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iSuc-ChiDT: A Computational Method for Identifying Succinylation Sites using Statistical Difference Table Encoding and the Chi-Square decision Table Classifier

Research Square

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Research Square 2021年

作者： Zeng, Ying Chen, Yuan Yuan, Zheming College of Computer and Communication Hunan Institute of Engineering Xiangtan411104 China Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making Hunan Agricultural University Changsha410128 China

Background: Lysine succinylation is a type of protein post-translational modification which is widely involved in cell differentiation, cell metabolism and other important physiological activities. To study the molecular mechanism of succinylation in depth, succinylation sites need to be accurately identified, and because experimental approaches are costly and time-consuming, there is a great demand for reliable computational methods. Feature extraction is a key step in building succinylation site prediction models, and the development of effective new features improves predictive accuracy. Because the number of false succinylation sites far exceeds that of true sites, traditional classifiers perform poorly, and designing a classifier to effectively handle highly imbalanced datasets has always been a challenge. Results: We propose a new computational method, iSuc-ChiDT, to identify succinylation sites in proteins. In iSuc-ChiDT, chi-square statistical difference table encoding is developed to extract positional features, and has the highest predictive accuracy and fewest features compared to binary encoding and physicochemical property encoding. The chi-square decision table (ChiDT) classifier is designed to implement imbalanced pattern classification. With a training set of 4748:50,551(true: false sites), independent tests showed that ChiDT significantly outperformed traditional classifiers (including random forest, artificial neural network and relaxed variable kernel density estimator) in predictive accuracy and only taking 17s. Using an independent testing set of experimentally identified succinylation sites, iSuc-ChiDT achieved sensitivity of 70.47%, specificity of 66.27%, Matthews correlation coefficient of 0.205, and a global accuracy index Q9 of 0.683, showing a significant improvement in sensitivity and overall accuracy compared to PSuccE, Success, SuccinSite and other existing succinylation site predictors. Conclusions: iSuc-ChiDT shows great promise in pr

关键词： Proteins

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Science as Exploration in a Knowledge Landscape: Tracing Hotspots or Seeking Opportunity?

arXiv

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arXiv 2023年

作者： Liu, Feifan Zhang, Shuang Xia, Haoxiang Institute of Systems Engineering Dalian University of Technology No.2 Linggong Road Liaoning Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Linggong Road Liaoning Dalian116024 China Institute for Advanced Intelligence Dalian University of Technology Linggong Road Liaoning Dalian116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education of China Linggong Road Liaoning Dalian116024 China

The selection of research topics by scientists can be viewed as an exploration process conducted by individuals with cognitive limitations traversing a complex cognitive landscape influenced by both individual and social factors. While existing theoretical investigations have provided valuable insights, the intricate and multifaceted nature of modern science hinders the implementation of empirical experiments. This study leverages advancements in deep learning techniques to investigate the patterns and dynamic mechanisms of topic-transition among scientists. By constructing the knowledge space across 6 large-scale disciplines, we depict the trajectories of scientists' topic transitions within this space, measuring the flow and distance of research regions across different sub-spaces. Our findings reveal a predominantly conservative pattern of topic transition at the individual level, with scientists primarily exploring local knowledge spaces. Furthermore, simulation modeling analysis identifies research intensity, driven by the concentration of scientists within a specific region, as the key facilitator of topic transition. Conversely, the knowledge distance between fields serves as a significant barrier to exploration. Notably, despite potential opportunities for breakthrough discoveries at the intersection of subfields, empirical evidence suggests that these opportunities do not exert a strong pull on scientists, leading them to favor familiar research areas. Our study provides valuable insights into the exploration dynamics of scientific knowledge production, highlighting the influence of individual cognition, social factors, and the intrinsic structure of the knowledge landscape itself. These findings offer a framework for understanding and potentially shaping the course of scientific progress. © 2023, CC BY-NC-ND.

关键词： Deep learning

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The Temporal Structural Pattern in Scientific Collaborative Behavior from the Perspective of Complex network 23rd

The Temporal Structural Pattern in Scientific Collaborative...

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23rd International Symposium on Knowledge and Systems Sciences, KSS 2024

作者： Zholdoshbaeva, Elina Zhang, Shuang Liu, Feifan Xia, Haoxiang Institute of Systems Engineering Dalian University of Technology No. 2 Linggong Road Liaoning Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Linggong Road Liaoning Dalian116024 China School of Business East China University of Science and Technology 130 Meilong Road Xuhui District Shanghai200237 China Institute for Advanced Intelligence Dalian University of Technology Linggong Road Liaoning Dalian116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education of China Linggong Road Liaoning Dalian116024 China

ISBN: (纸本)9789819601776

The rapid advancement of science has underscored the importance of collaboration among scientists. While many studies have explored the structure of collaboration networks, there is a lack of comprehensive quantitative research on the temporal patterns of these collaborations. This study addresses this gap by analyzing temporal scientific collaboration networks in physics, spanning from 1960 to 2021, using complex network theories and methods. By constructing five-year temporal collaborative networks and employing metrics such as network diameter, shortest path, and clustering coefficient, this research reveals an increase in connectivity and small-world characteristics over time. Additionally, community detection highlights a shift towards specialized collaboration patterns and increased cross-community interactions. The study also uncovers growing centrality and social stratification within the research system, with the "rich club" phenomenon indicating strong and dynamic collaborations among highly connected scientists. Temporal analysis shows that while ultra-high connectivity scientists engage in fluctuating relationships, medium-high connectivity scientists maintain more stable collaborations. These findings provide insights into the evolution of collaborative behaviors and inform science policy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative learning

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Opinion dynamics in finance and business: a literature review and research opportunities

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Financial Innovation 2020年第1期6卷 971-992页

作者： Quanbo Zha Gang Kou Hengjie Zhang Haiming Liang Xia Chen Cong‑Cong Li Yucheng Dong School of Management Science and Real Estate Chongqing UniversityChongqing 400045China School of Business Administration Southwestern University of Finance and EconomicsChengdu 611130China Business School Hohai UniversityNanjing 211100China Center for Network Big Data and Decision-Making Business SchoolSichuan UniversityChengdu 610065China School of Economics and Management Southwest Jiaotong UniversityChengdu 610031China

Opinion dynamics is an opinion evolution process of a group of agents,where the final opinion distribution tends to three stable states:consensus,polarization,and *** present,the opinion dynamics models have been extensively studied in differrent *** paper provides a review of opinion dynamics in finance and business,such as,finance,marketing,e-commerce,politics,and group decision ***,identified research challenges have been proposed to promote the future research of this topic.

关键词： Opinion dynamics Financial market Business

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Deep Learning on Parkinson's Rat behavior Point Sets in Open Field 20

Deep Learning on Parkinson's Rat behavior Point Sets in Open...

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2020 International Symposium on Artificial Intelligence in Medical Sciences, ISAIMS 2020

作者： Wu, Lei He, Quan Cao, Hezhi Liu, Long Research Center for Big Data and Intelligent Decision Making School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan province China Department of Biology and Chemistry National University of Defense Technology Changsha Hunan province China College of Electronic Science and Technology National University of Defense Technology Changsha Hunan province China

ISBN: (纸本)9781450388603

Parkinson's disease (PD) is one of the common progressive neurodegenerative disorder with motor deficits. A substantial volume of research has reported various methods to characterize Parkinson's rat model, but the performances are unsatisfactory. Because of the complexity of behavior data, we can hardly recognize the non-trivial behavioral traits and distinguish the difference between ill rat and the healthy. In this paper, we construct a deep learning model to analyze PD rat behavior data, which is obtained from an optical motion capture system. This system is widely used in movement analysis, robotics, making animations, and can provide high resolution of spatiotemporal information. By using the three-dimensional motion capture technology with retro-reflective optical markers placed in locations that represents rat head and body axis, we can get real-time three-dimensional coordinate information of rat's motion in open field. After the preprocessing, we fed the data set to our model to get a global feature via three abstraction layers and then process them by fully connected layers to obtain the final classification result. The results have shown that this method is robust enough to identify biomechanical and kinematic changes in response to Parkinson's rat with unilateral Striatum lesion. © 2020 ACM.

关键词： Rats

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Multi-trait genome-wide association studies reveal novel pleiotropic loci associated with yield and yield-related traits in rice

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Journal of Integrative Agriculture 2024年

作者： Chunhai Liu Chao Wu Zheming Yuan Bingchuan Tian Peiyi Yu Deze Xu Xingfei Zheng Lanzhi Li Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making College of Plant Protection Hunan Agricultural University Changsha 410128 China Huazhi Biotechnology Co. Ltd Changsha 410125 China Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement Food Crop Institute Hubei Academy of Agricultural Sciences Wuhan 430064 China

Rice yield is a complex trait affected by many related traits. Traditional single-trait genome-wide association studies (GWAS) have limitations when studying complex traits, as they cannot account for the genetic relationships among multiple traits. Multi-trait GWAS, which can consider the relationships among multiple traits and identify pleiotropic loci, is more suitable for complex traits such as rice yield than single-trait GWAS. In this study, we conducted a multi-trait GWAS on 11 two-trait combinations of yield and yield-related traits with 575 hybrid rice varieties across two environments. All of these yield-related traits showed significant genetic correlation with yield (YD), including filled grains per panicle (FGPP), kilo-grain weight (KGW), tillers per plant (TP), primary branch number (PB), secondary branch number (SB) and main panicle length (MPL). In total we identified 44 pleiotropic quantitative trait loci (pQTLs), including 29 new pQTLs not found in single-trait GWAS. We then screened 23 pQTLs showing common effects in two traits as key pQTLs. These key pQTLs were subsequently analyzed for haplotype analysis and identified 13 pleiotropic candidate genes. Finally, we identified two optimal yield-enhancing allele combinations by pyraming superior haplotypes: GS3-GL3.1-OsCIPK17 for the YD-KGW combination and GNP12 for the YD-FGPP and YD-SB combinations. This study provides pleiotropic candidate genes and allele combinations that exhibit superior differences in both yield and yield-related traits, offering valuable information for future high-yielding rice breeding.

关键词： rice yield multi-trait GWAS genetic correlation pleiotropic SNP 1

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Establishment of a novel engineering breeding technology for cytoplasmic male sterility

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Crop Journal 2025年

作者： Xu, Na Yu, Dong Zhang, Longhui Wang, Tiankang Hu, Zhongxiao Lei, Quanqiang Qiu, Yingxin Yuan, Zheming Song, Shufeng Li, Li Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-making College of Plant Protection Hunan Agricultural University Hunan Changsha 410128 China State Key Laboratory of Hybrid Rice Hunan Hybrid Rice Research Center Hunan Academy of Agricultural Sciences Hunan Changsha 410125 China Yuelushan Laboratory Hunan Changsha 410128 China Longping Branch College of Biology Hunan University Hunan Changsha 410125 China

The three-line hybrid rice system, which relies on cytoplasmic male sterility (CMS), faces challenges in breeding efficiency. To address these challenges, we developed CMS engineering breeding technology (CEBT). First, we constructed a complementation vector containing the fertility restoration module Double Rf4-Rf20, sorting marker gene DsRed2, and pollen lethal gene ZmAA1, then transformed this into a CMS line. We obtained reproductive lines without alterations to main agronomic traits, which can produce engineering sterile lines (ESL) and reproductive line seeds at a 1:1 ratio by self-crossing. CEBT converts the traditional three-line system into an efficient ‘new two-line’ hybrid framework: the reproductive line is improved while simultaneously improving the ESL, which significantly shortens the breeding process for three-line hybrid rice. © 2025 Crop Science Society of China and Institute of Crop Science, CAAS

关键词： CEBT CMS-WA Reproductive lines Rf gene Three-line hybrid rice

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Protection of Large Sparse network Through Optimal Cropped Depth-First Traversal Tree

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IEEE Transactions on network Science and Engineering 2025年

作者： Wei, Wei Hu, Qiuyuan Zhang, Qinghui Li, Peng Henan University of Technology Key Laboratory of Grain Information Processing and Control Ministry of Education Zhengzhou450001 China Henan University of Technology Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Zhengzhou450001 China Henan University of Technology Henan Grain Big Data Analysis and Application Engineering Research Center Zhengzhou450001 China Beijing Institute of Technology No. 5 Zhongguancun South Street Haidian District Beijing100086 China

The robustness of the network is its ability to withstand unexpected failures. There is an urgent need to enhance the robustness of sparse networks as they are prone to breakage. Among the various ways to enhance network robustness measured by the edge connectivity of the entire network, shielding candidate edges is a popular method when redundant edges are not affordable. However, existing tree-cutting favors dense graphs with exponential cropped-tree growth of search space, but the search space only scales linearly in sparse graphs. We explore sparse-graph limitations by extending cropping to maximum depth while maintaining sublinear overhead, leveraging ancestor-node constraints and cropping-chain theorems for optimal efficiency. Compared with the optimal algorithm, the results in representative network types show that the robustness measurement of small-scale graphs can be enhanced to 100%, and the ratio of acceleration can be greater than 105. In large-scale graphs with millions of nodes, robustness can be improved to over 99%, while the time can be as low as tens of seconds. © 2013 IEEE.

关键词： Trees (mathematics)

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Project duration-cost-quality prediction model based on Monte Carlo simulation

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Journal of Physics: Conference Series 2021年第1期1978卷

作者： Xingguang Chen Luqiang Cheng Guohua Deng Shuqi Guan Lewei Hu School of Business Jianghan University Wuhan 430056 China Institute of intelligent decision-making Jianghan University Wuhan 430056 China Big Data Research Center Manufacturing Industry Development Research Center on Wuhan City Circle Jianghan University Wuhan 430056 China

Based on the earned value management theory, the project duration and cost forecast data are obtained through Monte Carlo simulation, combined with the knowledge of mathematical statistics to in-depth analysis of the data, and then the project quality calculation is completed by establishing the quality correlation function. On the basis of existing scholars' research, at the process level, the management focus is identified by calculating process influence and quality, and at the project level, the operation of the entire project is simulated and predicted by the calculation of the total construction period, cost, and quality.

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Distributed linguistic representations in decision making: Taxonomy, key elements and applications, and challenges in data science and explainable artificial intelligence

arXiv

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arXiv 2020年

作者： Wu, Yuzhu Zhang, Zhen Kou, Gang Zhang, Hengjie Chao, Xiangrui Li, Cong-Cong Dong, Yucheng Herrera, Francisco School of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Institute of Systems Engineering Dalian University of Technology Dalian116024 China Business School Hohai University Nanjing211100 China Center for Network Big Data and Decision-Making Business School Sichuan University Chengdu610065 China School of Economics and Management Southwest Jiaotong University Chengdu610031 China School of Information Management and Statistics Hubei University of Economics Wuhan430205 China University of Granada Granada18071 Spain Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence. Copyright © 2020, The Authors. All rights reserved.

关键词： decision making

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