Incorporating a multiobjective knowledge-based energy function into differential evolution for protein structure prediction

Chen, Xingqian; Song, Shuangbao; Ji, Junkai; Tang, Zheng; Todo, Yuki

doi:10.1016/j.ins.2020.06.003

Cited by 20 publications

(15 citation statements)

References 43 publications

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“…Due to the effectiveness of DE for solving single-objective optimization problems, extending DE to solve MOOPs has attracted the interest of researchers in the literature [ 34 ]. Two important issues in extending DE into MODE need to be overcome.…”

Section: Methodsmentioning

confidence: 99%

“…DE is a simple but powerful stochastic optimization algorithm that was first proposed by Storn and Price in the 1990s [32]. Recent research has increased the efficiency for solving many realworld problems [33][34][35]. e characteristic of DE is using the difference between two candidate solutions to generate a new candidate solution.…”

Section: Standard Differential Evolutionmentioning

confidence: 99%

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A Two‐Stage Method Based on Multiobjective Differential Evolution for Gene Selection

Song

Chen

Tang

et al. 2021

Computational Intelligence and Neuroscience

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Standard Differential Evolutionmentioning

confidence: 99%

A Two‐Stage Method Based on Multiobjective Differential Evolution for Gene Selection

Song

Chen

Tang

et al. 2021

Computational Intelligence and Neuroscience

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“…It includes three different physical energy terms: bond energy, non-bond energy, and solvent accessible surface area. MODE-K [9] presents a multi-objective differential evolution algorithm and maintains an archive of optimal solutions. MODE-K uses RWplus [15] as the energy function and decomposes the energy function into two terms to get multiple objectives: a distance-dependent energy term and an orientation-dependent term.…”

Section: Related Workmentioning

confidence: 99%

“…scoring functions based on geometric constraints learnt by deep learning models [2]-VOLUME 4, 2016 [4]. PSP search methods include Monte Carlo algorithms [5], evolutionary algorithms [6], multi-objective optimisation [7], sequential search [8], differential evolution [9], memetic algorithms [10], [11], and gradient descent algorithms [2], [4], [12]. In general, these iterative search algorithms generate neighbour conformations i.e.…”

Section: Introductionmentioning

confidence: 99%

Constraint Guided Neighbor Generation for Protein Structure Prediction

et al. 2022

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Protein structure prediction (PSP) is essential for drug discovery. PSP involves minimising an unknown scoring function over an astronomical search space. PSP has achieved significant progress recently via end-to-end deep learning models that require enormous computational resources and almost all known proteins as training data. In this paper, we develop conformational search methods for PSP based on scoring functions involving geometric constraints learnt by deep learning models. When machine learning models achieve generality and thus obviously loose accuracy, conformational search methods could perform protein-specific fine tuning of the predicted conformations. However, effective conformational sampling in PSP remains a key challenge. Existing conformational search algorithms adopt random selection approaches for neighbor generation and thus greatly depend on luck. We propose a new approach to analyse geometric constraint-based scores, to identify the regions of the current conformations causing inferior scores, and to alter the identified regions to generate neighbour conformations. Our approach prefers informed decisions to random selections from an artificial intelligence perspective. The proposed method also provides promising search guidance as it obtains significant improvements from given initial conformations. Our approach significantly outperforms state-of-the-art PSP search algorithms that use random sampling with a similar scoring function on a set of benchmark proteins of varying types and sizes. Our sample generation approach could be used in other bioinformatics research areas requiring search.

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“…In addition, the slow convergence speed and the capability of being easily trapped in local minima are the main disadvantages of BP methods [24]. In contrast, the utility of a heuristic optimization method to solve real-world problems [47,48] has aroused the interest of researchers in recent years, including training ANNs [28,34]. The heuristic optimization algorithm called the competitive swarm optimizer (CSO) is also employed to train the LDNM in this study.…”

Section: Backpropagation Algorithmmentioning

confidence: 99%

A Neuron Model with Dendrite Morphology for Classification

et al. 2021

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Recent neurological studies have shown the importance of dendrites in neural computation. In this paper, a neuron model with dendrite morphology, called the logic dendritic neuron model (LDNM), is proposed for classification. This model consists of four layers: a synaptic layer, a dendritic layer, a membrane layer, and a soma body. After training, the LDNM is simplified by proprietary pruning mechanisms and is further transformed into a logic circuit classifier. Moreover, to address the high-dimensional challenge, feature selection is employed as the dimension reduction method before training the LDNM. In addition, the effort of employing a heuristic optimization algorithm as the learning method is also undertaken to speed up the convergence. Finally, the performance of the proposed model is assessed by five benchmark high-dimensional classification problems. In comparison with the other six classical classifiers, LDNM achieves the best classification performance on two (out of five) classification problems. The experimental results demonstrate the effectiveness of the proposed model. A new perspective for solving classification problems by the proposed LDNM is provided in the paper.

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Incorporating a multiobjective knowledge-based energy function into differential evolution for protein structure prediction

Cited by 20 publications

References 43 publications

A Two‐Stage Method Based on Multiobjective Differential Evolution for Gene Selection

A Two‐Stage Method Based on Multiobjective Differential Evolution for Gene Selection

Constraint Guided Neighbor Generation for Protein Structure Prediction

A Neuron Model with Dendrite Morphology for Classification

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