) University of Electronic Science and Technology of China Quzhou China
| | - Yijie Ding
- Institute of Yangtze Delta Region (Quzhou) University of Electronic Science and Technology of China Quzhou China
| | - Ying Rong
- Beidahuang Industry Group General Hospital Harbin China
| | - Dong Chen
- College of Electrical and Information Engineering, Quzhou University Quzhou China
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19
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Chung Kim Chung K, Mahdavi-Amiri Y, Korfmann C, Hili R. PhOxi-Seq: Single-Nucleotide Resolution Sequencing of N2-Methylation at Guanosine in RNA by Photoredox Catalysis. J Am Chem Soc 2022; 144:5723-5727. [PMID: 35316019 DOI: 10.1021/jacs.2c00670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Chemical modifications regulate the fate and function of cellular RNAs. Newly developed sequencing methods have allowed a deeper understanding of the biological role of RNA modifications; however, the vast majority of post-transcriptional modifications lack a well-defined sequencing method. Here, we report a photo-oxidative sequencing (PhOxi-seq) approach for guanosine N2-methylation, a common methylation mark seen in N2-methylguanosine (m2G) and N2,N2-dimethylguanosine (m22G). Using visible light-mediated organic photoredox catalysis, m2G and m22G are chemoselectively oxidized in the presence of canonical RNA nucleosides, which results in a strong mutation signature observed during sequencing. PhOxi-seq was demonstrated on various tRNAs and rRNA to reveal N2-methylation with excellent response and markedly improved read-through at m22G sites.
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Affiliation(s)
- Kimberley Chung Kim Chung
- Department of Chemistry and Centre for Research on Biomolecular Interactions, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
| | - Yasaman Mahdavi-Amiri
- Department of Chemistry and Centre for Research on Biomolecular Interactions, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
| | - Christopher Korfmann
- Department of Chemistry and Centre for Research on Biomolecular Interactions, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
| | - Ryan Hili
- Department of Chemistry and Centre for Research on Biomolecular Interactions, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
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20
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Ma D, Chen Z, He Z, Huang X. A SNARE Protein Identification Method Based on iLearnPlus to Efficiently Solve the Data Imbalance Problem. Front Genet 2022; 12:818841. [PMID: 35154261 PMCID: PMC8832978 DOI: 10.3389/fgene.2021.818841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 12/14/2021] [Indexed: 11/13/2022] Open
Abstract
Machine learning has been widely used to solve complex problems in engineering applications and scientific fields, and many machine learning-based methods have achieved good results in different fields. SNAREs are key elements of membrane fusion and required for the fusion process of stable intermediates. They are also associated with the formation of some psychiatric disorders. This study processes the original sequence data with the synthetic minority oversampling technique (SMOTE) to solve the problem of data imbalance and produces the most suitable machine learning model with the iLearnPlus platform for the identification of SNARE proteins. Ultimately, a sensitivity of 66.67%, specificity of 93.63%, accuracy of 91.33%, and MCC of 0.528 were obtained in the cross-validation dataset, and a sensitivity of 66.67%, specificity of 93.63%, accuracy of 91.33%, and MCC of 0.528 were obtained in the independent dataset (the adaptive skip dipeptide composition descriptor was used for feature extraction, and LightGBM with proper parameters was used as the classifier). These results demonstrate that this combination can perform well in the classification of SNARE proteins and is superior to other methods.
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21
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Zulfiqar H, Huang QL, Lv H, Sun ZJ, Dao FY, Lin H. Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique. Int J Mol Sci 2022; 23:1251. [PMID: 35163174 PMCID: PMC8836036 DOI: 10.3390/ijms23031251] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/19/2022] [Accepted: 01/20/2022] [Indexed: 12/15/2022] Open
Abstract
4mC is a type of DNA alteration that has the ability to synchronize multiple biological movements, for example, DNA replication, gene expressions, and transcriptional regulations. Accurate prediction of 4mC sites can provide exact information to their hereditary functions. The purpose of this study was to establish a robust deep learning model to recognize 4mC sites in Geobacter pickeringii. In the anticipated model, two kinds of feature descriptors, namely, binary and k-mer composition were used to encode the DNA sequences of Geobacter pickeringii. The obtained features from their fusion were optimized by using correlation and gradient-boosting decision tree (GBDT)-based algorithm with incremental feature selection (IFS) method. Then, these optimized features were inserted into 1D convolutional neural network (CNN) to classify 4mC sites from non-4mC sites in Geobacter pickeringii. The performance of the anticipated model on independent data exhibited an accuracy of 0.868, which was 4.2% higher than the existing model.
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Affiliation(s)
| | | | | | | | | | - Hao Lin
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; (H.Z.); (Q.-L.H.); (H.L.); (Z.-J.S.); (F.-Y.D.)
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22
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Zhai Y, Zhang J, Zhang T, Gong Y, Zhang Z, Zhang D, Zhao Y. AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs. Front Pharmacol 2022; 12:818115. [PMID: 35115948 PMCID: PMC8803896 DOI: 10.3389/fphar.2021.818115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 12/20/2021] [Indexed: 11/18/2022] Open
Abstract
Antioxidant proteins can not only balance the oxidative stress in the body, but are also an important component of antioxidant drugs. Accurate identification of antioxidant proteins is essential to help humans fight diseases and develop new drugs. In this paper, we developed a friendly method AOPM to identify antioxidant proteins. 188D and the Composition of k-spaced Amino Acid Pairs were adopted as the feature extraction method. In addition, the Max-Relevance-Max-Distance algorithm (MRMD) and random forest were the feature selection and classifier, respectively. We used 5-folds cross-validation and independent test dataset to evaluate our model. On the test dataset, AOPM presented a higher performance compared with the state-of-the-art methods. The sensitivity, specificity, accuracy, Matthew’s Correlation Coefficient and an Area Under the Curve reached 87.3, 94.2, 92.0%, 0.815 and 0.972, respectively. In addition, AOPM still has excellent performance in predicting the catalytic enzymes of antioxidant drugs. This work proved the feasibility of virtual drug screening based on sequence information and provided new ideas and solutions for drug development.
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Affiliation(s)
- Yixiao Zhai
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Jingyu Zhang
- Department of Neurology, the Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianjiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Yue Gong
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Zixiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Dandan Zhang
- Department of Obstetrics and Gynecology, the First Affiliated Hospital of Harbin Medical University, Harbin, China
- *Correspondence: Dandan Zhang, ; Yuming Zhao,
| | - Yuming Zhao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
- *Correspondence: Dandan Zhang, ; Yuming Zhao,
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23
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Lin C, Wang L, Shi L. AAPred-CNN: accurate predictor based on deep convolution neural network for identification of anti-angiogenic peptides. Methods 2022; 204:442-448. [PMID: 35031486 DOI: 10.1016/j.ymeth.2022.01.004] [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: 11/24/2021] [Revised: 12/28/2021] [Accepted: 01/09/2022] [Indexed: 12/13/2022] Open
Abstract
Recently, deep learning techniques have been developed for various bioactive peptide prediction tasks. However, there are only conventional machine learning-based methods for the prediction of anti-angiogenic peptides (AAP), which play an important role in cancer treatment. The main reason why no deep learning method has been involved in this field is that there are too few experimentally validated AAPs to support the training of deep models but researchers have believed that deep learning seriously depends on the amounts of labeled data. In this paper, as a tentative work, we try to predict AAP by constructing different classical deep learning models and propose the first deep convolution neural network-based predictor (AAPred-CNN) for AAP. Contrary to intuition, the experimental results show that deep learning models can achieve superior or comparable performance to the state-of-the-art model, although they are given a few labeled sequences to train. We also decipher the influence of hyper-parameters and training samples on the performance of deep learning models to help understand how the model work. Furthermore, we also visualize the learned embeddings by dimension reduction to increase the model interpretability and reveal the residue propensity of AAP through the statistics of convolutional features for different residues. In summary, this work demonstrates the powerful representation ability of AAPred-CNNfor AAP prediction, further improving the prediction accuracy of AAP.
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Affiliation(s)
- Changhang Lin
- School of Big Data and Artificial Intelligence, Fujian Polytechnic Normal University, Fuzhou, China
| | - Lei Wang
- Beidahuang Industry Group General Hospital, Harbin, China.
| | - Lei Shi
- Department of Spine Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
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24
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Li H, Shi L, Gao W, Zhang Z, Zhang L, Wang G. dPromoter-XGBoost: Detecting promoters and strength by combining multiple descriptors and feature selection using XGBoost. Methods 2022; 204:215-222. [PMID: 34998983 DOI: 10.1016/j.ymeth.2022.01.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 12/13/2021] [Accepted: 01/02/2022] [Indexed: 12/12/2022] Open
Abstract
Promoters play an irreplaceable role in biological processes and genetics, which are responsible for stimulating the transcription and expression of specific genes. Promoter abnormalities have been found in some diseases, and the level of promoter-binding transcription factors can be used as a marker before a disease occurs. Hence, detecting promoters from DNA sequences has important biological significance, particular, distinguishing strong promoters can help to elucidate differences in gene expression and the mechanisms of specific diseases. With the introduction of third-generation sequencing, it is difficult to match the speed of sequencing to the speed of labeling promoters experimentally. Many computing models have been designed to fill this gap and identify unlabeled DNA. However, their feature representation methods are very singular, which cannot reflect the information contained in the original samples. With the aim of avoiding information loss, we propose a computational model based on multiple descriptors and feature selection to jointly express samples. It is worth mentioning that a new feature descriptor called K-mer word vector is defined. The promoter model of multiple feature descriptors dominated by K-mer word vector achieves similar performance to existing methods, the sensitivity of 85.72% can distinguish the promoter more effectively than other methods. Furthermore, the performance of the promoter strength has surpassed published methods, and accuracy of 77.00% greatly improves the ability to distinguish between strong and weak promoters.
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Affiliation(s)
- Hongfei Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China; Yangtze Delta Region Institute, University of Electronic Science and Technology, Quzhou,China
| | - Lei Shi
- Department of Spine Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Wentao Gao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Zixiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Lichao Zhang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Guohua Wang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China.
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25
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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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26
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Gu X, Guo L, Liao B, Jiang Q. Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D. Front Genet 2021; 12:796327. [PMID: 34925468 PMCID: PMC8672092 DOI: 10.3389/fgene.2021.796327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 11/15/2021] [Indexed: 11/13/2022] Open
Abstract
Phages have seriously affected the biochemical systems of the world, and not only are phages related to our health, but medical treatments for many cancers and skin infections are related to phages; therefore, this paper sought to identify phage proteins. In this paper, a Pseudo-188D model was established. The digital features of the phage were extracted by PseudoKNC, an appropriate vector was selected by the AdaBoost tool, and features were extracted by 188D. Then, the extracted digital features were combined together, and finally, the viral proteins of the phage were predicted by a stochastic gradient descent algorithm. Our model effect reached 93.4853%. To verify the stability of our model, we randomly selected 80% of the downloaded data to train the model and used the remaining 20% of the data to verify the robustness of our model.
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Affiliation(s)
- Xiaomei Gu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Institute of Yangtze River Delta, University of Electronic Science and Technology of China, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Lina Guo
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Bo Liao
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Qinghua Jiang
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
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27
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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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28
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Gong Y, Liao B, Wang P, Zou Q. DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins. Front Pharmacol 2021; 12:771808. [PMID: 34916947 PMCID: PMC8669608 DOI: 10.3389/fphar.2021.771808] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 11/15/2021] [Indexed: 01/09/2023] Open
Abstract
Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has become a research hotspot. The key to the research and development of modern new drugs is first to identify potential drug targets. In this paper, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins. This method combines the three features of monoDiKGap (k = 2), cross-covariance, and grouped amino acid composition. It removes redundant features and analyses key features through MRMD and MRMD2.0. The cross-validation results show that 96.9944% of the potentially druggable proteins can be accurately identified, and the accuracy of the independent test set has reached 96.5665%. This all means that DrugHybrid_BS has the potential to become a useful predictive tool for druggable proteins. In addition, the hybrid key features can identify 80.0343% of the potentially druggable proteins combined with Bagging-SVM, which indicates the significance of this part of the features for research.
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Affiliation(s)
- Yuxin Gong
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, China
| | - Bo Liao
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, China
| | - Peng Wang
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
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29
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Jia Y, Huang S, Zhang T. KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest. Front Genet 2021; 12:811158. [PMID: 34912382 PMCID: PMC8667860 DOI: 10.3389/fgene.2021.811158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 11/15/2021] [Indexed: 02/04/2023] Open
Abstract
DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.
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Affiliation(s)
- Yuran Jia
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianjiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
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