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Zulfiqar H, Guo Z, Ahmad RM, Ahmed Z, Cai P, Chen X, Zhang Y, Lin H, Shi Z. Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings. Front Med (Lausanne) 2024; 10:1291352. [PMID: 38298505 PMCID: PMC10829051 DOI: 10.3389/fmed.2023.1291352] [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: 09/09/2023] [Accepted: 12/26/2023] [Indexed: 02/02/2024] Open
Abstract
Snake venom contains many toxic proteins that can destroy the circulatory system or nervous system of prey. Studies have found that these snake venom proteins have the potential to treat cardiovascular and nervous system diseases. Therefore, the study of snake venom protein is conducive to the development of related drugs. The research technologies based on traditional biochemistry can accurately identify these proteins, but the experimental cost is high and the time is long. Artificial intelligence technology provides a new means and strategy for large-scale screening of snake venom proteins from the perspective of computing. In this paper, we developed a sequence-based computational method to recognize snake toxin proteins. Specially, we utilized three different feature descriptors, namely g-gap, natural vector and word 2 vector, to encode snake toxin protein sequences. The analysis of variance (ANOVA), gradient-boost decision tree algorithm (GBDT) combined with incremental feature selection (IFS) were used to optimize the features, and then the optimized features were input into the deep learning model for model training. The results show that our model can achieve a prediction performance with an accuracy of 82.00% in 10-fold cross-validation. The model is further verified on independent data, and the accuracy rate reaches to 81.14%, which demonstrated that our model has excellent prediction performance and robustness.
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Affiliation(s)
- Hasan Zulfiqar
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China
| | - Zhiling Guo
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Ramala Masood Ahmad
- Department of Plant Breeding and Genetics, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Zahoor Ahmed
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China
| | - Peiling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, China
| | - Xiang Chen
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hao Lin
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China
| | - Zheng Shi
- Clinical Genetics Laboratory, Clinical Medical College & Affiliated Hospital, Chengdu University, Chengdu, China
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2
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Zulfiqar H, Ahmad RM, Raza A, Shahzad S, Lin H. Promoter Prediction in Agrobacterium tumefaciens Strain C58 by Using Artificial Intelligence Strategies. Methods Mol Biol 2024; 2844:33-44. [PMID: 39068330 DOI: 10.1007/978-1-0716-4063-0_2] [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] [Indexed: 07/30/2024]
Abstract
Promoters are the genomic regions upstream of genes that RNA polymerase binds in order to initiate gene transcription. Understanding the regulation of gene expression depends on being able to identify promoters, because they are the most important component of gene expression. Agrobacterium tumefaciens (A. tumefaciens) strain C58 was the subject of this study with the goal of creating a machine learning-based model to predict promoters. In this study, nucleotide density (ND), k-mer, and one-hot were used to encode the promoter sequence. Support vector machine (SVM) on fivefold cross-validation with incremental feature selection (IFS) was used to optimize the generated features. These improved characteristics were then used to distinguish promoter sequences by feeding them into the random forest (RF) classifier. Tenfold cross-validation (CV) analysis revealed that the projected model has the ability to produce an accuracy of 84.22%.
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Affiliation(s)
- Hasan Zulfiqar
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China.
| | - Ramala Masood Ahmad
- Department of Plant Breeding and Genetics, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Ali Raza
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China
| | - Sana Shahzad
- Institute of Horticultural Sciences, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Hao Lin
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China.
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3
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Yin ZN, Lai FL, Gao F. Unveiling human origins of replication using deep learning: accurate prediction and comprehensive analysis. Brief Bioinform 2023; 25:bbad432. [PMID: 38008420 PMCID: PMC10676776 DOI: 10.1093/bib/bbad432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/11/2023] [Accepted: 11/06/2023] [Indexed: 11/28/2023] Open
Abstract
Accurate identification of replication origins (ORIs) is crucial for a comprehensive investigation into the progression of human cell growth and cancer therapy. Here, we proposed a computational approach Ori-FinderH, which can efficiently and precisely predict the human ORIs of various lengths by combining the Z-curve method with deep learning approach. Compared with existing methods, Ori-FinderH exhibits superior performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.9616 for K562 cell line in 10-fold cross-validation. In addition, we also established a cross-cell-line predictive model, which yielded a further improved AUC of 0.9706. The model was subsequently employed as a fitness function to support genetic algorithm for generating artificial ORIs. Sequence analysis through iORI-Euk revealed that a vast majority of the created sequences, specifically 98% or more, incorporate at least one ORI for three cell lines (Hela, MCF7 and K562). This innovative approach could provide more efficient, accurate and comprehensive information for experimental investigation, thereby further advancing the development of this field.
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Affiliation(s)
- Zhen-Ning Yin
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
| | - Fei-Liao Lai
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
| | - Feng Gao
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300072, China
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4
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Abbas Z, Rehman MU, Tayara H, Chong KT. ORI-Explorer: a unified cell-specific tool for origin of replication sites prediction by feature fusion. Bioinformatics 2023; 39:btad664. [PMID: 37929975 PMCID: PMC10639035 DOI: 10.1093/bioinformatics/btad664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/20/2023] [Accepted: 10/27/2023] [Indexed: 11/07/2023] Open
Abstract
MOTIVATION The origins of replication sites (ORIs) are precise regions inside the DNA sequence where the replication process begins. These locations are critical for preserving the genome's integrity during cell division and guaranteeing the faithful transfer of genetic data from generation to generation. The advent of experimental techniques has aided in the discovery of ORIs in many species. Experimentation, on the other hand, is often more time-consuming and pricey than computational approaches, and it necessitates specific equipment and knowledge. Recently, ORI sites have been predicted using computational techniques like motif-based searches and artificial intelligence algorithms based on sequence characteristics and chromatin states. RESULTS In this article, we developed ORI-Explorer, a unique artificial intelligence-based technique that combines multiple feature engineering techniques to train CatBoost Classifier for recognizing ORIs from four distinct eukaryotic species. ORI-Explorer was created by utilizing a unique combination of three traditional feature-encoding techniques and a feature set obtained from a deep-learning neural network model. The ORI-Explorer has significantly outperformed current predictors on the testing dataset. Furthermore, by employing the sophisticated SHapley Additive exPlanation method, we give crucial insights that aid in comprehending model success, highlighting the most relevant features vital for forecasting cell-specific ORIs. ORI-Explorer is also intended to aid community-wide attempts in discovering potential ORIs and developing innovative verifiable biological hypotheses. AVAILABILITY AND IMPLEMENTATION The used datasets along with the source code are made available through https://github.com/Z-Abbas/ORI-Explorer and https://zenodo.org/record/8358679.
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Affiliation(s)
- Zeeshan Abbas
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea
| | - Mobeen Ur Rehman
- Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi, United Arab Emirates
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, South Korea
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea
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5
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Wang C, Zou Q, Ju Y, Shi H. Enhancer-FRL: Improved and Robust Identification of Enhancers and Their Activities Using Feature Representation Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:967-975. [PMID: 36063523 DOI: 10.1109/tcbb.2022.3204365] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Enhancers are crucial for precise regulation of gene expression, while enhancer identification and strength prediction are challenging because of their free distribution and tremendous number of similar fractions in the genome. Although several bioinformatics tools have been developed, shortfalls in these models remain, and their performances need further improvement. In the present study, a two-layer predictor called Enhancer-FRL was proposed for identifying enhancers (enhancers or nonenhancers) and their activities (strong and weak). More specifically, to build an efficient model, the feature representation learning scheme was applied to generate a 50D probabilistic vector based on 10 feature encodings and five machine learning algorithms. Subsequently, the multiview probabilistic features were integrated to construct the final prediction model. Compared with the single feature-based model, Enhancer-FRL showed significant performance improvement and model robustness. Performance assessment on the independent test dataset indicated that the proposed model outperformed state-of-the-art available toolkits. The webserver Enhancer-FRL is freely accessible at http://lab.malab.cn/∼wangchao/softwares/Enhancer-FRL/, The code and datasets can be downloaded at the webserver page or at the Github https://github.com/wangchao-malab/Enhancer-FRL/.
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6
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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] [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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7
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Dao FY, Liu ML, Su W, Lv H, Zhang ZY, Lin H, Liu L. AcrPred: A hybrid optimization with enumerated machine learning algorithm to predict Anti-CRISPR proteins. Int J Biol Macromol 2023; 228:706-714. [PMID: 36584777 DOI: 10.1016/j.ijbiomac.2022.12.250] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/12/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022]
Abstract
CRISPR-Cas, as a tool for gene editing, has received extensive attention in recent years. Anti-CRISPR (Acr) proteins can inactivate the CRISPR-Cas defense system during interference phase, and can be used as a potential tool for the regulation of gene editing. In-depth study of Anti-CRISPR proteins is of great significance for the implementation of gene editing. In this study, we developed a high-accuracy prediction model based on two-step model fusion strategy, called AcrPred, which could produce an AUC of 0.952 with independent dataset validation. To further validate the proposed model, we compared with published tools and correctly identified 9 of 10 new Acr proteins, indicating the strong generalization ability of our model. Finally, for the convenience of related wet-experimental researchers, a user-friendly web-server AcrPred (Anti-CRISPR proteins Prediction) was established at http://lin-group.cn/server/AcrPred, by which users can easily identify potential Anti-CRISPR proteins.
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Affiliation(s)
- Fu-Ying Dao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; School of Biological Sciences, Nanyang Technological University, Singapore 639798, Singapore
| | - Meng-Lu Liu
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Su
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lv
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; Department of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland; SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Zhao-Yue Zhang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Li Liu
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China.
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8
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Charoenkwan P, Schaduangrat N, Lio P, Moni MA, Chumnanpuen P, Shoombuatong W. iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides. ACS OMEGA 2022; 7:41082-41095. [PMID: 36406571 PMCID: PMC9670693 DOI: 10.1021/acsomega.2c04465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 10/20/2022] [Indexed: 06/16/2023]
Abstract
Antimalarial peptides (AMAPs) varying in length, amino acid composition, charge, conformational structure, hydrophobicity, and amphipathicity reflect their diversity in antimalarial mechanisms. Due to the worldwide major health problem concerning antimicrobial resistance, these peptides possess great therapeutic value owing to their low incidences of drug resistance as compared to conventional antibiotics. Although well-known experimental methods are able to precisely determine the antimalarial activity of peptides, these methods are still time-consuming and costly. Thus, machine learning (ML)-based methods that are capable of identifying AMAPs rapidly by using only sequence information would be beneficial for the high-throughput identification of AMAPs. In this study, we propose the first computational model (termed iAMAP-SCM) for the large-scale identification and characterization of peptides with antimalarial activity by using only sequence information. Specifically, we employed an interpretable scoring card method (SCM) to develop iAMAP-SCM and estimate propensities of 20 amino acids and 400 dipeptides to be AMAPs in a supervised manner. Experimental results showed that iAMAP-SCM could achieve a maximum accuracy and Matthew's coefficient correlation of 0.957 and 0.834, respectively, on the independent test dataset. In addition, SCM-derived propensities of 20 amino acids and selected physicochemical properties were used to provide an understanding of the functional mechanisms of AMAPs. Finally, a user-friendly online computational platform of iAMAP-SCM is publicly available at http://pmlabstack.pythonanywhere.com/iAMAP-SCM. The iAMAP-SCM predictor is anticipated to assist experimental scientists in the high-throughput identification of potential AMAP candidates for the treatment of malaria and other clinical applications.
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Affiliation(s)
- Phasit Charoenkwan
- Modern
Management and Information Technology, College of Arts, Media and
Technology, Chiang Mai University, Chiang Mai50200, Thailand
| | - Nalini Schaduangrat
- Center
of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok10700, Thailand
| | - Pietro Lio
- Department
of Computer Science and Technology, University
of Cambridge, CambridgeshireCB3 0FD, U.K.
| | - Mohammad Ali Moni
- Artificial
Intelligence & Digital Health, School of Health and Rehabilitation
Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St LuciaQLD 4072, Australia
| | - Pramote Chumnanpuen
- Department
of Zoology, Faculty of Science, Kasetsart
University, Bangkok10900, Thailand
- Omics Center
for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok10900, Thailand
| | - Watshara Shoombuatong
- Center
of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok10700, Thailand
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9
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Dao FY, Lv H, Fullwood MJ, Lin H. Accurate Identification of DNA Replication Origin by Fusing Epigenomics and Chromatin Interaction Information. RESEARCH (WASHINGTON, D.C.) 2022; 2022:9780293. [PMID: 36405252 PMCID: PMC9667886 DOI: 10.34133/2022/9780293] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/30/2022] [Indexed: 07/29/2023]
Abstract
DNA replication initiation is a complex process involving various genetic and epigenomic signatures. The correct identification of replication origins (ORIs) could provide important clues for the study of a variety of diseases caused by replication. Here, we design a computational approach named iORI-Epi to recognize ORIs by incorporating epigenome-based features, sequence-based features, and 3D genome-based features. The iORI-Epi displays excellent robustness and generalization ability on both training datasets and independent datasets of K562 cell line. Further experiments confirm that iORI-Epi is highly scalable in other cell lines (MCF7 and HCT116). We also analyze and clarify the regulatory role of epigenomic marks, DNA motifs, and chromatin interaction in DNA replication initiation of eukaryotic genomes. Finally, we discuss gene enrichment pathways from the perspective of ORIs in different replication timing states and heuristically dissect the effect of promoters on replication initiation. Our computational methodology is worth extending to ORI identification in other eukaryotic species.
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Affiliation(s)
- Fu-Ying Dao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
- School of Biological Sciences, Nanyang Technological University, Singapore 639798, Singapore
- Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore 117599, Singapore
| | - Hao Lv
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
- Department of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Melissa J. Fullwood
- School of Biological Sciences, Nanyang Technological University, Singapore 639798, Singapore
- Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore 117599, Singapore
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A∗STAR), Singapore 138673, Singapore
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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10
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Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework. iScience 2022; 25:104883. [PMID: 36046193 PMCID: PMC9421381 DOI: 10.1016/j.isci.2022.104883] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 07/08/2022] [Accepted: 08/02/2022] [Indexed: 11/22/2022] Open
Abstract
Discovery of potential drugs requires rapid and precise identification of drug targets. Although traditional experimental methodologies can accurately identify drug targets, they are time-consuming and inappropriate for high-throughput screening. Computational approaches based on machine learning (ML) algorithms can expedite the prediction of druggable proteins; however, the performance of the existing computational methods remains unsatisfactory. This study proposes a computational tool, SPIDER, to enhance the accurate prediction of druggable proteins. SPIDER employs various feature descriptors pertaining to several aspects, including physicochemical properties, compositional information, and composition-transition-distribution information, coupled with well-known ML algorithms to facilitate the construction of the final meta-predictor. The experimental results showed that SPIDER enabled more precise and robust prediction of druggable proteins than the baseline models and current existing methods in terms of the independent test dataset. An online web server was established and made freely available online. Computational models can expedite the identification of potential druggable proteins SPIDER represents the first stacked model proposed for druggable protein prediction SPIDER enables more precise prediction of druggable proteins than existing methods The SPIDER web server is available at http://pmlabstack.pythonanywhere.com/SPIDER.
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11
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Wang N, Zhang J, Liu B. IDRBP-PPCT: Identifying Nucleic Acid-Binding Proteins Based on Position-Specific Score Matrix and Position-Specific Frequency Matrix Cross Transformation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2284-2293. [PMID: 33780341 DOI: 10.1109/tcbb.2021.3069263] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) are two important nucleic acid-binding proteins (NABPs), which play important roles in biological processes such as replication, translation and transcription of genetic material. Some proteins (DRBPs) bind to both DNA and RNA, also play a key role in gene expression. Identification of DBPs, RBPs and DRBPs is important to study protein-nucleic acid interactions. Computational methods are increasingly being proposed to automatically identify DNA- or RNA-binding proteins based only on protein sequences. One challenge is to design an effective protein representation method to convert protein sequences into fixed-dimension feature vectors. In this study, we proposed a novel protein representation method called Position-Specific Scoring Matrix (PSSM) and Position-Specific Frequency Matrix (PSFM) Cross Transformation (PPCT) to represent protein sequences. This method contains the evolutionary information in PSSM and PSFM, and their correlations. A new computational predictor called IDRBP-PPCT was proposed by combining PPCT and the two-layer framework based on the random forest algorithm to identify DBPs, RBPs and DRBPs. The experimental results on the independent dataset and the tomato genome proved the effectiveness of the proposed method. A user-friendly web-server of IDRBP-PPCT was constructed, which is freely available at http://bliulab.net/IDRBP-PPCT.
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12
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Yao Y, Zhang S, Xue T. Integrating LASSO Feature Selection and Soft Voting Classifier to Identify Origins of Replication Sites. Curr Genomics 2022; 23:83-93. [PMID: 36778978 PMCID: PMC9878833 DOI: 10.2174/1389202923666220214122506] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/11/2021] [Accepted: 01/18/2022] [Indexed: 11/22/2022] Open
Abstract
Background: DNA replication plays an indispensable role in the transmission of genetic information. It is considered to be the basis of biological inheritance and the most fundamental process in all biological life. Considering that DNA replication initiates with a special location, namely the origin of replication, a better and accurate prediction of the origins of replication sites (ORIs) is essential to gain insight into the relationship with gene expression. Objective: In this study, we have developed an efficient predictor called iORI-LAVT for ORIs identification. Methods: This work focuses on extracting feature information from three aspects, including mono-nucleotide encoding, k-mer and ring-function-hydrogen-chemical properties. Subsequently, least absolute shrinkage and selection operator (LASSO) as a feature selection is applied to select the optimal features. Comparing the different combined soft voting classifiers results, the soft voting classifier based on GaussianNB and Logistic Regression is employed as the final classifier. Results: Based on 10-fold cross-validation test, the prediction accuracies of two benchmark datasets are 90.39% and 95.96%, respectively. As for the independent dataset, our method achieves high accuracy of 91.3%. Conclusion: Compared with previous predictors, iORI-LAVT outperforms the existing methods. It is believed that iORI-LAVT predictor is a promising alternative for further research on identifying ORIs.
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Affiliation(s)
- Yingying Yao
- School of Mathematics and Statistics, Xidian University, Xi’an 710071, P.R. China
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi’an 710071, P.R. China,Address correspondence to this author at the School of Mathematics and Statistics, Xidian University, Xi’an 710071, P.R. China; Tel/Fax: +86-29- 88202860; E-mail:
| | - Tian Xue
- School of Mathematics and Statistics, Xidian University, Xi’an 710071, P.R. China
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13
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DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features. Appl Bionics Biomech 2022; 2022:5483115. [PMID: 35465187 PMCID: PMC9020926 DOI: 10.1155/2022/5483115] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/25/2021] [Accepted: 02/05/2022] [Indexed: 12/29/2022] Open
Abstract
In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named “DNAPred_Prot”, which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins.
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14
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Ahmed Z, Zulfiqar H, Khan AA, Gul I, Dao FY, Zhang ZY, Yu XL, Tang L. iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy. Front Microbiol 2022; 13:790063. [PMID: 35273581 PMCID: PMC8902591 DOI: 10.3389/fmicb.2022.790063] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 01/10/2022] [Indexed: 01/20/2023] Open
Abstract
Thermophilic proteins have important application value in biotechnology and industrial processes. The correct identification of thermophilic proteins provides important information for the application of these proteins in engineering. The identification method of thermophilic proteins based on biochemistry is laborious, time-consuming, and high cost. Therefore, there is an urgent need for a fast and accurate method to identify thermophilic proteins. Considering this urgency, we constructed a reliable benchmark dataset containing 1,368 thermophilic and 1,443 non-thermophilic proteins. A multi-layer perceptron (MLP) model based on a multi-feature fusion strategy was proposed to discriminate thermophilic proteins from non-thermophilic proteins. On independent data set, the proposed model could achieve an accuracy of 96.26%, which demonstrates that the model has a good application prospect. In order to use the model conveniently, a user-friendly software package called iThermo was established and can be freely accessed at http://lin-group.cn/server/iThermo/index.html. The high accuracy of the model and the practicability of the developed software package indicate that this study can accelerate the discovery and engineering application of thermally stable proteins.
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Affiliation(s)
- Zahoor Ahmed
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hasan Zulfiqar
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Abdullah Aman Khan
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.,Sichuan Artificial Intelligence Research Institute, Yibin, China
| | - Ijaz Gul
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Tsinghua Shenzhen International Graduate School, Institute of Biopharmaceutical and Health Engineering, Tsinghua University, Shenzhen, China
| | - Fu-Ying Dao
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhao-Yue Zhang
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiao-Long Yu
- School of Materials Science and Engineering, Hainan University, Haikou, China
| | - Lixia Tang
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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15
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Qiao H, Zhang S, Xue T, Wang J, Wang B. iPro-GAN: A novel model based on generative adversarial learning for identifying promoters and their strength. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 215:106625. [PMID: 35038653 DOI: 10.1016/j.cmpb.2022.106625] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 12/13/2021] [Accepted: 01/06/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Promoter is a component of the gene, which can specifically bind with RNA polymerase and determine where transcription starts, and also determine the transcription efficiency of the gene. Promoters can be divided into strong promoters and weak promoters because their structures and the interaction time interval are quite different. The functional variation of the promoter can lead to a variety of diseases. Therefore, identifying promoters and their strength is necessary and has important biological significance. A novel and promising model based on deep learning is proposed to achieve it. METHODS In this work, we build a power model named iPro-GAN for identification of promoters and their strength. First, we collect benchmark datasets and independent datasets for training and testing. Then, Moran-based spatial auto-cross correlation method is used as feature extraction method. Finally, deep convolution generative adversarial network with 10-fold cross validation is applied for classifying. The first layer of the model is used to identify the promoter and the second layer is used to determine its type. RESULTS On the benchmark data set, the accuracy of the first layer predictor is 93.15%, and the accuracy of the second layer predictor is 92.30%. On the independent data set, the accuracy of the first layer predictor is 86.77%, and the accuracy of the second layer predictor is 91.66%. In particular, breakthrough progress has been made in the identification of promoters' strength. CONCLUSIONS These results are far higher than the existing best predictor, which indicate that our model is serviceable and practicable to identify promoters and their strength. Furthermore, the datasets and source codes are available from this link: https://github.com/Bovbene/iPro-GAN.
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Affiliation(s)
- Huijuan Qiao
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China.
| | - Tian Xue
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| | - Jinyue Wang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| | - Bowei Wang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
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16
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Meng C, Ju Y, Shi H. TMPpred: A support vector machine-based thermophilic protein identifier. Anal Biochem 2022; 645:114625. [PMID: 35218736 DOI: 10.1016/j.ab.2022.114625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 11/13/2022]
Abstract
MOTIVATION The thermostability of proteins will cause them to break the temperature binding and play more functions. Using machine learning, we explored the mechanism of and reasons for protein thermostability characteristics. RESULTS Different from other methods that only pursue the performance of models, we aim to find important features so as to provide a powerful reference for in vitro experiments. We transformed this problem into a binary classification problem, that is, the distinction between thermophilic proteins and nonthermophilic proteins. Using support vector machine-based model construction and analysis, we inferred that Gly, Ala, Ser and Thr may be the most important components at the residue level that determine the thermal stability of proteins. It is also noteworthy that our proposed model obtains an Sn of 0.892, an Sp of 0.857, an ACC of 0.87566 and an AUC of 0.874. To facilitate other researchers, we wrapped our model and deployed it as a web server, which is accessible at http://112.124.26.17:7000/TMPpred/index.html.
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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 for Agriculture and Animal Husbandry, Hohhot, China
| | - Ying Ju
- School of Informatics, Xiamen University, Xiamen, China.
| | - Hua Shi
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China.
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17
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Chen Z, Jiao S, Zhao D, Zou Q, Xu L, Zhang L, Su X. The Characterization of Structure and Prediction for Aquaporin in Tumour Progression by Machine Learning. Front Cell Dev Biol 2022; 10:845622. [PMID: 35178393 PMCID: PMC8844512 DOI: 10.3389/fcell.2022.845622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 01/17/2022] [Indexed: 11/21/2022] Open
Abstract
Recurrence and new cases of cancer constitute a challenging human health problem. Aquaporins (AQPs) can be expressed in many types of tumours, including the brain, breast, pancreas, colon, skin, ovaries, and lungs, and the histological grade of cancer is positively correlated with AQP expression. Therefore, the identification of aquaporins is an area to explore. Computational tools play an important role in aquaporin identification. In this research, we propose reliable, accurate and automated sequence predictor iAQPs-RF to identify AQPs. In this study, the feature extraction method was 188D (global protein sequence descriptor, GPSD). Six common classifiers, including random forest (RF), NaiveBayes (NB), support vector machine (SVM), XGBoost, logistic regression (LR) and decision tree (DT), were used for AQP classification. The classification results show that the random forest (RF) algorithm is the most suitable machine learning algorithm, and the accuracy was 97.689%. Analysis of Variance (ANOVA) was used to analyse these characteristics. Feature rank based on the ANOVA method and IFS strategy was applied to search for the optimal features. The classification results suggest that the 26th feature (neutral/hydrophobic) and 21st feature (hydrophobic) are the two most powerful and informative features that distinguish AQPs from non-AQPs. Previous studies reported that plasma membrane proteins have hydrophobic characteristics. Aquaporin subcellular localization prediction showed that all aquaporins were plasma membrane proteins with highly conserved transmembrane structures. In addition, the 3D structure of aquaporins was consistent with the localization results. Therefore, these studies confirmed that aquaporins possess hydrophobic properties. Although aquaporins are highly conserved transmembrane structures, the phylogenetic tree shows the diversity of aquaporins during evolution. The PCA showed that positive and negative samples were well separated by 54D features, indicating that the 54D feature can effectively classify aquaporins. The online prediction server is accessible at http://lab.malab.cn/∼acy/iAQP.
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Affiliation(s)
- Zheng Chen
- School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, 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
| | - Da Zhao
- School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Lijun Zhang
- School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, China
| | - Xi Su
- Foshan Maternal and Child Health Hospital, Foshan, China
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18
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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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19
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Shahid M, Ilyas M, Hussain W, Khan YD. ORI-Deep: improving the accuracy for predicting origin of replication sites by using a blend of features and long short-term memory network. Brief Bioinform 2022; 23:6511972. [PMID: 35048955 DOI: 10.1093/bib/bbac001] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/30/2021] [Accepted: 01/02/2022] [Indexed: 11/14/2022] Open
Abstract
Replication of DNA is an important process for the cell division cycle, gene expression regulation and other biological evolution processes. It also has a crucial role in a living organism's physical growth and structure. Replication of DNA comprises of three stages known as initiation, elongation and termination, whereas the origin of replication sites (ORI) is the location of initiation of the DNA replication process. There exist various methodologies to identify ORIs in the genomic sequences, however, these methods have used either extensive computations for execution, or have limited optimization for the large datasets. Herein, a model called ORI-Deep is proposed to identify ORIs from the multiple cell type genomic sequence benchmark data. An efficient method is proposed using a deep neural network to identify ORIs for four different eukaryotic species. For better representation of data, a feature vector is constructed using statistical moments for the training and testing of data and is further fed to a long short-term memory (LSTM) network. To prove the effectiveness of the proposed model, we applied several validation techniques at different levels to obtain seven accuracy metrics, and the accuracy score for self-consistency, 10-fold cross-validation, jackknife and the independent set test is observed to be 0.977, 0.948, 0.976 and 0.977, respectively. Based on the results, it can be concluded that ORI-Deep can efficiently predict the sites of origin replication in DNA sequence with high accuracy. Webserver for ORI-Deep is available at (https://share.streamlit.io/waqarhusain/orideep/main/app.py), whereas source code is available at (https://github.com/WaqarHusain/OriDeep).
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Affiliation(s)
- Mahwish Shahid
- School of Systems and Technologies, University of Management and Technology, Lahore, Pakistan
| | - Maham Ilyas
- University of Management and Technology, Lahore, Pakistan
| | - Waqar Hussain
- University of Management and Technology, Lahore, Pakistan
| | - Yaser Daanial Khan
- Department of Computer Science, University of Management and Technology, Lahore, Pakistan
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20
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Guo X, Zhou W, Yu Y, Cai Y, Zhang Y, Du A, Lu Q, Ding Y, Li C. Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease. Front Physiol 2021; 12:790086. [PMID: 34966294 PMCID: PMC8711098 DOI: 10.3389/fphys.2021.790086] [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: 10/25/2021] [Accepted: 11/17/2021] [Indexed: 11/28/2022] Open
Abstract
Dry weight (DW) is an important dialysis index for patients with end-stage renal disease. It can guide clinical hemodialysis. Brain natriuretic peptide, chest computed tomography image, ultrasound, and bioelectrical impedance analysis are key indicators (multisource information) for assessing DW. By these approaches, a trial-and-error method (traditional measurement method) is employed to assess DW. The assessment of clinician is time-consuming. In this study, we developed a method based on artificial intelligence technology to estimate patient DW. Based on the conventional radial basis function neural (RBFN) network, we propose a multiple Laplacian-regularized RBFN (MLapRBFN) model to predict DW of patient. Compared with other model and body composition monitor, our method achieves the lowest value (1.3226) of root mean square error. In Bland-Altman analysis of MLapRBFN, the number of out agreement interval is least (17 samples). MLapRBFN integrates multiple Laplace regularization terms, and employs an efficient iterative algorithm to solve the model. The ratio of out agreement interval is 3.57%, which is lower than 5%. Therefore, our method can be tentatively applied for clinical evaluation of DW in hemodialysis patients.
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Affiliation(s)
- Xiaoyi Guo
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Zhou
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Yan Yu
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Yinghua Cai
- Department of Nursing, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Yuan Zhang
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Aiyan Du
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Qun Lu
- Department of Nursing, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Chao Li
- General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
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21
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Chen Y, Juan L, Lv X, Shi L. Bioinformatics Research on Drug Sensitivity Prediction. Front Pharmacol 2021; 12:799712. [PMID: 34955863 PMCID: PMC8696280 DOI: 10.3389/fphar.2021.799712] [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: 10/22/2021] [Accepted: 11/18/2021] [Indexed: 11/28/2022] Open
Abstract
Modeling-based anti-cancer drug sensitivity prediction has been extensively studied in recent years. While most drug sensitivity prediction models only use gene expression data, the remarkable impacts of gene mutation, methylation, and copy number variation on drug sensitivity are neglected. Drug sensitivity prediction can both help protect patients from some adverse drug reactions and improve the efficacy of treatment. Genomics data are extremely useful for drug sensitivity prediction task. This article reviews the role of drug sensitivity prediction, describes a variety of methods for predicting drug sensitivity. Moreover, the research significance of drug sensitivity prediction, as well as existing problems are well discussed.
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Affiliation(s)
- Yaojia Chen
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Liran Juan
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xiao Lv
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Lei Shi
- Department of Spine Surgery Changzheng Hospital, Naval Medical University, Shanghai, China
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22
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Malik A, Subramaniyam S, Kim CB, Manavalan B. SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information. Comput Struct Biotechnol J 2021; 20:165-174. [PMID: 34976319 PMCID: PMC8703055 DOI: 10.1016/j.csbj.2021.12.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 12/12/2022] Open
Abstract
Sortase enzymes are cysteine transpeptidases that embellish the surface of Gram-positive bacteria with various proteins thereby allowing these microorganisms to interact with their neighboring environment. It is known that several of their substrates can cause pathological implications, so researchers have focused on the development of sortase inhibitors. Currently, six different classes of sortases (A-F) are recognized. However, with the extensive application of bacterial genome sequencing projects, the number of potential sortases in the public databases has exploded, presenting considerable challenges in annotating these sequences. It is very laborious and time-consuming to characterize these sortase classes experimentally. Therefore, this study developed the first machine-learning-based two-layer predictor called SortPred, where the first layer predicts the sortase from the given sequence and the second layer predicts their class from the predicted sortase. To develop SortPred, we constructed an original benchmarking dataset and investigated 31 feature descriptors, primarily on five feature encoding algorithms. Afterward, each of these descriptors were trained using a random forest classifier and their robustness was evaluated with an independent dataset. Finally, we selected the final model independently for both layers depending on the performance consistency between cross-validation and independent evaluation. SortPred is expected to be an effective tool for identifying bacterial sortases, which in turn may aid in designing sortase inhibitors and exploring their functions. The SortPred webserver and a standalone version are freely accessible at: https://procarb.org/sortpred.
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Affiliation(s)
- Adeel Malik
- Institute of Intelligence Informatics Technology, Sangmyung University, Seoul 03016, Republic of Korea
| | | | - Chang-Bae Kim
- Department of Biotechnology, Sangmyung University, Seoul 03016, Republic of Korea
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23
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Charoenkwan P, Nantasenamat C, Hasan MM, Moni MA, Manavalan B, Shoombuatong W. UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning. Int J Mol Sci 2021; 22:ijms222313124. [PMID: 34884927 PMCID: PMC8658322 DOI: 10.3390/ijms222313124] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 11/16/2022] Open
Abstract
Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, and costly. As a result, it is preferable to develop computational tools for the large-scale identification of available sequences in order to identify novel peptides with umami sensory properties. Although a computational tool has been developed for this purpose, its predictive performance is still insufficient. In this study, we use a feature representation learning approach to create a novel machine-learning meta-predictor called UMPred-FRL for improved umami peptide identification. We combined six well-known machine learning algorithms (extremely randomized trees, k-nearest neighbor, logistic regression, partial least squares, random forest, and support vector machine) with seven different feature encodings (amino acid composition, amphiphilic pseudo-amino acid composition, dipeptide composition, composition-transition-distribution, and pseudo-amino acid composition) to develop the final meta-predictor. Extensive experimental results demonstrated that UMPred-FRL was effective and achieved more accurate performance on the benchmark dataset compared to its baseline models, and consistently outperformed the existing method on the independent test dataset. Finally, to aid in the high-throughput identification of umami peptides, the UMPred-FRL web server was established and made freely available online. It is expected that UMPred-FRL will be a powerful tool for the cost-effective large-scale screening of candidate peptides with potential umami sensory properties.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand;
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand;
| | - Md Mehedi Hasan
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA;
| | - Mohammad Ali Moni
- Artificial Intelligence & Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4072, Australia;
| | - Balachandran Manavalan
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Korea
- Correspondence: (B.M.); (W.S.)
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand;
- Correspondence: (B.M.); (W.S.)
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24
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Xu Z, Luo M, Lin W, Xue G, Wang P, Jin X, Xu C, Zhou W, Cai Y, Yang W, Nie H, Jiang Q. DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor. Brief Bioinform 2021; 22:6355415. [PMID: 34415016 DOI: 10.1093/bib/bbab335] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/25/2021] [Accepted: 07/28/2021] [Indexed: 12/30/2022] Open
Abstract
Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.
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Affiliation(s)
- Zhaochun Xu
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Meng Luo
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Weizhong Lin
- Center for Bioinformatics, Computer Department, Jingdezhen Ceramic Institute, Jingdezhen 333403, China
| | - Guangfu Xue
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Pingping Wang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Xiyun Jin
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Chang Xu
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Wenyang Zhou
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Yideng Cai
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Wenyi Yang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Huan Nie
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Qinghua Jiang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China.,Key Laboratory of Biological Data (Harbin Institute of Technology), Ministry of Education, China
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25
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Zhang S, Shi H. iR5hmcSC: Identifying RNA 5-hydroxymethylcytosine with multiple features based on stacking learning. Comput Biol Chem 2021; 95:107583. [PMID: 34562726 DOI: 10.1016/j.compbiolchem.2021.107583] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 09/02/2021] [Accepted: 09/12/2021] [Indexed: 01/27/2023]
Abstract
RNA 5-hydroxymethylcytosine (5hmC) modification is the basis of the translation of genetic information and the biological evolution. The study of its distribution in transcriptome is fundamentally crucial to reveal the biological significance of 5hmC. Biochemical experiments can use a variety of sequencing-based technologies to achieve high-throughput identification of 5hmC; however, they are labor-intensive, time-consuming, as well as expensive. Therefore, it is urgent to develop more effective and feasible computational methods. In this paper, a novel and powerful model called iR5hmcSC is designed for identifying 5hmC. Firstly, we extract the different features by K-mer, Pseudo Structure Status Composition and One-Hot encoding. Subsequently, the combination of chi-square test and logistic regression is utilized as the feature selection method to select the optimal feature sets. And then stacking learning, an ensemble learning method including random forest (RF), extra trees (EX), AdaBoost (Ada), gradient boosting decision tree (GBDT), and support vector machine (SVM), is used to recognize 5hmC and non-5hmC. Finally, 10-fold cross-validation test is performed to evaluate the model. The accuracy reaches 85.27% and 79.92% on benchmark dataset and independent dataset, respectively. The result is better than the state-of-the-art methods, which indicates that our model is a feasible tool to identify 5hmC. The datasets and source code are freely available at https://github.com/HongyanShi026/iR5hmcSC.
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Affiliation(s)
- Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China.
| | - Hongyan Shi
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China
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26
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Xue Y, Ye X, Wei L, Zhang X, Sakurai T, Wei L. Better Performance with Transformer: CPPFormer in precise prediction of cell-Penetrating Peptides. Curr Med Chem 2021; 29:881-893. [PMID: 34544332 DOI: 10.2174/0929867328666210920103140] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 07/28/2021] [Accepted: 08/07/2021] [Indexed: 11/22/2022]
Abstract
With its superior performance, the Transformer model, which is based on the 'Encoder-Decoder' paradigm, has become the mainstream in natural language processing. On the other hand, bioinformatics has embraced machine learning and made great progress in drug design and protein property prediction. Cell-penetrating peptides (CPPs) are one kind of permeable protein that is convenient as a kind of 'postman' in drug penetration tasks. However, a small number of CPPs have been discovered by research, let alone practical applications in drug permeability. Therefore, correctly identifying the CPPs has opened up a new way to take macromolecules into cells without other potentially harmful materials in the drug. Most of the previous work only uses trivial machine learning techniques and hand-crafted features to construct a simple classifier. In CPPFormer, we learn from the idea of implementing the attention structure of Transformer, rebuilding the network based on the characteristics of CPPs according to its short length, and using an automatic feature extractor with a few manual engineered features to co-direct the predicted results. Compared to all previous methods and other classic text classification models, the empirical result has shown that our proposed deep model-based method has achieved the best performance of 92.16% accuracy in the CPP924 dataset and has passed various index tests.
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Affiliation(s)
- Yuyang Xue
- Department of Computer Science, University of Tsukuba, Tsukuba. Japan
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba. Japan
| | - Lesong Wei
- Department of Computer Science, University of Tsukuba, Tsukuba. Japan
| | - Xin Zhang
- School of Software, Shandong University, Jinan. China
| | - Tetsuya Sakurai
- Department of Computer Science, University of Tsukuba, Tsukuba. Japan
| | - Leyi Wei
- School of Software, Shandong University, Jinan. China
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27
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Khatun MS, Alam MA, Shoombuatong W, Mollah MNH, Kurata H, Hasan MM. Recent development of bioinformatics tools for microRNA target prediction. Curr Med Chem 2021; 29:865-880. [PMID: 34348604 DOI: 10.2174/0929867328666210804090224] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 06/10/2021] [Accepted: 06/15/2021] [Indexed: 11/22/2022]
Abstract
MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop the bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies specially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.
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Affiliation(s)
- Mst Shamima Khatun
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502. Japan
| | - Md Ashad Alam
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112. United States
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700. Thailand
| | - Md Nurul Haque Mollah
- Laboratory of Bioinformatics, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh. 5Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083. Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502. Japan
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502. Japan
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28
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Yu L, Su Y, Liu Y, Zeng X. Review of unsupervised pretraining strategies for molecules representation. Brief Funct Genomics 2021; 20:323-332. [PMID: 34342611 DOI: 10.1093/bfgp/elab036] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 11/14/2022] Open
Abstract
In recent years, the computer-assisted techniques make a great progress in the field of drug discovery. And, yet, the problem of limited labeled data problem is still challenging and also restricts the performance of these techniques in specific tasks, such as molecular property prediction, compound-protein interaction and de novo molecular generation. One effective solution is to utilize the experience and knowledge gained from other tasks to cope with related pursuits. Unsupervised pretraining is promising, due to its capability of leveraging a vast number of unlabeled molecules and acquiring a more informative molecular representation for the downstream tasks. In particular, models trained on large-scale unlabeled molecules can capture generalizable features, and this ability can be employed to improve the performance of specific downstream tasks. Many relevant pretraining works have been recently proposed. Here, we provide an overview of molecular unsupervised pretraining and related applications in drug discovery. Challenges and possible solutions are also summarized.
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29
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Zulfiqar H, Yuan SS, Huang QL, Sun ZJ, Dao FY, Yu XL, Lin H. Identification of cyclin protein using gradient boost decision tree algorithm. Comput Struct Biotechnol J 2021; 19:4123-4131. [PMID: 34527186 PMCID: PMC8346528 DOI: 10.1016/j.csbj.2021.07.013] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/15/2021] [Accepted: 07/15/2021] [Indexed: 12/12/2022] Open
Abstract
Cyclin proteins are capable to regulate the cell cycle by forming a complex with cyclin-dependent kinases to activate cell cycle. Correct recognition of cyclin proteins could provide key clues for studying their functions. However, their sequences share low similarity, which results in poor prediction for sequence similarity-based methods. Thus, it is urgent to construct a machine learning model to identify cyclin proteins. This study aimed to develop a computational model to discriminate cyclin proteins from non-cyclin proteins. In our model, protein sequences were encoded by seven kinds of features that are amino acid composition, composition of k-spaced amino acid pairs, tri peptide composition, pseudo amino acid composition, geary correlation, normalized moreau-broto autocorrelation and composition/transition/distribution. Afterward, these features were optimized by using analysis of variance (ANOVA) and minimum redundancy maximum relevance (mRMR) with incremental feature selection (IFS) technique. A gradient boost decision tree (GBDT) classifier was trained on the optimal features. Five-fold cross-validated results showed that our model would identify cyclins with an accuracy of 93.06% and AUC value of 0.971, which are higher than the two recent studies on the same data.
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Affiliation(s)
- Hasan Zulfiqar
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shi-Shi Yuan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qin-Lai Huang
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zi-Jie Sun
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Ying Dao
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xiao-Long Yu
- School of Materials Science and Engineering, Hainan University, Haikou 570228, China
| | - Hao Lin
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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30
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Zhu Z, Han X, Cheng L. Identification of gene signature associated with type 2 diabetes mellitus by integrating mutation and expression data. Curr Gene Ther 2021; 22:51-58. [PMID: 34238156 DOI: 10.2174/1566523221666210707140839] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 04/08/2021] [Accepted: 04/18/2021] [Indexed: 11/22/2022]
Abstract
Type 2 diabetes mellitus (T2DM) is a chronic disease. The molecular diagnosis should be helpful for the treatment of T2DM patients. With the development of sequencing technology, a large number of differentially expressed genes were identified from expression data. However, the method of machine learning can only identify the local optimal solution as the signature. The mutation information obtained by inheritance can better reflect the relationship between genes and diseases. Therefore, we need to integrate mutation information to more accurately identify the signature. To this end, we integrated genome-wide association study (GWAS) data and expression data, combined with expression quantitative trait loci (eQTL) technology to get T2DM predictive signature (T2DMSig-10). Firstly, we used GWAS data to obtain a list of T2DM susceptible loci. Then, we used eQTL technology to obtain risk single nucleotide polymorphisms (SNPs), and combined with the pancreatic β-cells gene expression data to obtain 10 protein-coding genes. Next, we combined these genes with equal weights. After receiver operating characteristic (ROC), single-gene removal and increase method, gene ontology function enrichment and protein-protein interaction network were used to verify the results that showed that T2DMSig-10 had an excellent predictive effect on T2DM (AUC=0.99), and was highly robust. In short, we obtained the predictive signature of T2DM, and further verified it.
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Affiliation(s)
- Zijun Zhu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Xudong Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Liang Cheng
- NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
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31
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Tan X, Wu X, Han M, Wang L, Xu L, Li B, Yuan Y. Yeast autonomously replicating sequence (ARS): Identification, function, and modification. Eng Life Sci 2021. [DOI: 10.1002/elsc.202000085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Affiliation(s)
- Xiao‐Yu Tan
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology Tianjin University Tianjin P. R. China
- Synthetic Biology Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin University Tianjin P. R. China
| | - Xiao‐Le Wu
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology Tianjin University Tianjin P. R. China
- Synthetic Biology Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin University Tianjin P. R. China
| | - Ming‐Zhe Han
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology Tianjin University Tianjin P. R. China
- Synthetic Biology Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin University Tianjin P. R. China
| | - Li Wang
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology Tianjin University Tianjin P. R. China
- Synthetic Biology Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin University Tianjin P. R. China
| | - Li Xu
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology Tianjin University Tianjin P. R. China
- Synthetic Biology Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin University Tianjin P. R. China
| | - Bing‐Zhi Li
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology Tianjin University Tianjin P. R. China
- Synthetic Biology Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin University Tianjin P. R. China
| | - Ying‐Jin Yuan
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology Tianjin University Tianjin P. R. China
- Synthetic Biology Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin University Tianjin P. R. China
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32
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Min X, Lu F, Li C. Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction. Curr Pharm Des 2021; 27:1847-1855. [PMID: 33234095 DOI: 10.2174/1381612826666201124112710] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 07/29/2020] [Accepted: 08/06/2020] [Indexed: 11/22/2022]
Abstract
Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation, which tightly controls gene expression. Identification of EPIs can help us better decipher gene regulation and understand disease mechanisms. However, experimental methods to identify EPIs are constrained by funds, time, and manpower, while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literature. First, we briefly introduce existing sequence- based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means, and evaluation strategies. Finally, we concluded with the challenges these methods are confronted with and suggest several future opportunities. We hope this review will provide a useful reference for further studies on enhancer-promoter interactions.
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Affiliation(s)
- Xiaoping Min
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Fengqing Lu
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Chunyan Li
- Graduate School, Yunnan Minzu University, Kunming 650504, China
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33
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Song B, Li Z, Lin X, Wang J, Wang T, Fu X. Pretraining model for biological sequence data. Brief Funct Genomics 2021; 20:181-195. [PMID: 34050350 PMCID: PMC8194843 DOI: 10.1093/bfgp/elab025] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/13/2021] [Accepted: 04/21/2021] [Indexed: 12/26/2022] Open
Abstract
With the development of high-throughput sequencing technology, biological sequence data reflecting life information becomes increasingly accessible. Particularly on the background of the COVID-19 pandemic, biological sequence data play an important role in detecting diseases, analyzing the mechanism and discovering specific drugs. In recent years, pretraining models that have emerged in natural language processing have attracted widespread attention in many research fields not only to decrease training cost but also to improve performance on downstream tasks. Pretraining models are used for embedding biological sequence and extracting feature from large biological sequence corpus to comprehensively understand the biological sequence data. In this survey, we provide a broad review on pretraining models for biological sequence data. Moreover, we first introduce biological sequences and corresponding datasets, including brief description and accessible link. Subsequently, we systematically summarize popular pretraining models for biological sequences based on four categories: CNN, word2vec, LSTM and Transformer. Then, we present some applications with proposed pretraining models on downstream tasks to explain the role of pretraining models. Next, we provide a novel pretraining scheme for protein sequences and a multitask benchmark for protein pretraining models. Finally, we discuss the challenges and future directions in pretraining models for biological sequences.
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Affiliation(s)
| | | | | | | | | | - Xiangzheng Fu
- Corresponding author: Xiangzheng Fu, College of Information Science and Engineering, Hunan University, Changsha, Hunan, China. Tel: 86-0731-88821907; E-mail:
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34
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Zhang J, Sun M, Zhao Y, Geng G, Hu Y. Identification of Gingivitis-Related Genes Across Human Tissues Based on the Summary Mendelian Randomization. Front Cell Dev Biol 2021; 8:624766. [PMID: 34026747 PMCID: PMC8134671 DOI: 10.3389/fcell.2020.624766] [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: 11/01/2020] [Accepted: 12/02/2020] [Indexed: 11/13/2022] Open
Abstract
Periodontal diseases are among the most frequent inflammatory diseases affecting children and adolescents, which affect the supporting structures of the teeth and lead to tooth loss and contribute to systemic inflammation. Gingivitis is the most common periodontal infection. Gingivitis, which is mainly caused by a substance produced by microbial plaque, systemic disorders, and genetic abnormalities in the host. Identifying gingivitis-related genes across human tissues is not only significant for understanding disease mechanisms but also disease development and clinical diagnosis. The Genome-wide association study (GWAS) a commonly used method to mine disease-related genetic variants. However, due to some factors such as linkage disequilibrium, it is difficult for GWAS to identify genes directly related to the disease. Hence, we constructed a data integration method that uses the Summary Mendelian randomization (SMR) to combine the GWAS with expression quantitative trait locus (eQTL) data to identify gingivitis-related genes. Five eQTL studies from different human tissues and one GWAS studies were referenced in this paper. This study identified several candidates SNPs and genes relate to gingivitis in tissue-specific or cross-tissue. Further, we also analyzed and explained the functions of these genes. The R program for the SMR method has been uploaded to GitHub(https://github.com/hxdde/SMR).
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Affiliation(s)
- Jiahui Zhang
- Department of Stomatology and Dental Hygiene, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Mingai Sun
- General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Yuanyuan Zhao
- General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Guannan Geng
- Department of Endocrinology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yang Hu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
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35
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Abiram RN, Vincent PMDR. Identity preserving multi-pose facial expression recognition using fine tuned VGG on the latent space vector of generative adversarial network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:3699-3717. [PMID: 34198408 DOI: 10.3934/mbe.2021186] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Facial expression is the crucial component for human beings to express their mental state and it has become one of the prominent areas of research in computer vision. However, the task becomes challenging when the given facial image is non-frontal. The influence of poses on facial images is alleviated using an encoder of a generative adversarial network capable of learning pose invariant representations. State-of-art results for image generation are achieved using styleGAN architecture. An efficient model is proposed to embed the given image into the latent vector space of styleGAN. The encoder extracts high-level features of the facial image and encodes them into the latent space. Rigorous analysis of semantics hidden in the latent space of styleGAN is performed. Based on the analysis, the facial image is synthesized, and facial expressions are recognized using an expression recognition neural network. The original image is recovered from the features encoded in the latent space. Semantic editing operations like face rotation, style transfer, face aging, image morphing and expression transfer can be performed on the image obtained from the image generated using the features encoded latent space of styleGAN. L2 feature-wise loss is applied to warrant the quality of the rebuilt image. The facial image is then fed into the attribute classifier to extract high-level features, and the features are concatenated to perform facial expression classification. Evaluations are performed on the generated results to demonstrate that state-of-art results are achieved using the proposed method.
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Affiliation(s)
- R Nandhini Abiram
- School of Information Technology, Vellore Institute of Technology, Vellore, Tamilnadu, India
| | - P M Durai Raj Vincent
- School of Information Technology, Vellore Institute of Technology, Vellore, Tamilnadu, India
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36
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Zulfiqar H, Khan RS, Hassan F, Hippe K, Hunt C, Ding H, Song XM, Cao R. Computational identification of N4-methylcytosine sites in the mouse genome with machine-learning method. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:3348-3363. [PMID: 34198389 DOI: 10.3934/mbe.2021167] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/24/2023]
Abstract
N4-methylcytosine (4mC) is a kind of DNA modification which could regulate multiple biological processes. Correctly identifying 4mC sites in genomic sequences can provide precise knowledge about their genetic roles. This study aimed to develop an ensemble model to predict 4mC sites in the mouse genome. In the proposed model, DNA sequences were encoded by k-mer, enhanced nucleic acid composition and composition of k-spaced nucleic acid pairs. Subsequently, these features were optimized by using minimum redundancy maximum relevance (mRMR) with incremental feature selection (IFS) and five-fold cross-validation. The obtained optimal features were inputted into random forest classifier for discriminating 4mC from non-4mC sites in mouse. On the independent dataset, our model could yield the overall accuracy of 85.41%, which was approximately 3.8% -6.3% higher than the two existing models, i4mC-Mouse and 4mCpred-EL respectively. The data and source code of the model can be freely download from https://github.com/linDing-groups/model_4mc.
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Affiliation(s)
- Hasan Zulfiqar
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Rida Sarwar Khan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Farwa Hassan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Kyle Hippe
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
| | - Cassandra Hunt
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
| | - Hui Ding
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xiao-Ming Song
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
- School of Life Sciences, North China University of Science and Technology, Tangshan, Hebei 063210, China
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
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37
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ANPrAod: Identify Antioxidant Proteins by Fusing Amino Acid Clustering Strategy and N-Peptide Combination. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5518209. [PMID: 33927782 PMCID: PMC8049822 DOI: 10.1155/2021/5518209] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/02/2021] [Accepted: 03/10/2021] [Indexed: 11/18/2022]
Abstract
Antioxidant proteins perform significant functions in disease control and delaying aging which can prevent free radicals from damaging organisms. Accurate identification of antioxidant proteins has important implications for the development of new drugs and the treatment of related diseases, as they play a critical role in the control or prevention of cancer and aging-related conditions. Since experimental identification techniques are time-consuming and expensive, many computational methods have been proposed to identify antioxidant proteins. Although the accuracy of these methods is acceptable, there are still some challenges. In this study, we developed a computational model called ANPrAod to identify antioxidant proteins based on a support vector machine. In order to eliminate potential redundant features and improve prediction accuracy, 673 amino acid reduction alphabets were calculated by us to find the optimal feature representation scheme. The final model could produce an overall accuracy of 87.53% with the ROC of 0.7266 in five-fold cross-validation, which was better than the existing methods. The results of the independent dataset also demonstrated the excellent robustness and reliability of ANPrAod, which could be a promising tool for antioxidant protein identification and contribute to hypothesis-driven experimental design.
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38
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Yao Y, Zhang S, Liang Y. iORI-ENST: identifying origin of replication sites based on elastic net and stacking learning. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:317-331. [PMID: 33730950 DOI: 10.1080/1062936x.2021.1895884] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/23/2021] [Indexed: 06/12/2023]
Abstract
DNA replication is not only the basis of biological inheritance but also the most fundamental process in all living organisms. It plays a crucial role in the cell-division cycle and gene expression regulation. Hence, the accurate identification of the origin of replication sites (ORIs) has a great meaning for further understanding the regulatory mechanism of gene expression and treating genic diseases. In this paper, a novel, feasible and powerful model, namely, iORI-ENST is designed for identifying ORIs. Firstly, we extract the different features by incorporating mono-nucleotide binary encoding and dinucleotide-based spatial autocorrelation. Subsequently, elastic net is utilized as the feature selection method to select the optimal feature set. And then stacking learning is employed to predict ORIs and non-ORIs, which contains random forest, adaboost, gradient boosting decision tree, extra trees and support vector machine. Finally, the ORI sites are identified on the benchmark datasets S1 and S2 with their accuracies of 91.41% and 95.07%, respectively. Meanwhile, an independent dataset S3 is employed to verify the validation and transferability of our model and its accuracy reaches 91.10%. Comparing with state-of-the-art methods, our model achieves more remarkable performance. The results show our model is a feasible, effective and powerful tool for identifying ORIs. The source code and datasets are available at https://github.com/YingyingYao/iORI-ENST.
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Affiliation(s)
- Y Yao
- School of Mathematics and Statistics, Xidian University, Xi'an, P. R. China
| | - S Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an, P. R. China
| | - Y Liang
- School of Science, Xi'an Polytechnic University, Xi'an, P. R. China
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39
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Niu K, Luo X, Zhang S, Teng Z, Zhang T, Zhao Y. iEnhancer-EBLSTM: Identifying Enhancers and Strengths by Ensembles of Bidirectional Long Short-Term Memory. Front Genet 2021; 12:665498. [PMID: 33833783 PMCID: PMC8021722 DOI: 10.3389/fgene.2021.665498] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 03/01/2021] [Indexed: 12/26/2022] Open
Abstract
Enhancers are regulatory DNA sequences that could be bound by specific proteins named transcription factors (TFs). The interactions between enhancers and TFs regulate specific genes by increasing the target gene expression. Therefore, enhancer identification and classification have been a critical issue in the enhancer field. Unfortunately, so far there has been a lack of suitable methods to identify enhancers. Previous research has mainly focused on the features of the enhancer's function and interactions, which ignores the sequence information. As we know, the recurrent neural network (RNN) and long short-term memory (LSTM) models are currently the most common methods for processing time series data. LSTM is more suitable than RNN to address the DNA sequence. In this paper, we take the advantages of LSTM to build a method named iEnhancer-EBLSTM to identify enhancers. iEnhancer-ensembles of bidirectional LSTM (EBLSTM) consists of two steps. In the first step, we extract subsequences by sliding a 3-mer window along the DNA sequence as features. Second, EBLSTM model is used to identify enhancers from the candidate input sequences. We use the dataset from the study of Quang H et al. as the benchmarks. The experimental results from the datasets demonstrate the efficiency of our proposed model.
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Affiliation(s)
- Kun Niu
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Ximei Luo
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Shumei Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Zhixia Teng
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Tianjiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Yuming Zhao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
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40
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Dao FY, Lv H, Su W, Sun ZJ, Huang QL, Lin H. iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network. Brief Bioinform 2021; 22:6158360. [PMID: 33751027 DOI: 10.1093/bib/bbab047] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 01/28/2021] [Accepted: 01/29/2021] [Indexed: 01/09/2023] Open
Abstract
DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription factor-binding site, etc. Moreover, the related locus of disease (or trait) are usually enriched in the DHS regions. Therefore, the detection of DHS region is of great significance. In this study, we develop a deep learning-based algorithm to identify whether an unknown sequence region would be potential DHS. The proposed method showed high prediction performance on both training datasets and independent datasets in different cell types and developmental stages, demonstrating that the method has excellent superiority in the identification of DHSs. Furthermore, for the convenience of related wet-experimental researchers, the user-friendly web-server iDHS-Deep was established at http://lin-group.cn/server/iDHS-Deep/, by which users can easily distinguish DHS and non-DHS and obtain the corresponding developmental stage ofDHS.
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Affiliation(s)
- Fu-Ying Dao
- Informational Biology at University of Electronic Science and Technology of China, China
| | - Hao Lv
- Informational Biology at University of Electronic Science and Technology of China, China
| | - Wei Su
- Informational Biology at University of Electronic Science and Technology of China, China
| | - Zi-Jie Sun
- Informational Biology at University of Electronic Science and Technology of China, China
| | - Qin-Lai Huang
- Informational Biology at University of Electronic Science and Technology of China, China
| | - Hao Lin
- Informational Biology at University of Electronic Science and Technology of China, China
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41
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Chen CX, Sun LN, Hou XX, Du PC, Wang XL, Du XC, Yu YF, Cai RK, Yu L, Li TJ, Luo MN, Shen Y, Lu C, Li Q, Zhang C, Gao HF, Ma X, Lin H, Cao ZF. Prevention and Control of Pathogens Based on Big-Data Mining and Visualization Analysis. Front Mol Biosci 2021; 7:626595. [PMID: 33718431 PMCID: PMC7947816 DOI: 10.3389/fmolb.2020.626595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 12/21/2020] [Indexed: 11/13/2022] Open
Abstract
Morbidity and mortality caused by infectious diseases rank first among all human illnesses. Many pathogenic mechanisms remain unclear, while misuse of antibiotics has led to the emergence of drug-resistant strains. Infectious diseases spread rapidly and pathogens mutate quickly, posing new threats to human health. However, with the increasing use of high-throughput screening of pathogen genomes, research based on big data mining and visualization analysis has gradually become a hot topic for studies of infectious disease prevention and control. In this paper, the framework was performed on four infectious pathogens (Fusobacterium, Streptococcus, Neisseria, and Streptococcus salivarius) through five functions: 1) genome annotation, 2) phylogeny analysis based on core genome, 3) analysis of structure differences between genomes, 4) prediction of virulence genes/factors with their pathogenic mechanisms, and 5) prediction of resistance genes/factors with their signaling pathways. The experiments were carried out from three angles: phylogeny (macro perspective), structure differences of genomes (micro perspective), and virulence and drug-resistance characteristics (prediction perspective). Therefore, the framework can not only provide evidence to support the rapid identification of new or unknown pathogens and thus plays a role in the prevention and control of infectious diseases, but also help to recommend the most appropriate strains for clinical and scientific research. This paper presented a new genome information visualization analysis process framework based on big data mining technology with the accommodation of the depth and breadth of pathogens in molecular level research.
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Affiliation(s)
- Cui-Xia Chen
- National Research Institute for Family Planning, Beijing, China.,National Center of Human Genetic Resources, Beijing, China
| | - Li-Na Sun
- National Institute for Communicable Disease Control and Prevention, Beijing, China
| | - Xue-Xin Hou
- National Institute for Communicable Disease Control and Prevention, Beijing, China
| | | | - Xiao-Long Wang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Xiao-Chen Du
- Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yu-Fei Yu
- National Research Institute for Family Planning, Beijing, China.,National Center of Human Genetic Resources, Beijing, China
| | - Rui-Kun Cai
- National Research Institute for Family Planning, Beijing, China.,National Center of Human Genetic Resources, Beijing, China
| | - Lei Yu
- National Research Institute for Family Planning, Beijing, China.,National Center of Human Genetic Resources, Beijing, China
| | - Tian-Jun Li
- National Research Institute for Family Planning, Beijing, China.,National Center of Human Genetic Resources, Beijing, China
| | - Min-Na Luo
- National Research Institute for Family Planning, Beijing, China.,National Center of Human Genetic Resources, Beijing, China
| | - Yue Shen
- National Research Institute for Family Planning, Beijing, China.,National Center of Human Genetic Resources, Beijing, China
| | - Chao Lu
- National Research Institute for Family Planning, Beijing, China.,National Center of Human Genetic Resources, Beijing, China
| | - Qian Li
- National Research Institute for Family Planning, Beijing, China.,National Center of Human Genetic Resources, Beijing, China
| | - Chuan Zhang
- National Research Institute for Family Planning, Beijing, China.,National Center of Human Genetic Resources, Beijing, China
| | - Hua-Fang Gao
- National Research Institute for Family Planning, Beijing, China.,National Center of Human Genetic Resources, Beijing, China
| | - Xu Ma
- National Research Institute for Family Planning, Beijing, China.,National Center of Human Genetic Resources, Beijing, China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zong-Fu Cao
- National Research Institute for Family Planning, Beijing, China.,National Center of Human Genetic Resources, Beijing, China
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42
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Jing XY, Li FM. Predicting Cell Wall Lytic Enzymes Using Combined Features. Front Bioeng Biotechnol 2021; 8:627335. [PMID: 33585423 PMCID: PMC7874139 DOI: 10.3389/fbioe.2020.627335] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 12/04/2020] [Indexed: 11/13/2022] Open
Abstract
Due to the overuse of antibiotics, people are worried that existing antibiotics will become ineffective against pathogens with the rapid rise of antibiotic-resistant strains. The use of cell wall lytic enzymes to destroy bacteria has become a viable alternative to avoid the crisis of antimicrobial resistance. In this paper, an improved method for cell wall lytic enzymes prediction was proposed and the amino acid composition (AAC), the dipeptide composition (DC), the position-specific score matrix auto-covariance (PSSM-AC), and the auto-covariance average chemical shift (acACS) were selected to predict the cell wall lytic enzymes with support vector machine (SVM). In order to overcome the imbalanced data classification problems and remove redundant or irrelevant features, the synthetic minority over-sampling technique (SMOTE) was used to balance the dataset. The F-score was used to select features. The Sn, Sp, MCC, and Acc were 99.35%, 99.02%, 0.98, and 99.19% with jackknife test using the optimized combination feature AAC+DC+acACS+PSSM-AC. The Sn, Sp, MCC, and Acc of cell wall lytic enzymes in our predictive model were higher than those in existing methods. This improved method may be helpful for protein function prediction.
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Affiliation(s)
- Xiao-Yang Jing
- College of Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Feng-Min Li
- College of Science, Inner Mongolia Agricultural University, Hohhot, China
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43
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Hasan MM, Shoombuatong W, Kurata H, Manavalan B. Critical evaluation of web-based DNA N6-methyladenine site prediction tools. Brief Funct Genomics 2021; 20:258-272. [PMID: 33491072 DOI: 10.1093/bfgp/elaa028] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 12/11/2020] [Accepted: 12/15/2020] [Indexed: 12/13/2022] Open
Abstract
Methylation of DNA N6-methyladenosine (6mA) is a type of epigenetic modification that plays pivotal roles in various biological processes. The accurate genome-wide identification of 6mA is a challenging task that leads to understanding the biological functions. For the last 5 years, a number of bioinformatics approaches and tools for 6mA site prediction have been established, and some of them are easily accessible as web application. Nevertheless, the accurate genome-wide identification of 6mA is still one of the challenging works that lead to understanding the biological functions. Especially in practical applications, these tools have implemented diverse encoding schemes, machine learning algorithms and feature selection methods, whereas few systematic performance comparisons of 6mA site predictors have been reported. In this review, 11 publicly available 6mA predictors evaluated with seven different species-specific datasets (Arabidopsis thaliana, Tolypocladium, Diospyros lotus, Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans and Escherichia coli). Of those, few species are close homologs, and the remaining datasets are distant sequences. Our independent, validation tests demonstrated that Meta-i6mA and MM-6mAPred models for A. thaliana, Tolypocladium, S. cerevisiae and D. melanogaster achieved excellent overall performance when compared with their counterparts. However, none of the existing methods were suitable for E. coli, C. elegans and D. lotus. A feasibility of the existing predictors is also discussed for the seven species. Our evaluation provides useful guidelines for the development of 6mA site predictors and helps biologists selecting suitable prediction tools.
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Affiliation(s)
| | - Watshara Shoombuatong
- Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics in the Kyushu Institute of Technology, Japan
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44
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Screening of Prospective Plant Compounds as H1R and CL1R Inhibitors and Its Antiallergic Efficacy through Molecular Docking Approach. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021. [DOI: 10.1155/2021/6683407] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Allergens have the ability to enter the body and cause illness. Leukotriene is the widespread allergen which could stimulate mast cells to discharge histamine which causes allergy symptoms. An effective strategy for treating leukotriene-induced allergy is to find the inhibitors of leukotriene or histamine activity from phytochemicals. For this purpose, a library of 8,500 phytochemicals was generated using MOE software. The structures of histamine-1 receptor and cysteinyl leukotriene receptor-1 were predicted by the homology modeling method through the SWISS model. The phytochemicals were docked with predicted structures of histamine-1 and cysteinyl leukotriene receptor-1 in MOE software to determine the binding affinity of the phytochemicals against the targets. Moreover, chemoinformatics properties and ADMET of phytochemicals were assessed to find the drug likeness behavior of compounds. Compound ID 10054216 has the lowest
-score value for H-1 receptor that is -18.9186 kcal/mol which is lower than the value of standard -15.167 kcal/mol. The other compounds 393471, 71448939, 10722577, and 442614 also showed good
-score values than the standard. Moreover, compound ID 11843082 has the lowest
-score value for CL1R that is -15.481 kcal/mol which is lower than the value of standard -12.453 kcal/mol. The other compounds 72284, 5282102, 66559251, and 102506430 also showed good
-score values than the standard. In this research article, we performed molecular docking to find the best inhibitors against H1R and CL1R and their antiallergic efficacy. This in silico knowledge will be helpful in near future for the design of novel, safe, and less costing H-1 receptor and CL1R inhibitors with the aim to improve human life quality.
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45
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Liu T, Chen JM, Zhang D, Zhang Q, Peng B, Xu L, Tang H. ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features. Front Cell Dev Biol 2021; 8:621144. [PMID: 33490085 PMCID: PMC7820372 DOI: 10.3389/fcell.2020.621144] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 11/24/2020] [Indexed: 01/24/2023] Open
Abstract
Apolipoprotein is a group of plasma proteins that are associated with a variety of diseases, such as hyperlipidemia, atherosclerosis, Alzheimer's disease, and diabetes. In order to investigate the function of apolipoproteins and to develop effective targets for related diseases, it is necessary to accurately identify and classify apolipoproteins. Although it is possible to identify apolipoproteins accurately through biochemical experiments, they are expensive and time-consuming. This work aims to establish a high-efficiency and high-accuracy prediction model for recognition of apolipoproteins and their subfamilies. We firstly constructed a high-quality benchmark dataset including 270 apolipoproteins and 535 non-apolipoproteins. Based on the dataset, pseudo-amino acid composition (PseAAC) and composition of k-spaced amino acid pairs (CKSAAP) were used as input vectors. To improve the prediction accuracy and eliminate redundant information, analysis of variance (ANOVA) was used to rank the features. And the incremental feature selection was utilized to obtain the best feature subset. Support vector machine (SVM) was proposed to construct the classification model, which could produce the accuracy of 97.27%, sensitivity of 96.30%, and specificity of 97.76% for discriminating apolipoprotein from non-apolipoprotein in 10-fold cross-validation. In addition, the same process was repeated to generate a new model for predicting apolipoprotein subfamilies. The new model could achieve an overall accuracy of 95.93% in 10-fold cross-validation. According to our proposed model, a convenient webserver called ApoPred was established, which can be freely accessed at http://tang-biolab.com/server/ApoPred/service.html. We expect that this work will contribute to apolipoprotein function research and drug development in relevant diseases.
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Affiliation(s)
- Ting Liu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, China
| | - Jia-Mao Chen
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, China
| | - Dan Zhang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qian Zhang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, China
| | - Bowen Peng
- Division of international Cooperation, Health Commission of Sichuan Province, Chengdu, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Hua Tang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, China
- Central Nervous System Drug Key Laboratory of Sichuan Province, Luzhou, China
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46
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iPTT(2 L)-CNN: A Two-Layer Predictor for Identifying Promoters and Their Types in Plant Genomes by Convolutional Neural Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6636350. [PMID: 33488763 PMCID: PMC7803414 DOI: 10.1155/2021/6636350] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 12/13/2020] [Accepted: 12/16/2020] [Indexed: 11/18/2022]
Abstract
A promoter is a short DNA sequence near to the start codon, responsible for initiating transcription of a specific gene in genome. The accurate recognition of promoters has great significance for a better understanding of the transcriptional regulation. Because of their importance in the process of biological transcriptional regulation, there is an urgent need to develop in silico tools to identify promoters and their types timely and accurately. A number of prediction methods had been developed in this regard; however, almost all of them were merely used for identifying promoters and their strength or sigma types. Owing to that TATA box region in TATA promoter that influences posttranscriptional processes, in the current study, we developed a two-layer predictor called iPTT(2L)-CNN by using the convolutional neural network (CNN) for identifying TATA and TATA-less promoters. The first layer can be used to identify a given DNA sequence as a promoter or nonpromoter. The second layer is used to identify whether the recognized promoter is TATA promoter or not. The 5-fold crossvalidation and independent testing results demonstrate that the constructed predictor is promising for identifying promoter and classifying TATA and TATA-less promoter. Furthermore, to make it easier for most experimental scientists get the results they need, a user-friendly web server has been established at http://www.jci-bioinfo.cn/iPPT(2L)-CNN.
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47
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Dao FY, Lv H, Zhang D, Zhang ZM, Liu L, Lin H. DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops. Brief Bioinform 2020; 22:6024741. [PMID: 33279983 DOI: 10.1093/bib/bbaa356] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 10/19/2020] [Accepted: 11/04/2020] [Indexed: 12/29/2022] Open
Abstract
The protein Yin Yang 1 (YY1) could form dimers that facilitate the interaction between active enhancers and promoter-proximal elements. YY1-mediated enhancer-promoter interaction is the general feature of mammalian gene control. Recently, some computational methods have been developed to characterize the interactions between DNA elements by elucidating important features of chromatin folding; however, no computational methods have been developed for identifying the YY1-mediated chromatin loops. In this study, we developed a deep learning algorithm named DeepYY1 based on word2vec to determine whether a pair of YY1 motifs would form a loop. The proposed models showed a high prediction performance (AUCs$\ge$0.93) on both training datasets and testing datasets in different cell types, demonstrating that DeepYY1 has an excellent performance in the identification of the YY1-mediated chromatin loops. Our study also suggested that sequences play an important role in the formation of YY1-mediated chromatin loops. Furthermore, we briefly discussed the distribution of the replication origin site in the loops. Finally, a user-friendly web server was established, and it can be freely accessed at http://lin-group.cn/server/DeepYY1.
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Affiliation(s)
- Fu-Ying Dao
- Center for Informational Biology at the University of Electronic Science and Technology of China
| | - Hao Lv
- Center for Informational Biology at the University of Electronic Science and Technology of China
| | - Dan Zhang
- Center for Informational Biology at the University of Electronic Science and Technology of China
| | - Zi-Mei Zhang
- Center for Informational Biology at the University of Electronic Science and Technology of China
| | - Li Liu
- Laboratory of Theoretical Biophysics at the Inner Mongolia University
| | - Hao Lin
- Center for Informational Biology at the University of Electronic Science and Technology of China
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48
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Manavalan B, Basith S, Shin TH, Lee G. Computational prediction of species-specific yeast DNA replication origin via iterative feature representation. Brief Bioinform 2020; 22:6000361. [PMID: 33232970 PMCID: PMC8294535 DOI: 10.1093/bib/bbaa304] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 10/08/2020] [Accepted: 10/09/2020] [Indexed: 12/13/2022] Open
Abstract
Deoxyribonucleic acid replication is one of the most crucial tasks taking place in the cell, and it has to be precisely regulated. This process is initiated in the replication origins (ORIs), and thus it is essential to identify such sites for a deeper understanding of the cellular processes and functions related to the regulation of gene expression. Considering the important tasks performed by ORIs, several experimental and computational approaches have been developed in the prediction of such sites. However, existing computational predictors for ORIs have certain curbs, such as building only single-feature encoding models, limited systematic feature engineering efforts and failure to validate model robustness. Hence, we developed a novel species-specific yeast predictor called yORIpred that accurately identify ORIs in the yeast genomes. To develop yORIpred, we first constructed optimal 40 baseline models by exploring eight different sequence-based encodings and five different machine learning classifiers. Subsequently, the predicted probability of 40 models was considered as the novel feature vector and carried out iterative feature learning approach independently using five different classifiers. Our systematic analysis revealed that the feature representation learned by the support vector machine algorithm (yORIpred) could well discriminate the distribution characteristics between ORIs and non-ORIs when compared with the other four algorithms. Comprehensive benchmarking experiments showed that yORIpred achieved superior and stable performance when compared with the existing predictors on the same training datasets. Furthermore, independent evaluation showcased the best and accurate performance of yORIpred thus underscoring the significance of iterative feature representation. To facilitate the users in obtaining their desired results without undergoing any mathematical, statistical or computational hassles, we developed a web server for the yORIpred predictor, which is available at: http://thegleelab.org/yORIpred.
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Affiliation(s)
| | - Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Republic of Korea
| | - Tae Hwan Shin
- Department of Physiology, Ajou University School of Medicine, Republic of Korea
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Republic of Korea
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49
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Wei L, He W, Malik A, Su R, Cui L, Manavalan B. Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework. Brief Bioinform 2020; 22:5956930. [PMID: 33152766 DOI: 10.1093/bib/bbaa275] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 09/14/2020] [Accepted: 09/21/2020] [Indexed: 12/13/2022] Open
Abstract
Origins of replication sites (ORIs), which refers to the initiative locations of genomic DNA replication, play essential roles in DNA replication process. Detection of ORIs' distribution in genome scale is one of key steps to in-depth understanding their regulation mechanisms. In this study, we presented a novel machine learning-based approach called Stack-ORI encompassing 10 cell-specific prediction models for identifying ORIs from four different eukaryotic species (Homo sapiens, Mus musculus, Drosophila melanogaster and Arabidopsis thaliana). For each cell-specific model, we employed 12 feature encoding schemes that cover nucleic acid composition, position-specific and physicochemical properties information. The optimal feature set was identified from each encoding individually and developed their respective baseline models using the eXtreme Gradient Boosting (XGBoost) classifier. Subsequently, the predicted scores of 12 baseline models are integrated as a novel feature vector to train XGBoost and develop the final model. Extensive experimental results show that Stack-ORI achieves significantly better performance as compared with their baseline models on both training and independent datasets. Interestingly, Stack-ORI consistently outperforms existing predictor in all cell-specific models, not only on training but also on independent test. Moreover, our novel approach provides necessary interpretations that help understanding model success by leveraging the powerful SHapley Additive exPlanation algorithm, thus underlining the most important feature encoding schemes significant for predicting cell-specific ORIs.
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Affiliation(s)
- Leyi Wei
- computer science from Xiamen University, China
| | - Wenjia He
- School of Software at Shandong University, China
| | - Adeel Malik
- Institute of Intelligence Informatics Technology, Sangmyung University, Seoul, Republic of Korea
| | - Ran Su
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Lizhen Cui
- School of Software, Shandong University, the Deputy Director of the E-Commerce Research Center and the Director of the Research Center of Software and Data Engineering, Jinan
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50
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Sequence based prediction of pattern recognition receptors by using feature selection technique. Int J Biol Macromol 2020; 162:931-934. [DOI: 10.1016/j.ijbiomac.2020.06.234] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 06/23/2020] [Accepted: 06/24/2020] [Indexed: 01/04/2023]
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