Article
Computer Science, Information Systems
Zhang Sun, Luis Valencia -Cabrera, Guimin Ning, Xiaoxiao Song
Summary: Spiking neural P systems are an abstraction of the structure and function of nervous systems and neurons. SNP-WOD systems, a new class of these systems, remove the mechanism of duplication and allow for the amplification of pulses during the firing of spiking rules. These systems have computational properties and can generate numbers.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Xiaoxiao Song, Luis Valencia-Cabrera, Hong Peng, Jun Wang
Summary: This paper introduces a new neural computing model - spiking neural P systems with autapses (SNP-AU systems) and demonstrates their ability to generate Turing-computable numbers. By building an SNP-AU system with 53 neurons and providing a universal machine, the universality of its computing function is shown.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Suxia Jiang, Yijun Liu, Bowen Xu, Junwei Sun, Yanfeng Wang
Summary: In this study, asynchronous numerical spiking neural (ANSN) P systems are investigated by combining set theory and threshold control strategy. It is proved that ANSN P systems are Turing universal and capable of processing information.
INFORMATION SCIENCES
(2022)
Article
Chemistry, Multidisciplinary
Yongshun Shen, Yuzhen Zhao
Summary: This paper proposes a new type of spiking neural P system (SNP) called RDGRSNP systems. In RDGRSNP systems, the rules in neurons can change based on the substances within them. The Turing universality of RDGRSNP systems is demonstrated with both a number-generating device and a number-accepting device. A small universal RDGRSNP system for function computation using 68 neurons is provided, which requires fewer neurons compared to other SNP system variants.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Yuping Liu, Yuzhen Zhao
Summary: This work introduces the phenomenon of lateral inhibition in biological nervous systems into spiking neural P systems and proposes SN P systems with lateral inhibition (LISN P systems). By adjusting the lateral distance, lateral inhibition can affect the information transmission between neurons, and the designed LISN P systems can be used to generate arbitrary numbers and perform function computation, demonstrating computational completeness.
Article
Computer Science, Artificial Intelligence
Xiaoxiao Song, Luis Valencia-Cabrera, Hong Peng, Jun Wang, Mario J. Perez-Jimenez
Summary: This paper introduces SNP-DS systems as a powerful computing model based on the characteristics and communication of neurons, with the feature of neurons receiving spikes at different instants through delaying synapses. Additionally, two small universal SNP-DS systems are constructed to compute functions, with a simulator provided for experimental validation.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Zeqiong Lv, Tingting Bao, Nan Zhou, Hong Peng, Xiangnian Huang, Agustin Riscos-Nunez, Mario J. Perez-Jimenez
Summary: The paper introduces a new variant of spiking neural P systems, called SNP-ECR systems, which are distributed parallel computing models with a stronger firing control mechanism. The new spiking rule ECR allows neurons to send different numbers of spikes, providing greater flexibility in computation. It is proven that SNP-ECR systems are Turing universal as number generating/accepting devices and can function as universal function computing devices.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Computer Science, Information Systems
Yuzhen Zhao, Yongshun Shen, Xuefu Liu, Yueguo Luo, Wenke Zang, Xiyu Liu
Summary: Spiking neural P systems (SNP systems) are enhanced with long-term potentiation/depression mechanisms from biological neural systems, allowing them to dynamically adjust synaptic weights based on environmental changes. This improves their learning ability and intelligence. A novel type of SNP system, called SNP systems with long-term potentiation and depression (LTPD-SNP systems), is developed. The weights of synapses in LTPD-SNP systems change dynamically based on the spikes' intensity, timing, and their own properties. The universality of LTPD-SNP systems is proven and their computational efficiency is studied in solving the SAT problem. Additionally, an anomaly detection method for AC engines based on LTPD-SNP systems is constructed to demonstrate their learning capabilities. This work enhances the learning ability and intelligence of SNP systems and provides insights for creating brain-like intelligent computational models.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Jianping Dong, Gexiang Zhang, Biao Luo, Qiang Yang, Dequan Guo, Haina Rong, Ming Zhu, Kang Zhou
Summary: This paper proposes a distributed adaptive optimization spiking neural P system (DAOSNPS) that can solve combinatorial optimization problems without the help of evolutionary algorithms or swarm intelligence algorithms. Extensive experiments demonstrate its superiority over other methods.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Liping Wang, Xiyu Liu, Minghe Sun, Yuzhen Zhao
Summary: In this paper, a new variant of evolution-communication spiking neural P systems, called ECSNP-ER systems, is proposed and developed. ECSNP-ER systems incorporate energy request rules in addition to spike-evolution rules and spike-communication rules. The definition, structure, and operations of ECSNP-ER systems are presented in detail. It is shown that ECSNP-ER systems have the same computational capabilities as Turing machines and can solve NP-complete problems, such as the SAT problem, in linear time.
Article
Computer Science, Information Systems
Jianping Dong, Gexiang Zhang, Biao Luo, Haina Rong
Summary: An extended numerical spiking neural (ENSN P) system is proposed to solve continuous constrained optimization problems. In ENSN P systems, production functions are selected by probability to achieve updated parameters. Experimental results show that the proposed method outperforms or is competitive with other 28 optimization algorithms in five benchmarks.
INFORMATION SCIENCES
(2023)
Article
Engineering, Chemical
Xiyu Liu, Qianqian Ren
Summary: The spiking neural membrane computing models (SNMC models) proposed in this paper combine neural network structure and data processing methods to improve the shortcomings of current spiking neural P systems (SNP) in numerical calculations. In SNMC models, the state of each neuron is represented by a real number, and there are new rules for neurons with time delay. The Turing universality of the SNMC model as a number generator and acceptor is also demonstrated.
Article
Computer Science, Artificial Intelligence
Tingfang Wu, Qiang Lyu, Linqiang Pan
Summary: This study explores spiking neural P systems (SNP systems) and their variant evolution-communication SNP (ECSNP) systems, demonstrating the Turing universality of ECSNP systems as number-generating devices and highlighting the critical impact of the quantity of spikes in neurons on the computational power of ECSNP systems.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Engineering, Chemical
Xiu Yin, Xiyu Liu, Minghe Sun, Qianqian Ren
Summary: NSNVC P systems are a novel type of neural P systems with a variable consumption strategy, introducing delay features and variable consumption strategy for number generating and function computing. They possess distributed parallel computing models and Turing universality.
Review
Chemistry, Multidisciplinary
Yicen Liu, Ying Chen, Prithwineel Paul, Songhai Fan, Xiaomin Ma, Gexiang Zhang
Summary: This paper discusses the application of spiking neural P systems in fault diagnosis in power systems, and explores their efficiency in different power equipment systems as well as future research directions.
APPLIED SCIENCES-BASEL
(2021)
Article
Biochemical Research Methods
Xue Li, Peifu Han, Wenqi Chen, Changnan Gao, Shuang Wang, Tao Song, Muyuan Niu, Alfonso Rodriguez-Paton
Summary: This study proposes a protein-protein interaction (PPI) prediction model called multi-scale architecture residual network for PPIs (MARPPI) that utilizes dual-channel and multi-feature methods. The model leverages Res2vec to obtain residue association information and uses various descriptors to capture amino acid composition and order information, physicochemical properties, and information entropy. MARPPI achieves high accuracy rates ranging from 91.80% to 99.01% on different datasets, outperforming other advanced methods. The results also demonstrate the model's ability to detect hidden interactions and predict cross-species interactions.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biology
Xun Wang, Lulu Wang, Shuang Wang, Yongqi Ren, Wenqi Chen, Xue Li, Peifu Han, Tao Song
Summary: Molecular toxicity prediction is crucial for drug discovery and human health. Existing machine learning models for toxicity prediction do not fully utilize the 3D information of molecules, which can influence their toxicity. In this study, we propose QuantumTox, the first application of quantum chemistry in drug molecule toxicity prediction. Our model extracts quantum chemical information as 3D features and uses ensemble learning methods to improve accuracy and generalization. Experimental results demonstrate consistent outperformance compared to baseline models, even on small datasets.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Environmental Sciences
Fan Meng, Yichen Yao, Zhibin Wang, Shiqiu Peng, Danya Xu, Tao Song
Summary: This study proposes a machine learning approach for probabilistic forecasting of tropical cyclone intensity. Previous studies cannot directly characterize the uncertainty in TC forecasting and suffer from computational effort issues. This study introduces a new method of evaluating the forecast without this uncertainty through the forecast distribution. The model outperforms current operational models and provides reliable probabilistic forecasts critical for disaster warnings.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Article
Biochemical Research Methods
Yanyan Li, Bosheng Song, Xiangxiang Zeng
Summary: The article introduces neural-like P systems with plasmids (NP P systems) which are inspired by bacteria's DNA processing. It also presents NPMC P systems, which use multiple channels for communication between bacteria and explores their computation power.
IEEE TRANSACTIONS ON NANOBIOSCIENCE
(2023)
Article
Biochemical Research Methods
Xixi Yang, Zhangming Niu, Yuansheng Liu, Bosheng Song, Weiqiang Lu, Li Zeng, Xiangxiang Zeng
Summary: Prediction of drug-target affinity is crucial in drug discovery. Existing deep learning methods focus on single modality inputs, while our proposed Modality-DTA leverages the multimodality of drugs and targets for better prediction performance. Experimental results demonstrate the superiority of Modality-DTA over existing methods in all metrics.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Xuan Lin, Zhe Quan, Zhi-Jie Wang, Yan Guo, Xiangxiang Zeng, Philip S. S. Yu
Summary: Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design. We propose a deep learning framework named GraphCPI, which captures the structural information of compounds and leverages the chemical context of protein sequences. Our method shows competitiveness and feasibility in extensive experiments based on widely-used CPI datasets.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Tao Song, Yongqi Ren, Shuang Wang, Peifu Han, Lulu Wang, Xue Li, Alfonso Rodriguez-Paton
Summary: Deep learning has greatly improved and changed the process of de novo molecular design. The proposed DNMG model utilizes a deep generative adversarial network combined with transfer learning to consider the 3D spatial information and physicochemical properties of molecules, generating valid and novel drug-like ligands. The computational results demonstrate that the molecules generated by DNMG have better binding ability and physicochemical properties for target proteins.
Article
Genetics & Heredity
Linfang Jiao, Yongqi Ren, Lulu Wang, Changnan Gao, Shuang Wang, Tao Song
Summary: Advancements in single-cell sequencing research have revolutionized our understanding of cellular heterogeneity and functional diversity. However, scRNA-seq data analysis remains a computational challenge due to the high dimensionality and sparsity of the data, as well as the time-consuming and subjective nature of manual cell type identification.
FRONTIERS IN GENETICS
(2023)
Article
Biochemistry & Molecular Biology
Linfang Jiao, Gan Wang, Huanhuan Dai, Xue Li, Shuang Wang, Tao Song
Summary: Single-cell transcriptomics is advancing our understanding of complex tissues and cells, with scRNA-seq holding great potential for cell composition identification. However, manual annotation is time-consuming and unreliable for scRNA-seq data analysis. This paper introduces scTransSort, a cell-type annotation method based on scRNA-seq data and Transformer concept, which reduces data sparsity and computational complexity for cell type identification.
Article
Environmental Sciences
Tao Song, Jiarong Wang, Jidong Huo, Wei Wei, Runsheng Han, Danya Xu, Fan Meng
Summary: This study aims to develop a new deep learning algorithm, EEMD-LSTM, to accurately predict the significant wave height (SWH) of deep and distant ocean. The results show that the EEMD-LSTM model outperforms other comparative models in short-term and medium- and long-term SWH predictions, with RMSEs of 0.0204, 0.0279, 0.0452, 0.0941, and 0.1949 for the future 1, 3, 6, 12, and 18 h, respectively. It can serve as a rapid SWH prediction system to ensure navigation safety and has great practical significance and application value.
FRONTIERS IN MARINE SCIENCE
(2023)
Review
Geosciences, Multidisciplinary
Tao Song, Cong Pang, Boyang Hou, Guangxu Xu, Junyu Xue, Handan Sun, Fan Meng
Summary: The utilization and exploitation of marine resources by humans have contributed to the growth of marine research. With advancing technology, artificial intelligence (AI) approaches are being applied to maritime research, complementing traditional marine forecasting models and observation techniques. This article explores the application of AI in ocean observation, phenomena identification, and element forecasting.
FRONTIERS IN EARTH SCIENCE
(2023)
Article
Biochemical Research Methods
Shudong Wang, Chuanru Ren, Yulin Zhang, Yunyin Li, Shanchen Pang, Tao Song
Summary: In this study, a novel predictive model called RPCA$\Gamma $NR is proposed, which utilizes a new robust PCA framework based on $\gamma $-norm and $l_{2,1}$-norm regularization and an augmented Lagrange multiplier method to optimize it, thereby deriving the association scores for SM-miRNA. Through extensive evaluation, RPCA$\Gamma $NR outperforms existing models in terms of accuracy, efficiency, and robustness, significantly streamlining the process of determining SM-miRNA associations.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biotechnology & Applied Microbiology
Su-Kui Jin, Li-Na Xu, Yu-Jia Leng, Ming-Qiu Zhang, Qing-Qing Yang, Shui-Lian Wang, Shu-Wen Jia, Tao Song, Ruo-An Wang, Tao Tao, Qiao-Quan Liu, Xiu-Ling Cai, Ji-Ping Gao
Summary: In this study, the researchers identified a NAC transcription factor, OsNAC24, that regulates starch synthesis in rice. Through analysis of osnac24 mutants, it was found that OsNAC24 regulates starch synthesis by modulating the mRNA and protein levels of OsGBSSI and OsSBEI. Additionally, OsNAC24 interacts with another NAC transcription factor, OsNAP, to coactivate the expression of target genes. These findings highlight the important role of the OsNAC24-OsNAP complex in fine-tuning starch synthesis in rice endosperm.
PLANT BIOTECHNOLOGY JOURNAL
(2023)
Article
Automation & Control Systems
Ying Xu, Chong Xu, Huan Zhang, Lei Huang, Yiping Liu, Yusuke Nojima, Xiangxiang Zeng
Summary: This article proposes a new metric to calculate the contribution of each decision variable to the optimization objectives, and based on this, a multiobjective evolutionary algorithm called DVCOEA is introduced. The experimental results show that DVCOEA is a competitive approach for solving large-scale multi/many-objective problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Handan Sun, Tao Song, Ying Li, Kunlin Yang, Danya Xu, Fan Meng
Summary: This study proposes a hybrid model based on ensemble empirical mode decomposition and Convolutional long short-term memory network to solve the non-smoothness problem in sea surface wind speed prediction. Experimental findings show that this model has the best prediction effect, and this advantage becomes increasingly evident as time increases.
APPLIED INTELLIGENCE
(2023)
