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Computational Intelligence Methods in Bioinformatics
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Computational Intelligence Methods in Bioinformatics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Dynamical Systems".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 14670

Special Issue Editors

Prof. Dr. Zhiqiang Ma

E-Mail Website
Guest Editor
School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
Interests: bioinformatics; software engineering; molecular biology; machine learning; data mining
Dr. Yunhe Wang

E-Mail Website
Guest Editor
School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
Interests: intelligent computation; swarm intelligence; machine learning; evolutionary optimization; bioinformatics

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed the explosive growth of scientific data in bioinformatics and computational biology disciplines. However, traditional algorithms always suffer from multitudes of computational challenges, including the dimensionality curse, data noises, data scalability, and data processing. To address these issues, novel computational methods have to be developed. Therefore, in this Special Issue, we envision that computational intelligence methods will play an important role for addressing those computational challenges since the characteristics of intelligence and robustness. This Special Issue welcomes contributions that report innovative research and applications related to the theme of “Computational Intelligence Methods in Bioinformatics”. Articles with sound methodology and scientific practice are particularly welcomed. 

Prof. Dr. Zhiqiang Ma
Dr. Yunhe Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • evolutionary optimization
  • computational biology
  • bioinformatics
  • machine learning
  • deep learning
  • swarm intelligence

Published Papers (7 papers)

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Research

10 pages, 931 KiB  
Article
Physical Attention-Gated Spatial-Temporal Predictive Network for Weather Forecasting
by Xueliang Zhao, Qilong Sun and Xiaoguang Lin
Mathematics 2023, 11(6), 1330; https://doi.org/10.3390/math11061330 - 9 Mar 2023
Cited by 1 | Viewed by 1245
Abstract
Spatial-temporal sequence prediction is one of the hottest topics in the field of deep learning due to its wide range of potential applications in video-like data processing, specifically weather forecasting. Since most spatial-temporal observations evolve under physical laws, we adopt an attentional gating [...] Read more.
Spatial-temporal sequence prediction is one of the hottest topics in the field of deep learning due to its wide range of potential applications in video-like data processing, specifically weather forecasting. Since most spatial-temporal observations evolve under physical laws, we adopt an attentional gating scheme to leverage the dynamic patterns captured by tailored convolution structures and propose a novel neural network, PastNet, to achieve accurate predictions. By highlighting useful parts of the whole feature map, the gating units help increase the efficiency of the architecture. Extensive experiments conducted on synthetic and real-world datasets reveal that PastNet bears the ability to accomplish this task with better performance than baseline methods. Full article
(This article belongs to the Special Issue Computational Intelligence Methods in Bioinformatics)
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42 pages, 19875 KiB  
Article
Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems
by Jinhua You, Heming Jia, Di Wu, Honghua Rao, Changsheng Wen, Qingxin Liu and Laith Abualigah
Mathematics 2023, 11(5), 1256; https://doi.org/10.3390/math11051256 - 5 Mar 2023
Cited by 3 | Viewed by 2633
Abstract
The artificial Gorilla Troop Optimization (GTO) algorithm (GTO) is a metaheuristic optimization algorithm that simulates the social life of gorillas. This paper proposes three innovative strategies considering the GTO algorithm’s insufficient convergence accuracy and low convergence speed. First, a shrinkage control factor fusion [...] Read more.
The artificial Gorilla Troop Optimization (GTO) algorithm (GTO) is a metaheuristic optimization algorithm that simulates the social life of gorillas. This paper proposes three innovative strategies considering the GTO algorithm’s insufficient convergence accuracy and low convergence speed. First, a shrinkage control factor fusion strategy is proposed to expand the search space and reduce search blindness by strengthening the communication between silverback gorillas and other gorillas to improve global optimization performance. Second, a sine cosine interaction fusion strategy based on closeness is proposed to stabilize the performance of silverback gorillas and other gorilla individuals and improve the convergence ability and speed of the algorithm. Finally, a gorilla individual difference identification strategy is proposed to reduce the difference between gorilla and silverback gorillas to improve the quality of the optimal solution. In order to verify the optimization effect of the modified artificial gorilla troop optimization (MGTO) algorithm, we used 23 classic benchmark functions, 30 CEC2014 benchmark functions, and 10 CEC2020 benchmark functions to test the performance of the proposed MGTO algorithm. In this study, we used a total of 63 functions for algorithm comparison. At the same time, we carried out the exploitation and exploration balance experiment of 30 CEC2014 and 10 CEC2020 functions for the MGTO algorithm. In addition, the MGTO algorithm was also applied to test seven practical engineering problems, and it achieved good results. Full article
(This article belongs to the Special Issue Computational Intelligence Methods in Bioinformatics)
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41 pages, 9560 KiB  
Article
Modified Sand Cat Swarm Optimization Algorithm for Solving Constrained Engineering Optimization Problems
by Di Wu, Honghua Rao, Changsheng Wen, Heming Jia, Qingxin Liu and Laith Abualigah
Mathematics 2022, 10(22), 4350; https://doi.org/10.3390/math10224350 - 19 Nov 2022
Cited by 37 | Viewed by 3691
Abstract
The sand cat swarm optimization algorithm (SCSO) is a recently proposed metaheuristic optimization algorithm. It stimulates the hunting behavior of the sand cat, which attacks or searches for prey according to the sound frequency; each sand cat aims to catch better prey. Therefore, [...] Read more.
The sand cat swarm optimization algorithm (SCSO) is a recently proposed metaheuristic optimization algorithm. It stimulates the hunting behavior of the sand cat, which attacks or searches for prey according to the sound frequency; each sand cat aims to catch better prey. Therefore, the sand cat will search for a better location to catch better prey. In the SCSO algorithm, each sand cat will gradually approach its prey, which makes the algorithm a strong exploitation ability. However, in the later stage of the SCSO algorithm, each sand cat is prone to fall into the local optimum, making it unable to find a better position. In order to improve the mobility of the sand cat and the exploration ability of the algorithm. In this paper, a modified sand cat swarm optimization (MSCSO) algorithm is proposed. The MSCSO algorithm adds a wandering strategy. When attacking or searching for prey, the sand cat will walk to find a better position. The MSCSO algorithm with a wandering strategy enhances the mobility of the sand cat and makes the algorithm have stronger global exploration ability. After that, the lens opposition-based learning strategy is added to enhance the global property of the algorithm so that the algorithm can converge faster. To evaluate the optimization effect of the MSCSO algorithm, we used 23 standard benchmark functions and CEC2014 benchmark functions to evaluate the optimization performance of the MSCSO algorithm. In the experiment, we analyzed the data statistics, convergence curve, Wilcoxon rank sum test, and box graph. Experiments show that the MSCSO algorithm with a walking strategy and a lens position-based learning strategy had a stronger exploration ability. Finally, the MSCSO algorithm was used to test seven engineering problems, which also verified the engineering practicability of the proposed algorithm. Full article
(This article belongs to the Special Issue Computational Intelligence Methods in Bioinformatics)
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15 pages, 2281 KiB  
Article
Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec
by Chun-Ming Xu, Jia-Shuai Zhang, Ling-Qiang Kong, Xue-Bo Jin, Jian-Lei Kong, Yu-Ting Bai, Ting-Li Su, Hui-Jun Ma and Prasun Chakrabarti
Mathematics 2022, 10(22), 4283; https://doi.org/10.3390/math10224283 - 16 Nov 2022
Cited by 2 | Viewed by 1472
Abstract
Effective prediction of wastewater treatment is beneficial for precise control of wastewater treatment processes. The nonlinearity of pollutant indicators such as chemical oxygen demand (COD) and total phosphorus (TP) makes the model difficult to fit and has low prediction accuracy. The classical deep [...] Read more.
Effective prediction of wastewater treatment is beneficial for precise control of wastewater treatment processes. The nonlinearity of pollutant indicators such as chemical oxygen demand (COD) and total phosphorus (TP) makes the model difficult to fit and has low prediction accuracy. The classical deep learning methods have been shown to perform nonlinear modeling. However, there are enormous numerical differences between multi-dimensional data in the prediction problem of wastewater treatment, such as COD above 3000 mg/L and TP around 30 mg/L. It will make current normalization methods challenging to handle effectively, leading to the training failing to converge and the gradient disappearing or exploding. This paper proposes a multi-factor prediction model based on deep learning. The model consists of a combined normalization layer and a codec. The combined normalization layer combines the advantages of three normalization calculation methods: z-score, Interval, and Max, which can realize the adaptive processing of multi-factor data, fully retain the characteristics of the data, and finally cooperate with the codec to learn the data characteristics and output the prediction results. Experiments show that the proposed model can overcome data differences and complex nonlinearity in predicting industrial wastewater pollutant indicators and achieve better prediction accuracy than classical models. Full article
(This article belongs to the Special Issue Computational Intelligence Methods in Bioinformatics)
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18 pages, 1710 KiB  
Article
Machine Learning Combined with Restriction Enzyme Mining Assists in the Design of Multi-Point Mutagenic Primers
by Yu-Huei Cheng, Li-Yeh Chuang and Cheng-Hong Yang
Mathematics 2022, 10(21), 4105; https://doi.org/10.3390/math10214105 - 3 Nov 2022
Viewed by 1296
Abstract
The polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) experiment has the characteristics of low-cost, rapidity, simplicity, convenience, high sensitivity and high specificity; thus, many small and medium laboratories use it to perform all kinds of single nucleotide polymorphisms (SNPs) genotyping works, and as [...] Read more.
The polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) experiment has the characteristics of low-cost, rapidity, simplicity, convenience, high sensitivity and high specificity; thus, many small and medium laboratories use it to perform all kinds of single nucleotide polymorphisms (SNPs) genotyping works, and as a molecular biotechnology for disease-related analysis. However, many single nucleotide polymorphisms lack available restriction enzymes to distinguish the specific genotypes on a target SNP, and that causes the PCR-RFLP assay which is unavailable to be called mismatch PCR-RFLP. In order to completely solve the problem of mismatch PCR-RFLP, we have created a teaching–learning-based optimization (TLBO) multi-point mutagenic primer design algorithm which, combined with REHUNT, provides a complete and specific restriction enzyme mining solution. The proposed method not only introduces several search strategies suitable for multi-point mutagenesis primers, but also enhances the reliability of mutagenic primer design. In addition, this study is also designed for more complex SNP structures (with multiple dNTPs and insertion and deletion) to provide specific solutions for SNP diversity. We tested against fifteen mismatch PCR-RFLP SNPs in the human SLC6A4 gene on the NCBI dbSNP database as experimental templates. The experimental results indicate that the proposed method is helpful for providing the multi-point mutagenic primers that meet the constrain conditions of PCR experiments. Full article
(This article belongs to the Special Issue Computational Intelligence Methods in Bioinformatics)
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36 pages, 10935 KiB  
Article
Modified Remora Optimization Algorithm with Multistrategies for Global Optimization Problem
by Changsheng Wen, Heming Jia, Di Wu, Honghua Rao, Shanglong Li, Qingxin Liu and Laith Abualigah
Mathematics 2022, 10(19), 3604; https://doi.org/10.3390/math10193604 - 2 Oct 2022
Cited by 23 | Viewed by 2072
Abstract
Remora Optimization Algorithm (ROA) is a metaheuristic optimization algorithm, proposed in 2021, which simulates the parasitic attachment, experiential attack, and host feeding behavior of remora in the ocean. However, the performance of ROA is not very good. Considering the habits of the remora [...] Read more.
Remora Optimization Algorithm (ROA) is a metaheuristic optimization algorithm, proposed in 2021, which simulates the parasitic attachment, experiential attack, and host feeding behavior of remora in the ocean. However, the performance of ROA is not very good. Considering the habits of the remora that rely on the host to find food, and in order to improve the performance of the ROA, we designed a new host-switching mechanism. By adding new a host-switching mechanism, joint opposite selection, and restart strategy, a modified remora optimization algorithm (MROA) is proposed. We use 23 standard benchmark and CEC2020 functions to test the performance of MROA and compare them with eight state-of-art optimization algorithms. The experimental results show that MROA has better-optimized performance and robustness. Finally, the ability of MROA to solve practical problems is demonstrated by five classical engineering problems. Full article
(This article belongs to the Special Issue Computational Intelligence Methods in Bioinformatics)
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14 pages, 495 KiB  
Article
Neural Metric Factorization for Recommendation
by Xiaoxin Sun, Liqiu Gong, Zhichao Han, Peng Zhao, Junchao Yu and Suhua Wang
Mathematics 2022, 10(3), 503; https://doi.org/10.3390/math10030503 - 4 Feb 2022
Cited by 1 | Viewed by 1358
Abstract
All current recommendation algorithms, when modeling user–item interactions, basically use dot product. This dot product calculation is derived from matrix factorization. We argue that an inherent drawback of matrix factorization is that latent semantic vectors of users or items sometimes do not satisfy [...] Read more.
All current recommendation algorithms, when modeling user–item interactions, basically use dot product. This dot product calculation is derived from matrix factorization. We argue that an inherent drawback of matrix factorization is that latent semantic vectors of users or items sometimes do not satisfy triangular inequalities, which may affect the performance of the recommendation. Recently, metric factorization was proposed to replace matrix factorization and has achieved some improvements in terms of recommendation accuracy. However, similar to matrix factorization, metric factorization still uses a simple, linear fashion. In this paper, we explore leveraging nonlinear deep neural networks to realize Euclidean distance interaction between users and items. We propose a generic Neural Metric Factorization Framework (NMetricF), which learns representations for users and items by incorporating Euclidean metric factorization into deep neural networks. Extensive experiments on six real-world datasets show that, compared to the previous recommendation algorithms based purely on rating data, NMetricF achieves the best performance. Full article
(This article belongs to the Special Issue Computational Intelligence Methods in Bioinformatics)
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