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Proteome-wide assessment of differential missense variant clustering in neurodevelopmental disorders and cancer

CELL GENOMICS 2025年第4期5卷 100807-100807页

作者： Ng, Jeffrey K. Chen, Yilin Akinwe, Titilope M. Heins, Hillary B. Mehinovic, Elvisa Chang, Yoonhoo Gutmann, David H. Gurnett, Christina A. Payne, Zachary L. Manuel, Juana G. Karchin, Rachel Turner, Tychele N. Washington Univ Sch Med Dept Genet St Louis MO 63110 USA Johns Hopkins Univ Dept Biomed Engn Baltimore MD 21205 USA Washington Univ Sch Med Mol Genet & Genom Grad Program St Louis MO 63110 USA Washington Univ Sch Med Human & Stat Genet Grad Program St Louis MO 63110 USA Washington Univ Sch Med Dept Neurol St Louis MO 63110 USA Washington Univ Sch Med Intellectual & Dev Disabil Res Ctr St Louis MO 63110 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21205 USA Johns Hopkins Univ Sch Med Sidney Kimmel Comprehens Canc Ctr Baltimore MD 21205 USA Johns Hopkins Univ Inst Computat Med Baltimore MD 21205 USA

Prior studies examining genomic variants suggest that some proteins contribute to both neurodevelopmental disorders (NDDs) and cancer. While there are several potential etiologies, here, we hypothesize that missense variation in proteins occurs in different clustering patterns, resulting in distinct phenotypic outcomes. This concept was first explored in 1D protein space and expanded using 3D protein structure models. Missense de novo variants were examined from 39,883 families with NDDs and missense somatic variants from 10,543 sequenced tumors covering five The Cancer Genome Atlas (TCGA) cancer types and two Catalog of Somatic Mutations in Cancer (COSMIC) pan-cancer aggregates of tissue types. We find 18 proteins with differential missense variation clustering in NDDs compared to cancers and 19 in cancers relative to NDDs. These proteins may be important for detailed assessments in thinking of future prognostic and therapeutic applications. We establish a framework for interpreting missense patterns in NDDs and cancer, using advances in 3D protein structure prediction.

关键词： neurodevelopmental disorders cancer clustering algorithm 3D protein structure models missense de novo somatic variant interpretation protein

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Cluster-based deep ensemble learning for emotion classification in Internet memes

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JOURNAL OF INFORMATION SCIENCE 2025年第1期51卷 265-283页

作者： Guo, Xiaoyu Ma, Jing Zubiaga, Arkaitz Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London England Nanjing Univ Aeronaut & Astronaut Coll Econ & Management 29 Jiangjun Rd Nanjing 211100 Jiangsu Peoples R China

Memes have gained popularity as a means to share visual ideas through the Internet and social media by mixing text, images and videos, often for humorous purposes. Research enabling automated analysis of memes has gained attention in recent years, including among others the task of classifying the emotion expressed in memes. In this article, we propose a novel model, cluster-based deep ensemble learning (CDEL), for emotion classification in memes. CDEL is a hybrid model that leverages the benefits of a deep learning model in combination with a clustering algorithm, which enhances the model with additional information after clustering memes with similar facial features. We evaluate the performance of CDEL on a benchmark data set for emotion classification, proving its effectiveness by outperforming a wide range of baseline models and achieving state-of-the-art performance. Further evaluation through ablated models demonstrates the effectiveness of the different components of CDEL.

关键词： clustering algorithm emotion classification ensemble learning Internet memes

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Evaluation on the state of sand filling layer and the influence on segment deformation of immersed tunnels

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Underground Space 2024年第3期16卷 224-238页

作者： Ziyao Xu Ailan Che Chao Su School of Naval Architecture Ocean and Civil EngineeringShanghai Jiao Tong UniversityShanghai 200240China CCCC Second Harbor Engineering Company Ltd. Wuhan 430040China

The immersed tunnel is considered an effective solution for traffic problems across rivers and *** sand filling layer,as an important part of immersed tunnel foundation treatments,directly affects segment attitude *** to difficulties in quality control of concealed construction and the complex hydrodynamic environment,the sand filling layer is prone to compaction defects,further leading to changes in segment ***,limited by structural concealment and state complexity,most studies consider the sand filling layer part of the foundation to study its impact on settlement while neglecting its influence on segment *** research proposes an evaluation method for the sand filling layer state based on elastic wave testing and the elastic wave characteristic parameters selected come from analysis of the time domain,frequency domain and time–frequency *** classifying the elastic wave characteristic parameters through the K-means clustering method,the relationship between the state of the sand filling layer and the elastic wave characteristic parameters is *** state of the sand filling layer is divided into dense,incompact,and void.A numerical model is established based on the Guangzhou BI-UT immersed tunnel with incompact and void sand filling layer states to simulate deformation and *** results indicate that the settlement of the tunnel segment is low in the eastern region and high in the western region due to the presence of a less dense sand filling layer,with a maximum differential settlement of 0.04 *** evaluation method plays a crucial role in guiding the construction of immersed tube tunnels.

关键词： Sand filling layer state Immersed tunnel Elastic wave test clustering algorithm

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A training pattern recognition algorithm based on weight clustering for improving cooling load prediction accuracy of HVAC system

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JOURNAL OF BUILDING ENGINEERING 2022年 52卷

作者： Chen, Sihao Wang, Liangzhu (Leon) Li, Jing Zhou, Guang Zhou, Xiaoqing Guangzhou Univ Sch Civil Engn Guangdong Prov Key Lab Bldg Energy Efficiency & A Acad Bldg Energy Efficiency Guangzhou 510006 Peoples R China Concordia Univ Dept Bldg Civil & Environm Engn Ctr Zero Energy Bldg Studies Montreal PQ H3G 1M8 Canada Zhongkai Univ Agr & Engn Guangzhou 510225 Peoples R China

The cooling load-based optimal control is an advanced technology for the efficient operation of heating, ventilation, and air conditioning (HVAC). Thus, the prediction reliability of cooling load plays a key role in HVAC's optimal control. Current publications primarily focused on the structure optimization of prediction models, while less on the clustering-based cooling load prediction. However, the data quality determines the upper limit of the model's prediction performance. Thus, a training pattern recognition algorithm based on weight clustering is proposed for improving cooling load prediction accuracy. Compared with the existing clustering-based prediction methods, the main innovations of the proposed method are: (i) considering the input variables' weights on cooling load in the clustering process;and (ii) investigating the matching between the various prediction models and the K-means clustering algorithm. The case studies showed that the proposed method achieves a significant improvement in the prediction performance, such as MAPEs of the MLR, MNR, and ANN decrease by 34.67%, 35.56%, and 14.53% on average, respectively. Compared with the non-weights clustering method, the introduction of the weights can further improve the above models' prediction accuracy, such as their MAPEs decrease by 6.30%, 7.59%, and 3.07% on average, respectively. These results also demonstrated that the clustering-based prediction method is more suitable for the regression models (e.g., MLR and MNR) with low complexity compared to the ANN. When the clustering number is about 4, the models' prediction performances were more robust. Applying the proposed method to the time-series models (i.e., AR, ARX, and ANN) resulted in their MAPEs as low as 1.79%, 1.78%, and 2.06%, respectively. the proposed method can provide a new idea for improving the accuracy of cooling load prediction.

关键词： Cooling load prediction Pattern recognition clustering algorithm Data preprocessing Mode identification

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A single-valued neutrosophic affinity propagation approach for engineering geological zoning of open-pit mine slopes

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 146卷

作者： Qin, Jibo Sun, Xiaoming Du, Shigui Ye, Jun Ningbo Univ Inst Rock Mech Ningbo 315211 Zhejiang Peoples R China China Univ Min & Technol Beijing State Key Lab Tunnel Engn Beijing 100083 Peoples R China China Univ Min & Technol Beijing Sch Mech & Civil Engn Beijing 100083 Peoples R China

Engineering geological zoning is an important foundation for assessing the stability of open-pit mine (OPM) slopes. Because of the complexity and uncertainty of geological conditions, this work brings great challenges to mining engineers and researchers. This paper presents a single-valued neutrosophic affinity propagation (SVNSAP) approach for engineering geological zoning of OPM slopes. The approach utilizes the concept of a singlevalued neutrosophic set (SVNS) to express the inconsistent and indeterminate information present in the influencing factors of engineering geological conditions through truth, indeterminate and falsity membership functions. Then, the similarity measure of SVNS is integrated into the affinity propagation (AP) algorithm to calculate the degree of similarity between the data points. Finally, the modified silhouette index is used to evaluate the clustering results and decide the optimal number of clusters. The practical application results of the engineering geological zoning of Lanping lead-zinc OPM demonstrate that the SVNS-AP method is an effective way for engineering geological zoning of OPM slopes in the uncertain environment. clustering results based on datasets in the literature and the UC Irvine Machine Learning Repository show that the proposed method can be used as a general clustering algorithm.

关键词： clustering algorithm Single-valued neutrosophic set Neutrosophic similarity measure Affinity propagation method Engineering geological zoning Open-pit mine

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Multivariate time series clustering analysis of the Global Dietary Database to uncover patterns in dietary trends (1990-2018)

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PUBLIC HEALTH NUTRITION 2025年第1期28卷 e89-e89页

作者： Matousek, Adriano Leung, Tiffany H. Pang, Herbert Univ Cambridge Dept Publ Hlth & Primary Care Robinson Way Cambridge England Univ Hong Kong Li Ka Shing Fac Med Dept Med Hong Kong Peoples R China Genentech Inc PD Data Sci & Analyt 1 DNA Way South San Francisco CA 94080 USA Univ Hong Kong Li Ka Shing Fac Med Sch Publ Hlth Hong Kong Peoples R China Duke Univ Sch Med Dept Biostat & Bioinformat Durham NC 27705 USA

Objective: Understanding country-level nutrition intake is crucial to global nutritional policies that aim to reduce disparities and relevant disease burdens. Still, there are limited numbers of studies using clustering techniques to analyse the recent Global Dietary Database (GDD). This study aims to extend an existing multivariate time series (MTS) clustering algorithm to allow for greater customisability and provide the first cluster analysis of the GDD to explore temporal trends in country-level nutrition profiles (1990-2018).Design: Trends in sugar-sweetened beverage intake and nutritional deficiency were explored using the newly developed programme 'MTSclust'. Time series clustering algorithms are different from simple clustering approaches in their ability to appreciate temporal ***: Nutritional and demographical data from 176 countries were analysed from the ***: Population representative samples of the 176 in the ***: In a three-class test specific to the domain, the MTSclust programme achieved a mean accuracy of 715 % (adjusted Rand Index [ARI] = 0381) while the mean accuracy of a popular algorithm, DTWclust, was 58 % (ARI = 0224). The clustering of nutritional deficiency and sugar-sweetened beverage intake identified several common trends among countries and found that these did not change by demographics. MTS clustering demonstrated a global convergence towards a Western ***: While global nutrition trends are associated with geography, demographic variables such as sex and age are less influential to the trends of certain nutrition intake. The literature could be further supplemented by applying outcome-guided methods to explore how these trends link to disease burdens.

关键词： clustering algorithm Machine learning Time-series clustering Trend Global Dietary Database

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Unsupervised clustering identifies thermohaline staircases in the Canada Basin of the Arctic Ocean

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ENVIRONMENTAL DATA SCIENCE 2024年 3卷 e13-e13页

作者： Schee, Mikhail G. Rosenblum, Erica Lilly, Jonathan M. Grisouard, Nicolas Univ Toronto Dept Phys Toronto ON Canada Univ Manitoba Ctr Earth Observat Sci Winnipeg MB Canada Planetary Sci Inst Tucson AZ USA

Thermohaline staircases are a widespread stratification feature that impacts the vertical transport of heat and nutrients and are consistently observed throughout the Canada Basin of the Arctic Ocean. Observations of staircases from the same time period and geographic region form clusters in temperature-salinity (T-S) space. Here, for the first time, we use an automated clustering algorithm called the hierarchical density-based spatial clustering of applications with noise to detect and connect individual well-mixed staircase layers across profiles from ice-tethered profilers. Our application only requires an estimate of the typical layer thickness and expected salinity range of staircases. We compare this method to two previous studies that used different approaches to detect layers and reproduce several results, including the mean lateral density ratio R-L and that the difference in salinity between neighboring layers is a magnitude larger than the salinity variance within a layer. We find that we can accurately and automatically track individual layers in coherent staircases across time and space between different profiles. In evaluating the algorithm's performance, we find evidence of different physical features, namely splitting or merging layers and remnant intrusions. Further, we find a dependence of R-L on pressure, whereas previous studies have reported constant R-L. Our results demonstrate that clustering algorithms are an effective and parsimonious method of identifying staircases in ocean profile data. Impact Statement clustering algorithms are unsupervised machine learning methods that are used across many areas of data science. The use of such methods can automate the identification of certain features, thus allowing for the analysis of very large datasets. Here, we show that a particular clustering algorithm called hierarchical density-based spatial clustering of applications with noise (HDBSCAN) can be used to automatically identify thermohaline stai

关键词： Arctic Ocean clustering algorithm ice-tethered profilers machine learning thermohaline staircases

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Power Load Curve clustering Based on IGWOK-medoids 7

Power Load Curve Clustering Based on IGWOK-medoids

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7th International Conference on Energy, Electrical and Power Engineering (CEEPE)

作者： Zhang, Weifeng Wu, Binfeng Zhao, Xinyi Xu, Jing Zhang, Yihan Wu, Pingping Wang, Li Chen, Xi State Grid Zhejiang Elect Power Co Ltd Lishui Power Supply Co Lishui Peoples R China

ISBN: (纸本)9798350375794;9798350375800

This paper introduces an advanced clustering algorithm for power load curves, named IGWO-Kmedoids, addressing challenges faced by traditional clustering methods like k-means and DBSCAN in handling power system load data. These challenges include difficulty in parameter setting, slow clustering speeds, and suboptimal results. The proposed algorithm begins with data preprocessing, including outlier removal and filling missing values through interpolation, followed by standardization. It then employs Principal Component Analysis (PCA) for dimensionality reduction, easing the computational load for clustering. The core innovation lies in optimizing the K-medoids clustering algorithm with an Improved Grey Wolf Optimization (IGWO) approach, enhancing clustering accuracy and speed. This optimization significantly boosts the power system's operational efficiency and the optimization of scheduling policies.

关键词： clustering algorithm IGWO-Kmedoids PCA

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A NOVEL clustering algorithm BASED ON P SYSTEMS

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2014年第2期10卷 753-765页

作者： Jiang, Yang Peng, Hong Huang, Xiaoli Zhang, Jiarong Shi, Peng Xihua Univ Ctr Radio Adm & Technol Dev Chengdu 610039 Sichuan Peoples R China Harbin Engn Univ Coll Automat Harbin 150001 Heilongjiang Peoples R China Victoria Univ Sch Engn & Sci Melbourne Vic 8001 Australia

Membrane computing (known as P systems) is a novel class of distributed parallel computing models. In this paper, a partition-based clustering algorithm under the framework of membrane computing is proposed. The clustering algorithm is based on a tissue-like P system, which is used to exploit the optimal cluster centers for a data set. Each object in the tissue-like P system represents a group of candidate cluster centers and is evolved through simulated annealing mechanism and mutation mechanism. Meanwhile, communication rules are used to exchange and share the objects between different elementary membranes and between elementary membranes and the environment. The proposed clustering algorithm is evaluated over two artificial data sets and two real-life data sets and is further compared with k-means algorithm and GA-based k-means algorithm respectively. The comparison results reveal the superiority of the proposed clustering algorithm in terms of clustering quality and stability.

关键词： Membrane computing Tissue-like P systems clustering algorithm Simulated annealing K-means

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Robust optimization-based energy management system for integrating renewable energy resources and smart grid technology

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2025年 75卷

作者： Loganathan, N. Mayurappriyan, P. S. Lakshmi, K. Madhankumar, S. Sri Krishna Coll Engn & Technol Dept Elect & Elect Engn Coimbatore 641008 India Kumaraguru Coll Technol Dept Elect & Instrumentat Engn Coimbatore 641049 India KSR Coll Engn Dept Elect & Elect Engn Tiruchengode 637215 India Sri Krishna Coll Engn & Technol Dept Mechatron Engn Coimbatore 641008 India

This study focuses on enhancing Energy Management (EM) by integrating Renewable Energy Resources (RERs) like Wind Energy (WE) and Solar Energy (SE) with grid technology. To analyze the proposed integration technology, an academic campus with interconnected group, cost-responsive demands was considered. These demands can be met using both the grid and RERs;however, EM challenges arise when integrating RERs with the grid. The Evolutionary Energy Preserve Rate clustering algorithm (EEPRCA) was utilized to analyze the EM, and the Robust Optimization (RO) strategy was used to predict the unpredictability associated with the power output of RERs, as well as the cost of power produced and energy received from or sold to the main grid. Additionally, Smart Grid (SG) technology was employed to enable bidirectional connectivity between the Energy Management System (EMS) and the main grid, as well as between the EMS and RERs. The EMS plans energy consumption at the start of the working day without SG. However, with SG, the EMS uses bidirectional communication to reschedule utilization based on energy prices and WE and SE generation. Using SG technology leads to a 32% improvement in total utility grid efficiency compared to an EMS without SG. Also, an EMS with SG maximizes the use of available wind and solar RERs, whereas without SG only 55% of these resources are utilized. From these outcomes, it is suggested that the proposed EMS integration strategy can efficiently manage energy, increase the consumption of RERs, and reduce the energy utilization cost for the academic campus.

关键词： Energy management system Renewable energy resources Smart grid clustering algorithm Robust optimization

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