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Wang TY, Li JF, Zhang HD, Chen JZY. Designs to Improve Capability of Neural Networks to Make Structural Predictions. CHINESE JOURNAL OF POLYMER SCIENCE 2023. [DOI: 10.1007/s10118-023-2910-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
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2
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Issa M. Expeditious COVID-19 similarity measure tool based on consolidated SCA algorithm with mutation and opposition operators. Appl Soft Comput 2021; 104:107197. [PMID: 33642960 PMCID: PMC7895693 DOI: 10.1016/j.asoc.2021.107197] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 02/09/2021] [Accepted: 02/15/2021] [Indexed: 11/21/2022]
Abstract
COVID-19 is a global pandemic that aroused the interest of scientists to prevent it and design a drug for it. Nowadays, presenting intelligent biological data analysis tools at a low cost is important to analyze the biological structure of COVID-19. The global alignment algorithm is one of the important bioinformatics tools that measure the most accurate similarity between a pair of biological sequences. The huge time consumption of the standard global alignment algorithm is its main limitation especially for sequences with huge lengths. This work proposed a fast global alignment tool (G-Aligner) based on meta-heuristic algorithms that estimate similarity measurements near the exact ones at a reasonable time with low cost. The huge length of sequences leads G-Aligner based on standard Sine–Cosine optimization algorithm (SCA) to trap in local minima. Therefore, an improved version of SCA was presented in this work that is based on integration with PSO. Besides, mutation and opposition operators are applied to enhance the exploration capability and avoiding trapping in local minima. The performance of the improved SCA algorithm (SP-MO) was evaluated on a set of IEEE CEC functions. Besides, G-Aligner based on the SP-MO algorithm was tested to measure the similarity of real biological sequence. It was used also to measure the similarity of the COVID-19 virus with the other 13 viruses to validate its performance. The tests concluded that the SP-MO algorithm has superiority over the relevant studies in the literature and produce the highest average similarity measurements 75% of the exact one.
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Affiliation(s)
- Mohamed Issa
- Computer and Systems Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt.,Faculty of Computers and Informatics, Nahda University, Beni Suef, Egypt
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Tang J, Wang Y, Luo Y, Fu J, Zhang Y, Li Y, Xiao Z, Lou Y, Qiu Y, Zhu F. Computational advances of tumor marker selection and sample classification in cancer proteomics. Comput Struct Biotechnol J 2020; 18:2012-2025. [PMID: 32802273 PMCID: PMC7403885 DOI: 10.1016/j.csbj.2020.07.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/06/2020] [Accepted: 07/08/2020] [Indexed: 12/11/2022] Open
Abstract
Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.
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Key Words
- ANN, Artificial Neural Network
- ANOVA, Analysis of Variance
- CFS, Correlation-based Feature Selection
- Cancer proteomics
- Computational methods
- DAPC, Discriminant Analysis of Principal Component
- DT, Decision Trees
- EDA, Estimation of Distribution Algorithm
- FC, Fold Change
- GA, Genetic Algorithms
- GR, Gain Ratio
- HC, Hill Climbing
- HCA, Hierarchical Cluster Analysis
- IG, Information Gain
- LDA, Linear Discriminant Analysis
- LIMMA, Linear Models for Microarray Data
- MBF, Markov Blanket Filter
- MWW, Mann–Whitney–Wilcoxon test
- OPLS-DA, Orthogonal Partial Least Squares Discriminant Analysis
- PCA, Principal Component Analysis
- PLS-DA, Partial Least Square Discriminant Analysis
- RF, Random Forest
- RF-RFE, Random Forest with Recursive Feature Elimination
- SA, Simulated Annealing
- SAM, Significance Analysis of Microarrays
- SBE, Sequential Backward Elimination
- SFS, and Sequential Forward Selection
- SOM, Self-organizing Map
- SU, Symmetrical Uncertainty
- SVM, Support Vector Machine
- SVM-RFE, Support Vector Machine with Recursive Feature Elimination
- Sample classification
- Tumor marker selection
- sPLSDA, Sparse Partial Least Squares Discriminant Analysis
- t-SNE, Student t Distribution
- χ2, Chi-square
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Affiliation(s)
- Jing Tang
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yang Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
| | - Yi Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ziyu Xiao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Yunqing Qiu
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Feng Zhu
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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Hattori LT, Gutoski M, Vargas Benítez CM, Nunes LF, Lopes HS. A benchmark of optimally folded protein structures using integer programming and the 3D-HP-SC model. Comput Biol Chem 2020; 84:107192. [PMID: 31918170 DOI: 10.1016/j.compbiolchem.2019.107192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Revised: 12/09/2019] [Accepted: 12/10/2019] [Indexed: 01/04/2023]
Abstract
The Protein Structure Prediction (PSP) problem comprises, among other issues, forecasting the three-dimensional native structure of proteins using only their primary structure information. Most computational studies in this area use synthetic data instead of real biological data. However, the closer to the real-world, the more the impact of results and their applicability. This work presents 17 real protein sequences extracted from the Protein Data Bank for a benchmark to the PSP problem using the tri-dimensional Hydrophobic-Polar with Side-Chains model (3D-HP-SC). The native structure of these proteins was found by maximizing the number of hydrophobic contacts between the side-chains of amino acids. The problem was treated as an optimization problem and solved by means of an Integer Programming approach. Although the method optimally solves the problem, the processing time has an exponential trend. Therefore, due to computational limitations, the method is a proof-of-concept and it is not applicable to large sequences. For unknown sequences, an upper bound of the number of hydrophobic contacts (using this model) can be found, due to a linear relationship with the number of hydrophobic residues. The comparison between the predicted and the biological structures showed that the highest similarity between them was found with distance thresholds around 5.2-8.2 Å. Both the dataset and the programs developed will be freely available to foster further research in the area.
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Affiliation(s)
- Leandro Takeshi Hattori
- Bioinformatics and Computational Intelligence Laboratory, Federal University of Technology Paraná (UTFPR), Av. 7 de Setembro, 3165, 80230-901 Curitiba (PR), Brazil.
| | - Matheus Gutoski
- Bioinformatics and Computational Intelligence Laboratory, Federal University of Technology Paraná (UTFPR), Av. 7 de Setembro, 3165, 80230-901 Curitiba (PR), Brazil
| | - César Manuel Vargas Benítez
- Bioinformatics and Computational Intelligence Laboratory, Federal University of Technology Paraná (UTFPR), Av. 7 de Setembro, 3165, 80230-901 Curitiba (PR), Brazil
| | - Luiz Fernando Nunes
- Bioinformatics and Computational Intelligence Laboratory, Federal University of Technology Paraná (UTFPR), Av. 7 de Setembro, 3165, 80230-901 Curitiba (PR), Brazil.
| | - Heitor Silvério Lopes
- Bioinformatics and Computational Intelligence Laboratory, Federal University of Technology Paraná (UTFPR), Av. 7 de Setembro, 3165, 80230-901 Curitiba (PR), Brazil.
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Wu H, Yang R, Fu Q, Chen J, Lu W, Li H. Research on predicting 2D-HP protein folding using reinforcement learning with full state space. BMC Bioinformatics 2019; 20:685. [PMID: 31874607 PMCID: PMC6929271 DOI: 10.1186/s12859-019-3259-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Protein structure prediction has always been an important issue in bioinformatics. Prediction of the two-dimensional structure of proteins based on the hydrophobic polarity model is a typical non-deterministic polynomial hard problem. Currently reported hydrophobic polarity model optimization methods, greedy method, brute-force method, and genetic algorithm usually cannot converge robustly to the lowest energy conformations. Reinforcement learning with the advantages of continuous Markov optimal decision-making and maximizing global cumulative return is especially suitable for solving global optimization problems of biological sequences. RESULTS In this study, we proposed a novel hydrophobic polarity model optimization method derived from reinforcement learning which structured the full state space, and designed an energy-based reward function and a rigid overlap detection rule. To validate the performance, sixteen sequences were selected from the classical data set. The results indicated that reinforcement learning with full states successfully converged to the lowest energy conformations against all sequences, while the reinforcement learning with partial states folded 50% sequences to the lowest energy conformations. Reinforcement learning with full states hits the lowest energy on an average 5 times, which is 40 and 100% higher than the three and zero hit by the greedy algorithm and reinforcement learning with partial states respectively in the last 100 episodes. CONCLUSIONS Our results indicate that reinforcement learning with full states is a powerful method for predicting two-dimensional hydrophobic-polarity protein structure. It has obvious competitive advantages compared with greedy algorithm and reinforcement learning with partial states.
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Affiliation(s)
- Hongjie Wu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China
| | - Ru Yang
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China
| | - Qiming Fu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China. .,Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, 215009, China.
| | - Jianping Chen
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.,Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, 215009, China
| | - Weizhong Lu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China
| | - Haiou Li
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China
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Optimization of protein folding using chemical reaction optimization in HP cubic lattice model. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04447-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Protein folding optimization using differential evolution extended with local search and component reinitialization. Inf Sci (N Y) 2018. [DOI: 10.1016/j.ins.2018.04.072] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Li B, Fooksa M, Heinze S, Meiler J. Finding the needle in the haystack: towards solving the protein-folding problem computationally. Crit Rev Biochem Mol Biol 2018; 53:1-28. [PMID: 28976219 PMCID: PMC6790072 DOI: 10.1080/10409238.2017.1380596] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 08/22/2017] [Accepted: 09/13/2017] [Indexed: 12/22/2022]
Abstract
Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem," has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions.
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Affiliation(s)
- Bian Li
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Michaela Fooksa
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Chemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, TN, USA
| | - Sten Heinze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
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Selection of appropriate metaheuristic algorithms for protein structure prediction in AB off-lattice model: a perspective from fitness landscape analysis. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2017.01.020] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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10
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Nama S, Saha AK, Ghosh S. Improved backtracking search algorithm for pseudo dynamic active earth pressure on retaining wall supporting c-Ф backfill. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2016.09.037] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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