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Cong H, Liu H, Cao Y, Chen Y, Liang C. Multiple Protein Subcellular Locations Prediction Based on Deep Convolutional Neural Networks with Self-Attention Mechanism. Interdiscip Sci 2022; 14:421-438. [PMID: 35066812 DOI: 10.1007/s12539-021-00496-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 12/06/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022]
Abstract
As an important research field in bioinformatics, protein subcellular location prediction is critical to reveal the protein functions and provide insightful information for disease diagnosis and drug development. Predicting protein subcellular locations remains a challenging task due to the difficulty of finding representative features and robust classifiers. Many feature fusion methods have been widely applied to tackle the above issues. However, they still suffer from accuracy loss due to feature redundancy. Furthermore, multiple protein subcellular locations prediction is more complicated since it is fundamentally a multi-label classification problem. The traditional binary classifiers or even multi-class classifiers cannot achieve satisfactory results. This paper proposes a novel method for protein subcellular location prediction with both single and multiple sites based on deep convolutional neural networks. Specifically, we first obtain the integrated features by simultaneously considering the pseudo amino acid, amino acid index distribution, and physicochemical property. We then adopt deep convolutional neural networks to extract high-dimensional features from the fused feature, removing the redundant preliminary features and gaining better representations of the raw sequences. Moreover, we use the self-attention mechanism and a customized loss function to ensure that the model is more inclined to positive data. In addition, we use random k-label sets to reduce the number of prediction labels. Meanwhile, we employ a hybrid strategy of over-sampling and under-sampling to tackle the data imbalance problem. We compare our model with three representative classification alternatives. The experiment results show that our model achieves the best performance in terms of accuracy, demonstrating the efficacy of the proposed model.
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Affiliation(s)
- Hanhan Cong
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
- Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan, China
| | - Hong Liu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China.
- Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan, China.
| | - Yi Cao
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key Laboratory of Network Based Intelligent, Computing University of Jinan, Jinan, China
| | - Yuehui Chen
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key Laboratory of Network Based Intelligent, Computing University of Jinan, Jinan, China
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
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2
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Gao X, Bian X. Autonomous driving of vehicles based on artificial intelligence. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189982] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
With the fast development, Internet technology has become a game-changer to the automotive industry. The advances and general applications of high-precision maps make it possible for accurate real-time positioning of vehicles. Meanwhile, the extensive applications of intelligent driving technology make it easier and more intelligent to drive vehicles. This paper reviewed the application of artificial intelligence (AI) in the field of autonomous driving comprehensively and explored the innovative studies of other unmanned motion systems at the same time. Firstly, the hardware architecture of the autonomous driving system is introduced, including five modules as follows: sensing, autonomous driving computer, power supply, signal communication, execution and braking. In addition, General Motors autonomous vehicle is used as an example to introduce its differences from the traditional vehicles in the hardware part. Subsequently, the autonomous driving software is divided into four modules according to functions: positioning, sensing, planning, and control, and the innovative application of artificial intelligence algorithms is introduced. Finally, this paper expands from autonomous driving technology and puts forward an innovative research idea for the intelligent unmanned system.
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Affiliation(s)
- Xianping Gao
- School of Mechanical Engineering, Hebei University of Technology, Tianjin, China
- School of Automotive & Transportation, Tianjin University of Technology & Education, Tianjin, China
| | - Xueliang Bian
- School of Mechanical Engineering, Hebei University of Technology, Tianjin, China
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3
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Ras-Carmona A, Gomez-Perosanz M, Reche PA. Prediction of unconventional protein secretion by exosomes. BMC Bioinformatics 2021; 22:333. [PMID: 34134630 PMCID: PMC8210391 DOI: 10.1186/s12859-021-04219-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/21/2021] [Indexed: 01/08/2023] Open
Abstract
MOTIVATION In eukaryotes, proteins targeted for secretion contain a signal peptide, which allows them to proceed through the conventional ER/Golgi-dependent pathway. However, an important number of proteins lacking a signal peptide can be secreted through unconventional routes, including that mediated by exosomes. Currently, no method is available to predict protein secretion via exosomes. RESULTS Here, we first assembled a dataset including the sequences of 2992 proteins secreted by exosomes and 2961 proteins that are not secreted by exosomes. Subsequently, we trained different random forests models on feature vectors derived from the sequences in this dataset. In tenfold cross-validation, the best model was trained on dipeptide composition, reaching an accuracy of 69.88% ± 2.08 and an area under the curve (AUC) of 0.76 ± 0.03. In an independent dataset, this model reached an accuracy of 75.73% and an AUC of 0.840. After these results, we developed ExoPred, a web-based tool that uses random forests to predict protein secretion by exosomes. CONCLUSION ExoPred is available for free public use at http://imath.med.ucm.es/exopred/ . Datasets are available at http://imath.med.ucm.es/exopred/datasets/ .
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Affiliation(s)
- Alvaro Ras-Carmona
- Laboratory of Immunomedicine, Department of Immunology, Faculty of Medicine, Complutense University of Madrid, Pza Ramón y Cajal, s/n, 28040 Madrid, Spain
| | - Marta Gomez-Perosanz
- Laboratory of Immunomedicine, Department of Immunology, Faculty of Medicine, Complutense University of Madrid, Pza Ramón y Cajal, s/n, 28040 Madrid, Spain
| | - Pedro A. Reche
- Laboratory of Immunomedicine, Department of Immunology, Faculty of Medicine, Complutense University of Madrid, Pza Ramón y Cajal, s/n, 28040 Madrid, Spain
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4
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Du L, Meng Q, Chen Y, Wu P. Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA. BMC Bioinformatics 2020; 21:212. [PMID: 32448129 PMCID: PMC7245797 DOI: 10.1186/s12859-020-3539-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 05/06/2020] [Indexed: 11/13/2022] Open
Abstract
Background Apoptosis, also called programmed cell death, refers to the spontaneous and orderly death of cells controlled by genes in order to maintain a stable internal environment. Identifying the subcellular location of apoptosis proteins is very helpful in understanding the mechanism of apoptosis and designing drugs. Therefore, the subcellular localization of apoptosis proteins has attracted increased attention in computational biology. Effective feature extraction methods play a critical role in predicting the subcellular location of proteins. Results In this paper, we proposed two novel feature extraction methods based on evolutionary information. One of the features obtained the evolutionary information via the transition matrix of the consensus sequence (CTM). And the other utilized the evolutionary information from PSSM based on absolute entropy correlation analysis (AECA-PSSM). After fusing the two kinds of features, linear discriminant analysis (LDA) was used to reduce the dimension of the proposed features. Finally, the support vector machine (SVM) was adopted to predict the protein subcellular locations. The proposed CTM-AECA-PSSM-LDA subcellular location prediction method was evaluated using the CL317 dataset and ZW225 dataset. By jackknife test, the overall accuracy was 99.7% (CL317) and 95.6% (ZW225) respectively. Conclusions The experimental results show that the proposed method which is hopefully to be a complementary tool for the existing methods of subcellular localization, can effectively extract more abundant features of protein sequence and is feasible in predicting the subcellular location of apoptosis proteins.
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Affiliation(s)
- Lei Du
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, China.,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
| | - Qingfang Meng
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, China. .,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China.
| | - Yuehui Chen
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, China.,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
| | - Peng Wu
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, China.,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
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5
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Medina-Ortiz D, Contreras S, Quiroz C, Olivera-Nappa Á. Development of Supervised Learning Predictive Models for Highly Non-linear Biological, Biomedical, and General Datasets. Front Mol Biosci 2020; 7:13. [PMID: 32118039 PMCID: PMC7031350 DOI: 10.3389/fmolb.2020.00013] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 01/22/2020] [Indexed: 11/13/2022] Open
Abstract
In highly non-linear datasets, attributes or features do not allow readily finding visual patterns for identifying common underlying behaviors. Therefore, it is not possible to achieve classification or regression using linear or mildly non-linear hyperspace partition functions. Hence, supervised learning models based on the application of most existing algorithms are limited, and their performance metrics are low. Linear transformations of variables, such as principal components analysis, cannot avoid the problem, and even models based on artificial neural networks and deep learning are unable to improve the metrics. Sometimes, even when features allow classification or regression in reported cases, performance metrics of supervised learning algorithms remain unsatisfyingly low. This problem is recurrent in many areas of study as, per example, the clinical, biotechnological, and protein engineering areas, where many of the attributes are correlated in an unknown and very non-linear fashion or are categorical and difficult to relate to a target response variable. In such areas, being able to create predictive models would dramatically impact the quality of their outcomes, generating an immediate added value for both the scientific and general public. In this manuscript, we present RV-Clustering, a library of unsupervised learning algorithms, and a new methodology designed to find optimum partitions within highly non-linear datasets that allow deconvoluting variables and notoriously improving performance metrics in supervised learning classification or regression models. The partitions obtained are statistically cross-validated, ensuring correct representativity and no over-fitting. We have successfully tested RV-Clustering in several highly non-linear datasets with different origins. The approach herein proposed has generated classification and regression models with high-performance metrics, which further supports its ability to generate predictive models for highly non-linear datasets. Advantageously, the method does not require significant human input, which guarantees a higher usability in the biological, biomedical, and protein engineering community with no specific knowledge in the machine learning area.
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Affiliation(s)
- David Medina-Ortiz
- Departamento de Ingeniería Química, Biotecnología y Materiales, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Santiago, Chile.,Centre for Biotechnology and Bioengineering, Universidad de Chile, Santiago, Chile
| | - Sebastián Contreras
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Santiago, Chile
| | - Cristofer Quiroz
- Facultad de Ingeniería, Universidad Autónoma de Chile, Talca, Chile
| | - Álvaro Olivera-Nappa
- Departamento de Ingeniería Química, Biotecnología y Materiales, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Santiago, Chile.,Centre for Biotechnology and Bioengineering, Universidad de Chile, Santiago, Chile
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6
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Protein sequence information extraction and subcellular localization prediction with gapped k-Mer method. BMC Bioinformatics 2019; 20:719. [PMID: 31888447 PMCID: PMC6936157 DOI: 10.1186/s12859-019-3232-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Subcellular localization prediction of protein is an important component of bioinformatics, which has great importance for drug design and other applications. A multitude of computational tools for proteins subcellular location have been developed in the recent decades, however, existing methods differ in the protein sequence representation techniques and classification algorithms adopted. RESULTS In this paper, we firstly introduce two kinds of protein sequences encoding schemes: dipeptide information with space and Gapped k-mer information. Then, the Gapped k-mer calculation method which is based on quad-tree is also introduced. CONCLUSIONS >From the prediction results, this method not only reduces the dimension, but also improves the prediction precision of protein subcellular localization.
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7
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Ruan X, Zhou D, Nie R, Hou R, Cao Z. Prediction of apoptosis protein subcellular location based on position-specific scoring matrix and isometric mapping algorithm. Med Biol Eng Comput 2019; 57:2553-2565. [PMID: 31621050 DOI: 10.1007/s11517-019-02045-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 09/04/2019] [Indexed: 01/04/2023]
Abstract
Apoptosis proteins are related to many diseases. Obtaining the subcellular localization information of apoptosis proteins is helpful to understand the mechanism of diseases and to develop new drugs. At present, the researchers mainly focus on the primary protein sequences, so there is still room for improvement in the prediction accuracy of the subcellular localization of apoptosis proteins. In this paper, a new method named ERT-ECT-PSSM-IS is proposed to predict apoptosis proteins based on the position-specific scoring matrix (PSSM). First, the local and global features of different directions are extracted by evolutionary row transformation (ERT) and cross-covariance of evolutionary column transformation (ECT) based on PSSM (ERT-ECT-PSSM). Second, an improved isometric mapping algorithm (I-SMA) is used to eliminate redundant features. Finally, we adopt a support vector machine (SVM) to classify our results, and the prediction accuracy is evaluated by jackknife cross-validation tests. The experimental results show that the proposed method not only extracts more abundant feature expression but also has better predictive performance and robustness for the subcellular localization of apoptosis proteins in ZD98, ZW225, and CL317 databases. Graphical abstract Framework of the proposed prediction model.
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Affiliation(s)
- Xiaoli Ruan
- Information College, Yunnan University, Kunming, 650504, China
| | - Dongming Zhou
- Information College, Yunnan University, Kunming, 650504, China.
| | - Rencan Nie
- Information College, Yunnan University, Kunming, 650504, China
| | - Ruichao Hou
- Information College, Yunnan University, Kunming, 650504, China
| | - Zicheng Cao
- School of Public Health, Sun Yat-sen University, Shenzhen, 510080, China
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8
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Prediction of Protein-Protein Interactions Based on Domain. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:5238406. [PMID: 31531123 PMCID: PMC6720845 DOI: 10.1155/2019/5238406] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 07/09/2019] [Accepted: 07/30/2019] [Indexed: 11/17/2022]
Abstract
Protein-protein interactions (PPIs) play a crucial role in various biological processes. To better comprehend the pathogenesis and treatments of various diseases, it is necessary to learn the detail of these interactions. However, the current experimental method still has many false-positive and false-negative problems. Computational prediction of protein-protein interaction has become a more important prediction method which can overcome the obstacles of the experimental method. In this work, we proposed a novel computational domain-based method for PPI prediction, and an SVM model for the prediction was built based on the physicochemical property of the domain. The outcomes of SVM and the domain-domain score were used to construct the prediction model for protein-protein interaction. The predicted results demonstrated the domain-based research can enhance the ability to predict protein interactions.
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9
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Lin J, Chen H, Li S, Liu Y, Li X, Yu B. Accurate prediction of potential druggable proteins based on genetic algorithm and Bagging-SVM ensemble classifier. Artif Intell Med 2019; 98:35-47. [DOI: 10.1016/j.artmed.2019.07.005] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2018] [Revised: 03/03/2019] [Accepted: 07/18/2019] [Indexed: 12/14/2022]
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10
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Jin H, Ma N, Li X, Kang M, Guo M, Song L. Application of GC/MS-Based Metabonomic Profiling in Studying the Therapeutic Effects of Aconitum carmichaeli with Ampelopsis japonica Extract on Collagen-Induced Arthritis in Rats. Molecules 2019; 24:molecules24101934. [PMID: 31137469 PMCID: PMC6571615 DOI: 10.3390/molecules24101934] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Revised: 05/11/2019] [Accepted: 05/16/2019] [Indexed: 11/16/2022] Open
Abstract
Aconitum carmichaeli with Ampelopsis japonica (AA) is a classical traditional Chinese medicine (TCM) formula. There are a lot of examples showing that AA can be used to treat rheumatoid arthritis, but its mechanism of action is still not completely clear. In this research, collagen-induced arthritis (CIA) was chosen as a rheumatoid arthritis (RA) model. Rats of treated groups were continuously administered Aconitum carmichaeli (AC), Ampelopsis japonica (AJ) and Aconitum carmichaeli + Ampelopsis japonica (AA) orally once a day from the day after the onset of arthritis (day 7) until day 42. The results showed that AA not only significantly reduced paw swelling, but also improved the levels of TNF-α and IL-6 in serum. GC-MS-based urine metabonomics was established to analysis metabolic profiles and 21 biomarkers of RA rats were identified by the Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) methods. The prediction rate of the SVM method for the 21 biomarkers was 100%. Twenty of 21 biomarkers, including D-galactose, inositol and glycerol, gradually returned to normal levels after administration of AA. Metabolomic Pathway Analysis (MetPA) generated three related metabolic pathways-galactose metabolism, glycerolipid metabolism and inositol phosphate metabolism-which explain the mechanism of AA treatment of rheumatoid arthritis. This research provides a better understanding of the therapeutic effects and possible therapeutic mechanism of action of a complex TCM (AA) on rheumatoid arthritis.
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Affiliation(s)
- Hua Jin
- College of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Jian Kang Chan Ye Yuan, Jinghai Dist., Tianjin 301617, China.
| | - Ningning Ma
- School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Jian Kang Chan Ye Yuan, Jinghai Dist., Tianjin 301617, China.
| | - Xin Li
- School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Jian Kang Chan Ye Yuan, Jinghai Dist., Tianjin 301617, China.
| | - Mingqin Kang
- Changchun Customs (Former Jilin Inspection and Quarantine Bureau), Changchun 130012, China.
| | - Maojuan Guo
- Department of Pathology, School of integrative Medicine, Tianjin University of Traditional Chinese Medicine, Jian Kang Chan Ye Yuan, Jinghai Dist., Tianjin 301617, China.
| | - Lili Song
- School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Jian Kang Chan Ye Yuan, Jinghai Dist., Tianjin 301617, China.
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11
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Predicting Apoptosis Protein Subcellular Locations based on the Protein Overlapping Property Matrix and Tri-Gram Encoding. Int J Mol Sci 2019; 20:ijms20092344. [PMID: 31083553 PMCID: PMC6539631 DOI: 10.3390/ijms20092344] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 04/25/2019] [Accepted: 05/08/2019] [Indexed: 12/22/2022] Open
Abstract
To reveal the working pattern of programmed cell death, knowledge of the subcellular location of apoptosis proteins is essential. Besides the costly and time-consuming method of experimental determination, research into computational locating schemes, focusing mainly on the innovation of representation techniques on protein sequences and the selection of classification algorithms, has become popular in recent decades. In this study, a novel tri-gram encoding model is proposed, which is based on using the protein overlapping property matrix (POPM) for predicting apoptosis protein subcellular location. Next, a 1000-dimensional feature vector is built to represent a protein. Finally, with the help of support vector machine-recursive feature elimination (SVM-RFE), we select the optimal features and put them into a support vector machine (SVM) classifier for predictions. The results of jackknife tests on two benchmark datasets demonstrate that our proposed method can achieve satisfactory prediction performance level with less computing capacity required and could work as a promising tool to predict the subcellular locations of apoptosis proteins.
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12
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Tian B, Wu X, Chen C, Qiu W, Ma Q, Yu B. Predicting protein–protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach. J Theor Biol 2019; 462:329-346. [DOI: 10.1016/j.jtbi.2018.11.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 11/08/2018] [Accepted: 11/15/2018] [Indexed: 12/26/2022]
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13
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Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach. BIOMED RESEARCH INTERNATIONAL 2019; 2019:2436924. [PMID: 30834257 PMCID: PMC6374881 DOI: 10.1155/2019/2436924] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 01/04/2019] [Accepted: 01/20/2019] [Indexed: 11/29/2022]
Abstract
The prediction of apoptosis protein subcellular localization plays an important role in understanding the progress in cell proliferation and death. Recently computational approaches to this issue have become very popular, since the traditional biological experiments are so costly and time-consuming that they cannot catch up with the growth rate of sequence data anymore. In order to improve the prediction accuracy of apoptosis protein subcellular localization, we proposed a sparse coding method combined with traditional feature extraction algorithm to complete the sparse representation of apoptosis protein sequences, using multilayer pooling based on different sizes of dictionaries to integrate the processed features, as well as oversampling approach to decrease the influences caused by unbalanced data sets. Then the extracted features were input to a support vector machine to predict the subcellular localization of the apoptosis protein. The experiment results obtained by Jackknife test on two benchmark data sets indicate that our method can significantly improve the accuracy of the apoptosis protein subcellular localization prediction.
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14
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Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition. J Theor Biol 2018; 450:86-103. [DOI: 10.1016/j.jtbi.2018.04.026] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 04/10/2018] [Accepted: 04/16/2018] [Indexed: 01/16/2023]
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15
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Yu B, Li S, Qiu W, Wang M, Du J, Zhang Y, Chen X. Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction. BMC Genomics 2018; 19:478. [PMID: 29914358 PMCID: PMC6006758 DOI: 10.1186/s12864-018-4849-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 06/01/2018] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Apoptosis is associated with some human diseases, including cancer, autoimmune disease, neurodegenerative disease and ischemic damage, etc. Apoptosis proteins subcellular localization information is very important for understanding the mechanism of programmed cell death and the development of drugs. Therefore, the prediction of subcellular localization of apoptosis protein is still a challenging task. RESULTS In this paper, we propose a novel method for predicting apoptosis protein subcellular localization, called PsePSSM-DCCA-LFDA. Firstly, the protein sequences are extracted by combining pseudo-position specific scoring matrix (PsePSSM) and detrended cross-correlation analysis coefficient (DCCA coefficient), then the extracted feature information is reduced dimensionality by LFDA (local Fisher discriminant analysis). Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of the apoptosis proteins. The overall prediction accuracy of 99.7, 99.6 and 100% are achieved respectively on the three benchmark datasets by the most rigorous jackknife test, which is better than other state-of-the-art methods. CONCLUSION The experimental results indicate that our method can significantly improve the prediction accuracy of subcellular localization of apoptosis proteins, which is quite high to be able to become a promising tool for further proteomics studies. The source code and all datasets are available at https://github.com/QUST-BSBRC/PsePSSM-DCCA-LFDA/ .
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Affiliation(s)
- Bin Yu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China. .,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China. .,School of Life Sciences, University of Science and Technology of China, Hefei, 230027, China.
| | - Shan Li
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China.,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Wenying Qiu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China.,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Minghui Wang
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China.,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Junwei Du
- College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Yusen Zhang
- School of Mathematics and Statistics, Shandong University at Weihai, Weihai, 264209, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 21116, China
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16
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Tang Y, Xie L, Chen L. iAPSL-IF: Identification of Apoptosis Protein Subcellular Location Using Integrative Features Captured from Amino Acid Sequences. Int J Mol Sci 2018; 19:ijms19041190. [PMID: 29652843 PMCID: PMC5979326 DOI: 10.3390/ijms19041190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 04/07/2018] [Accepted: 04/09/2018] [Indexed: 11/24/2022] Open
Abstract
Apoptosis proteins (APs) control normal tissue homeostasis by regulating the balance between cell proliferation and death. The function of APs is strongly related to their subcellular location. To date, computational methods have been reported that reliably identify the subcellular location of APs, however, there is still room for improvement of the prediction accuracy. In this study, we developed a novel method named iAPSL-IF (identification of apoptosis protein subcellular location—integrative features), which is based on integrative features captured from Markov chains, physicochemical property matrices, and position-specific score matrices (PSSMs) of amino acid sequences. The matrices with different lengths were transformed into fixed-length feature vectors using an auto cross-covariance (ACC) method. An optimal subset of the features was chosen using a recursive feature elimination (RFE) algorithm method, and the sequences with these features were trained by a support vector machine (SVM) classifier. Based on three datasets ZD98, CL317, and ZW225, the iAPSL-IF was examined using a jackknife cross-validation test. The resulting data showed that the iAPSL-IF outperformed the known predictors reported in the literature: its overall accuracy on the three datasets was 98.98% (ZD98), 94.95% (CL317), and 97.33% (ZW225), respectively; the Matthews correlation coefficient, sensitivity, and specificity for several classes of subcellular location proteins (e.g., membrane proteins, cytoplasmic proteins, endoplasmic reticulum proteins, nuclear proteins, and secreted proteins) in the datasets were 0.92–1.0, 94.23–100%, and 97.07–100%, respectively. Overall, the results of this study provide a high throughput and sequence-based method for better identification of the subcellular location of APs, and facilitates further understanding of programmed cell death in organisms.
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Affiliation(s)
- Yadong Tang
- Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture, College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
| | - Lu Xie
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China.
| | - Lanming Chen
- Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture, College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
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17
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Wang S, Yue Y. Protein subnuclear localization based on a new effective representation and intelligent kernel linear discriminant analysis by dichotomous greedy genetic algorithm. PLoS One 2018; 13:e0195636. [PMID: 29649330 PMCID: PMC5896989 DOI: 10.1371/journal.pone.0195636] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 03/26/2018] [Indexed: 01/03/2023] Open
Abstract
A wide variety of methods have been proposed in protein subnuclear localization to improve the prediction accuracy. However, one important trend of these means is to treat fusion representation by fusing multiple feature representations, of which, the fusion process takes a lot of time. In view of this, this paper novelly proposed a method by combining a new single feature representation and a new algorithm to obtain good recognition rate. Specifically, based on the position-specific scoring matrix (PSSM), we proposed a new expression, correlation position-specific scoring matrix (CoPSSM) as the protein feature representation. Based on the classic nonlinear dimension reduction algorithm, kernel linear discriminant analysis (KLDA), we added a new discriminant criterion and proposed a dichotomous greedy genetic algorithm (DGGA) to intelligently select its kernel bandwidth parameter. Two public datasets with Jackknife test and KNN classifier were used for the numerical experiments. The results showed that the overall success rate (OSR) with single representation CoPSSM is larger than that with many relevant representations. The OSR of the proposed method can reach as high as 87.444% and 90.3361% for these two datasets, respectively, outperforming many current methods. To show the generalization of the proposed algorithm, two extra standard datasets of protein subcellular were chosen to conduct the expending experiment, and the prediction accuracy by Jackknife test and Independent test is still considerable.
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Affiliation(s)
- Shunfang Wang
- School of Information Science and Engineering, Yunnan University, Kunming, PR China
- * E-mail:
| | - Yaoting Yue
- School of Information Science and Engineering, Yunnan University, Kunming, PR China
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18
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Tahir M, Jan B, Hayat M, Shah SU, Amin M. Efficient computational model for classification of protein localization images using Extended Threshold Adjacency Statistics and Support Vector Machines. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 157:205-215. [PMID: 29477429 DOI: 10.1016/j.cmpb.2018.01.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 01/02/2018] [Accepted: 01/24/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Discriminative and informative feature extraction is the core requirement for accurate and efficient classification of protein subcellular localization images so that drug development could be more effective. The objective of this paper is to propose a novel modification in the Threshold Adjacency Statistics technique and enhance its discriminative power. METHODS In this work, we utilized Threshold Adjacency Statistics from a novel perspective to enhance its discrimination power and efficiency. In this connection, we utilized seven threshold ranges to produce seven distinct feature spaces, which are then used to train seven SVMs. The final prediction is obtained through the majority voting scheme. The proposed ETAS-SubLoc system is tested on two benchmark datasets using 5-fold cross-validation technique. RESULTS We observed that our proposed novel utilization of TAS technique has improved the discriminative power of the classifier. The ETAS-SubLoc system has achieved 99.2% accuracy, 99.3% sensitivity and 99.1% specificity for Endogenous dataset outperforming the classical Threshold Adjacency Statistics technique. Similarly, 91.8% accuracy, 96.3% sensitivity and 91.6% specificity values are achieved for Transfected dataset. CONCLUSIONS Simulation results validated the effectiveness of ETAS-SubLoc that provides superior prediction performance compared to the existing technique. The proposed methodology aims at providing support to pharmaceutical industry as well as research community towards better drug designing and innovation in the fields of bioinformatics and computational biology. The implementation code for replicating the experiments presented in this paper is available at: https://drive.google.com/file/d/0B7IyGPObWbSqRTRMcXI2bG5CZWs/view?usp=sharing.
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Affiliation(s)
- Muhammad Tahir
- College of Computing and Informatics, Saudi Electronic University, Al-Madinah Branch, Saudi Arabia
| | - Bismillah Jan
- Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan; Department of Computer Science, National University of Computer and Emerging Sciences, Peshawar Campus, Pakistan
| | - Maqsood Hayat
- Department of Computer Science, University College of Sciences, Shanker, Abdul Wali Khan University, Mardan, Pakistan.
| | - Shakir Ullah Shah
- Department of Computer Science, National University of Computer and Emerging Sciences, Peshawar Campus, Pakistan
| | - Muhammad Amin
- Department of Computer Science, National University of Computer and Emerging Sciences, Peshawar Campus, Pakistan
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19
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Accurate prediction of subcellular location of apoptosis proteins combining Chou's PseAAC and PsePSSM based on wavelet denoising. Oncotarget 2017; 8:107640-107665. [PMID: 29296195 PMCID: PMC5746097 DOI: 10.18632/oncotarget.22585] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Accepted: 10/30/2017] [Indexed: 02/05/2023] Open
Abstract
Apoptosis proteins subcellular localization information are very important for understanding the mechanism of programmed cell death and the development of drugs. The prediction of subcellular localization of an apoptosis protein is still a challenging task because the prediction of apoptosis proteins subcellular localization can help to understand their function and the role of metabolic processes. In this paper, we propose a novel method for protein subcellular localization prediction. Firstly, the features of the protein sequence are extracted by combining Chou's pseudo amino acid composition (PseAAC) and pseudo-position specific scoring matrix (PsePSSM), then the feature information of the extracted is denoised by two-dimensional (2-D) wavelet denoising. Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of apoptosis proteins. Quite promising predictions are obtained using the jackknife test on three widely used datasets and compared with other state-of-the-art methods. The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of apoptosis protein subcellular localization, which will be a supplementary tool for future proteomics research.
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