1
|
Multi-view multi-objective clustering-based framework for scientific document summarization using citation context. APPL INTELL 2023. [DOI: 10.1007/s10489-022-04166-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
|
2
|
Mufassirin MMM, Newton MAH, Sattar A. Artificial intelligence for template-free protein structure prediction: a comprehensive review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10350-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|
3
|
Newton MH, Zaman R, Mataeimoghadam F, Rahman J, Sattar A. Constraint Guided Beta-Sheet Refinement for Protein Structure Prediction. Comput Biol Chem 2022; 101:107773. [DOI: 10.1016/j.compbiolchem.2022.107773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022]
|
4
|
Abstract
AbstractWind driven optimization (WDO) is a meta-heuristic algorithm based on swarm intelligence. The original selection method makes it easy to converge prematurely and trap in local optima. Maintaining population diversity can solve this problem well. Therefore, we introduce a new fitness-distance balance-based selection strategy to replace the original selection method, and add chaotic local search with selecting chaotic map based on memory to further improve the search performance of the algorithm. A chaotic wind driven optimization with fitness-distance balance strategy is proposed, called CFDBWDO. In the experimental section, we find the optimal parameter settings for the proposed algorithm. To verify the effect of the algorithm, we conduct comparative experiments on the CEC 2017 benchmark functions. The experimental results denote that the proposed algorithm has superior performance. Compared with WDO, CFDBWDO can gradually converge in function optimization. We further verify the practicality of the proposed algorithm with six real-world optimization problems, and the obtained results are all better than other algorithms.
Collapse
|
5
|
Hu G, Zhu X, Wang X, Wei G. Multi-strategy boosted marine predators algorithm for optimizing approximate developable surface. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109615] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
|
6
|
|
7
|
A Two-Stage Method Based on Multiobjective Differential Evolution for Gene Selection. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:5227377. [PMID: 34966420 PMCID: PMC8712129 DOI: 10.1155/2021/5227377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 08/06/2021] [Accepted: 12/03/2021] [Indexed: 11/17/2022]
Abstract
Microarray gene expression data provide a prospective way to diagnose disease and classify cancer. However, in bioinformatics, the gene selection problem, i.e., how to select the most informative genes from thousands of genes, remains challenging. This problem is a specific feature selection problem with high-dimensional features and small sample sizes. In this paper, a two-stage method combining a filter feature selection method and a wrapper feature selection method is proposed to solve the gene selection problem. In contrast to common methods, the proposed method models the gene selection problem as a multiobjective optimization problem. Both stages employ the same multiobjective differential evolution (MODE) as the search strategy but incorporate different objective functions. The three objective functions of the filter method are mainly based on mutual information. The two objective functions of the wrapper method are the number of selected features and the classification error of a naive Bayes (NB) classifier. Finally, the performance of the proposed method is tested and analyzed on six benchmark gene expression datasets. The experimental results verified that this paper provides a novel and effective way to solve the gene selection problem by applying a multiobjective optimization algorithm.
Collapse
|
8
|
Song S, Chen X, Zhang Y, Tang Z, Todo Y. Protein–ligand docking using differential evolution with an adaptive mechanism. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107433] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
|
9
|
Guo L, Jiang Q, Jin X, Liu L, Zhou W, Yao S, Wu M, Wang Y. A Deep Convolutional Neural Network to Improve the Prediction of Protein Secondary Structure. Curr Bioinform 2020. [DOI: 10.2174/1574893615666200120103050] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Protein secondary structure prediction (PSSP) is a fundamental task in
bioinformatics that is helpful for understanding the three-dimensional structure and biological
function of proteins. Many neural network-based prediction methods have been developed for
protein secondary structures. Deep learning and multiple features are two obvious means to improve
prediction accuracy.
Objective:
To promote the development of PSSP, a deep convolutional neural network-based
method is proposed to predict both the eight-state and three-state of protein secondary structure.
Methods:
In this model, sequence and evolutionary information of proteins are combined as multiple
input features after preprocessing. A deep convolutional neural network with no pooling layer and
connection layer is then constructed to predict the secondary structure of proteins. L2 regularization,
batch normalization, and dropout techniques are employed to avoid over-fitting and obtain better
prediction performance, and an improved cross-entropy is used as the loss function.
Results:
Our proposed model can obtain Q3 prediction results of 86.2%, 84.5%, 87.8%, and 84.7%,
respectively, on CullPDB, CB513, CASP10 and CASP11 datasets, with corresponding Q8
prediction results of 74.1%, 70.5%, 74.9%, and 71.3%.
Conclusion:
We have proposed the DCNN-SS deep convolutional-network-based PSSP method,
and experimental results show that DCNN-SS performs competitively with other methods.
Collapse
Affiliation(s)
- Lin Guo
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Qian Jiang
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Xin Jin
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Lin Liu
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Wei Zhou
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Shaowen Yao
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Min Wu
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Yun Wang
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| |
Collapse
|
10
|
Chen X, Song S, Ji J, Tang Z, Todo Y. Incorporating a multiobjective knowledge-based energy function into differential evolution for protein structure prediction. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.06.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
|
11
|
A Dendritic Neuron Model with Adaptive Synapses Trained by Differential Evolution Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:2710561. [PMID: 32405292 PMCID: PMC7201754 DOI: 10.1155/2020/2710561] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 11/12/2019] [Accepted: 12/30/2019] [Indexed: 11/24/2022]
Abstract
A dendritic neuron model with adaptive synapses (DMASs) based on differential evolution (DE) algorithm training is proposed. According to the signal transmission order, a DNM can be divided into four parts: the synaptic layer, dendritic layer, membrane layer, and somatic cell layer. It can be converted to a logic circuit that is easily implemented on hardware by removing useless synapses and dendrites after training. This logic circuit can be designed to solve complex nonlinear problems using only four basic logical devices: comparators, AND (conjunction), OR (disjunction), and NOT (negation). To obtain a faster and better solution, we adopt the most popular DE for DMAS training. We have chosen five classification datasets from the UCI Machine Learning Repository for an experiment. We analyze and discuss the experimental results in terms of the correct rate, convergence rate, ROC curve, and the cross-validation and then compare the results with a dendritic neuron model trained by the backpropagation algorithm (BP-DNM) and a neural network trained by the backpropagation algorithm (BPNN). The analysis results show that the DE-DMAS shows better performance in all aspects.
Collapse
|
12
|
Zhang GJ, Ma LF, Wang XQ, Zhou XG. Secondary Structure and Contact Guided Differential Evolution for Protein Structure Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1068-1081. [PMID: 30295627 DOI: 10.1109/tcbb.2018.2873691] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Ab initio protein tertiary structure prediction is one of the long-standing problems in structural bioinformatics. With the help of residue-residue contact and secondary structure prediction information, the accuracy of ab initio structure prediction can be enhanced. In this study, an improved differential evolution with secondary structure and residue-residue contact information referred to as SCDE is proposed for protein structure prediction. In SCDE, two score models based on secondary structure and contact information are proposed, and two selection strategies, namely, secondary structure-based selection strategy and contact-based selection strategy, are designed to guide conformation space search. A probability distribution function is designed to balance these two selection strategies. Experimental results on a benchmark dataset with 28 proteins and four free model targets in CASP12 demonstrate that the proposed SCDE is effective and efficient.
Collapse
|
13
|
Emami N, Pakchin PS, Ferdousi R. Computational predictive approaches for interaction and structure of aptamers. J Theor Biol 2020; 497:110268. [PMID: 32311376 DOI: 10.1016/j.jtbi.2020.110268] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/27/2020] [Accepted: 04/02/2020] [Indexed: 02/07/2023]
Abstract
Aptamers are short single-strand sequences that can bind to their specific targets with high affinity and specificity. Usually, aptamers are selected experimentally via systematic evolution of ligands by exponential enrichment (SELEX), an evolutionary process that consists of multiple cycles of selection and amplification. The SELEX process is expensive, time-consuming, and its success rates are relatively low. To overcome these difficulties, in recent years, several computational techniques have been developed in aptamer sciences that bring together different disciplines and branches of technologies. In this paper, a complementary review on computational predictive approaches of the aptamer has been organized. Generally, the computational prediction approaches of aptamer have been proposed to carry out in two main categories: interaction-based prediction and structure-based predictions. Furthermore, the available software packages and toolkits in this scope were reviewed. The aim of describing computational methods and tools in aptamer science is that aptamer scientists might take advantage of these computational techniques to develop more accurate and more sensitive aptamers.
Collapse
Affiliation(s)
- Neda Emami
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Parvin Samadi Pakchin
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran; Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.
| |
Collapse
|
14
|
Silva FT, Silva MX, Belchior JC. A New Genetic Algorithm Approach Applied to Atomic and Molecular Cluster Studies. Front Chem 2019; 7:707. [PMID: 31750290 PMCID: PMC6848380 DOI: 10.3389/fchem.2019.00707] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 10/09/2019] [Indexed: 12/16/2022] Open
Abstract
A new procedure is suggested to improve genetic algorithms for the prediction of structures of nanoparticles. The strategy focuses on managing the creation of new individuals by evaluating the efficiency of operators (o1, o2,…,o13) in generating well-adapted offspring. This is done by increasing the creation rate of operators with better performance and decreasing that rate for the ones which poorly fulfill the task of creating favorable new generation. Additionally, several strategies (thirteen at this level of approach) from different optimization techniques were implemented on the actual genetic algorithm. Trials were performed on the general case studies of 26 and 55-atom clusters with binding energy governed by a Lennard-Jones empirical potential with all individuals being created by each of the particular thirteen operators tested. A 18-atom carbon cluster and some polynitrogen systems were also studied within REBO potential and quantum approaches, respectively. Results show that our management strategy could avoid bad operators, keeping the overall method performance with great confidence. Moreover, amongst the operators taken from the literature and tested herein, the genetic algorithm was faster when the generation of new individuals was carried out by the twist operator, even when compared to commonly used operators such as Deaven and Ho cut-and-splice crossover. Operators typically designed for basin-hopping methodology also performed well on the proposed genetic algorithm scheme.
Collapse
Affiliation(s)
- Frederico T Silva
- Departamento de Química Fundamental-CCEN, Universidade Federal de Pernambuco, Cidade Universitária, Recife, Brazil
| | - Mateus X Silva
- Programa de Pós-Graduação em Modelagem Matemática e Computacional, Centro Federal de Educação Tecnológica de Minas Gerais (CEFET-MG), Belo Horizonte, Brazil
| | - Jadson C Belchior
- Departamento de Química-ICEx, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| |
Collapse
|
15
|
Long S, Tian P. A simple neural network implementation of generalized solvation free energy for assessment of protein structural models. RSC Adv 2019; 9:36227-36233. [PMID: 35540566 PMCID: PMC9074945 DOI: 10.1039/c9ra05168f] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 10/14/2019] [Indexed: 11/21/2022] Open
Abstract
Rapid and accurate assessment of protein structural models is essential for protein structure prediction and design. Great progress has been made in this regard, especially by recent application of "knowledge-based" potentials. Various machine learning based protein structural model quality assessment methods are also quite successful. However, performance of traditional "physics-based" models has not been as effective. Based on our analysis of the fundamental computational limitation behind unsatisfactory performance of "physics-based" models, we propose a generalized solvation free energy (GSFE) framework, which is intrinsically flexible for multi-scale treatments and is amenable for machine learning implementation. Finally, we implemented a simple example of backbone-based residue level GSFE with neural network, which was found to have competitive performance when compared with highly complex latest "knowledge-based" atomic potentials in distinguishing native structures from decoys.
Collapse
Affiliation(s)
- Shiyang Long
- School of Chemistry, Jilin University Changchun China
| | - Pu Tian
- School of Life Science and School of Artificial Intelligence, Jilin University 2699 Qianjin Street Changchun China 130012
| |
Collapse
|
16
|
Li ZW, Sun K, Hao XH, Hu J, Ma LF, Zhou XG, Zhang GJ. Loop Enhanced Conformational Resampling Method for Protein Structure Prediction. IEEE Trans Nanobioscience 2019; 18:567-577. [PMID: 31180866 DOI: 10.1109/tnb.2019.2922101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Protein structure prediction has been a long-standing problem for the past decades. In particular, the loop region structure remains an obstacle in forming an accurate protein tertiary structure because of its flexibility. In this study, Rama torsion angle and secondary structure feature-guided differential evolution named RSDE is proposed to predict three-dimensional structure with the exploitation on the loop region structure. In RSDE, the structure of the loop region is improved by the following: loop-based cross operator, which interchanges configuration of a randomly selected loop region between individuals, and loop-based mutate operator, which considers torsion angle feature into conformational sampling. A stochastic ranking selective strategy is designed to select conformations with low energy and near-native structure. Moreover, the conformational resampling method, which uses previously learned knowledge to guide subsequent sampling, is proposed to improve the sampling efficiency. Experiments on a total of 28 test proteins reveals that the proposed RSDE is effective and can obtain native-like models.
Collapse
|
17
|
Song S, Ji J, Chen X, Gao S, Tang Z, Todo Y. Adoption of an improved PSO to explore a compound multi-objective energy function in protein structure prediction. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.07.042] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
|