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clustering Gene Expression Data Through Modified Agglomerative M-CURE Hierarchical Algorithm

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Computer Systems Science & Engineering 2022年第6期41卷 1027-1041页

作者： E.Kavitha R.Tamilarasan N.Poonguzhali M.K.Jayanthi Kannan A Constituent College of Anna University University College of EngineeringVillupuram605103India A Constituent College of Anna University University College of EngineeringPattukkottai614701India Department of Computer Science and Engineering Manakula Vinayagar Institute of TechnologyPuducherry605107India Department of Computer Science Engineering Faculty of Engineering and TechnologyJAIN(Deemed-To-Be University)Bangalore562112India

Gene expression refers to the process in which the gene information isused in the functional gene product synthesis. They basically encode the proteinswhich in turn dictate the functionality of the cell. The first step in gene expressionstudy involves the clustering usage. This is due to the reason that biological networks are very complex and the genes volume increases the comprehending challenges along with the data interpretation which itself inhibit vagueness, noise andimprecision. For a biological system to function, the essential cellular moleculesmust interact with its surrounding including RNA, DNA, metabolites and proteins. clustering methods will help to expose the structures and the patterns inthe original data for taking further decisions. The traditional clustering techniquesinvolve hierarchical, model based, partitioning, density based, grid based and softclustering methods. Though many of these methods provide a reliable output inclustering, they fail to incorporate huge data of gene expressions. Also, thereare statistical issues along with choosing the right method and the choice of dissimilarity matrix when dealing with gene expression data. We propose to use amodified clustering algorithm using representatives (M-CURE) in this workwhich is more robust to outliers as compared to K-means clustering and also ableto find clusters with size variances.

关键词： clustering gene identifiers representatives dimension reduction

clustering solver for displacement-based numerical homogenization

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Theoretical & Applied Mechanics Letters 2022年第3期12卷 207-212页

作者： Shaoqiang Tang Xi Zhu HEDPS CAPT and LTCSCollege of EngineeringPeking UniversityBeijing 100871China

Based on strain-clustering via k-means,we decompose computational domain into clusters of possibly disjoint cells.Assuming cells in each cluster take the same strain,we reconstruct displacement field.We further propose a new way to condensate the governing equations of displacement-based finite element method to reduce the complexity while maintain the accuracy.Numerical examples are presented to illustrate the efficiency of the clustering solver.Numerical convergence studies are performed for the examples.Avoiding complexities which is common in existing clustering analysis methods,the proposed clustering solver is easy to implement,particularly for numerical homogenization using commercial softwares.

关键词： clustering Finite element method Numerical homogenization

Massive data clustering by multi-scale psychological observations

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National Science Review 2022年第2期9卷 43-51页

作者： Shusen Yang Liwen Zhang Chen Xu Hanqiao Yu Jianqing Fan Zongben Xu National Engineering Laboratory of Big Data Analytics Xi'an Jiaotong University Department of Mathematics and Statistics University of Ottawa Center for Statistics and Machine Learning Princeton University Industrial Artificial Intelligent Center Pazhou Laboratory

clustering is the discovery of latent group structure in data and is a fundamental problem in artificial intelligence,and a vital procedure in data-driven scientific research over all disciplines.Yet,existing methods have various limitations,especially we ak cognitive interpretability and poor computational scalability,when it comes to clustering massive datasets that are increasingly available in all domains.Here,by simulating the multi-scale cognitive observation process of humans,we design a scalable algorithm to detect clusters hierarchically hidden in massive datasets.The observation scale changes,following the Weber-Fechner law to capture the gradually emerging meaningful grouping structure.We validated our approach in real datasets with up to a billion records and 2000 dimensions,including taxi trajectories,single-cell gene expressions,face images,computer logs and audios.Our approach outperformed popular methods in usability,efficiency,effectiveness and robustness across different domains.

关键词： massive data clustering psychological observation Weber–Fechner law cognitive interpretability computational scalability

clustering of prostate cancer healthcare pathways in the French National Healthcare database

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Cancer Innovation 2023年第1期2卷 52-64页

作者： Roméo Baulain Jérémy Jové Dunia Sakr Marine Gross‐Goupil Magali Rouyer Marius Puel Patrick Blin Cécile Droz‐Perroteau Régis Lassalle Nicolas H.Thurin École nationale de la statistique et de l'administrationéconomique Paris(ENSAE) Institut Polytechnique ParisPalaiseauFrance Univ.Bordeaux INSERM CIC‐P 1401Bordeaux PharmacoEpiBordeauxFrance Medical Oncology Hôpital Saint AndréCHU de BordeauxBordeauxFrance

Background:Healthcare pathways of patients with prostate cancer are heterogeneous and complex to apprehend using traditional descriptive statistics.clustering and visualization methods can enhance their characterization.Methods:Patients with prostate cancer in 2014 were identified in the French National Healthcare database(Système National des Données de Santé—SNDS)and their data were extracted with up to 5 years of history and 4 years of follow‐up.Fifty‐one‐specific encounters constitutive of prostate cancer management were synthesized into four macro‐variables using a clustering approach.Their values over patient follow‐ups constituted healthcare pathways.Optimal matching was applied to calculate distances between pathways.Partitioning around medoids was then used to define consistent groups across four exclusive cohorts of incident prostate cancer patients:Hormone‐sensitive(HSPC),metastatic hormone‐sensitive(mHSPC),castration‐resistant(CRPC),and metastatic castration‐resistant(mCRPC).Index plots were used to represent pathways clusters.Results:The repartition of macro‐variables values—surveillance,local treatment,androgenic deprivation,and advanced treatment—appeared to be consistent with prostate cancer status.Two to five clusters of healthcare pathways were observed in each of the different cohorts,corresponding for most of them to relevant clinical patterns,although some heterogeneity remained.For instance,clustering allowed to distinguish patients undergoing active surveillance,or treated according to cancer progression risk in HSPC,and patients receiving treatment for potentially curative or palliative purposes in mHSPC and mCRPC.Conclusion:Visualization methods combined with a clustering approach enabled the identification of clinically relevant patterns of prostate cancer management.Characterization of these care pathways is an essential element for the comprehension and the robust assessment of healthcare technology effectiveness.

关键词： prostate cancer healthcare pathway clustering machine learning SNDS

clustering and classification of energy meter data: A comparison analysis of data from individual homes and the aggregated data from multiple homes

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Building Simulation 2021年第1期14卷 103-117页

作者： Juan Sala Rongling Li Morten H.Christensen Department of Civil Engineering Technical University of DenmarkLyngbyDenmark Department of Electrical Engineering Technical University of DenmarkLyngbyDenmark

The transition towards a more sustainable environment requires the development of new control systems on the demand side to integrate renewable energy sources into the energy systems.For this purpose,energy meter data of homes have been broadly used in modelling,forecast and optimal control of energy use.However,usability and reliability of household energy meter data have not been specifically addressed.In this study,we apply commonly used machine learning methods on the heating consumption data of(1)two individual homes in an apartment building and(2)the district heating substation of the apartment building which includes 72 homes,to identify how the characteristics of data affect the result of data analysis.Two clustering approaches were applied using the K-means algorithm to group similar heating daily profiles.Using the clustering results,different classification algorithms such as logistic regression and random forest were applied to predict the heating consumption level with regards to the weather conditions.The data analysis process showed that the substation data which is the aggregated heating consumption of the 72 homes is more reliable and valid for energy prediction than the data from two individual homes.This is due to the large variation and uncertainty in the daily energy use of individual homes.

关键词： smart meter data daily profile clustering classification data usability heat substation

simplifyEnrichment:A Bioconductor Package for clustering and Visualizing Functional Enrichment Results

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Genomics, Proteomics & Bioinformatics 2023年第1期21卷 190-202页

作者： Zuguang Gu Daniel Hübschmann Molecular Precision Oncology Program National Center for Tumor Diseases(NCT)HeidelbergD-69120 HeidelbergGermany Heidelberg Institute of Stem Cell Technology and Experimental Medicine(HI-STEM) D-69120 HeidelbergGermany German Cancer Consortium(DKTK) D-69120 HeidelbergGermany Department of Pediatric Immunology Hematology and OncologyUniversity Hospital HeidelbergD-69120 HeidelbergGermany

Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates the biological importance of a list of genes of interest.However,it may produce a long list of significant terms with highly redundant information that is difficult to summarize.Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters.We propose a new method named binary cut for clustering similarity matrices of functional terms.Through comprehensive benchmarks on both simulated and real-world datasets,we demonstrated that binary cut could efficiently cluster functional terms into groups where terms showed consistent similarities within groups and were mutually exclusive between groups.We compared binary cut clustering on the similarity matrices obtained from different similarity measures and found that semantic similarity worked well with binary cut,while similarity matrices based on gene overlap showed less consistent patterns.We implemented the binary cut algorithm in the R package simplifyEnrichment,which additionally provides functionalities for visualizing,summarizing,and comparing the clustering.The simplifyEnrichment package and the documentation are available at https://***/packages/simplifyEnrichment/.

关键词： Functional enrichment Simplify enrichment clustering R/Bioconductor Software Visualization

clustering Structure Analysis in Time-Series Data With Density-Based Clusterability Measure

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1332-1343页

作者： Juho Jokinen Tomi Raty Timo Lintonen VTT Technical Research Centre of Finland Vuorimiehentie 3 P.O.Box 100002044 EspooFinland

clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.

关键词： clustering exploratory data analysis time-series unsupervised learning

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clustering and Resource Allocation Strategy for D2D Multicast Networks with Machine Learning Approaches

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China Communications 2021年第1期18卷 196-211页

作者： Fan Jiang Lan Zhang Changyin Sun Zeng Yuan Xi’an University of Posts and Telecommunications Xi’an 710121China Shaanxi Key Laboratory of Telecommunications and Information Networks and Security Xi’an 710121China

In this paper,the clustering and resource allocation problem in device-to-device(D2D)multicast transmission underlay cellular networks are investigated.For the sake of classifying D2D users into different D2D multicast clusters,a hybrid intelligent clustering strategy(HICS)based on unsupervised machine learning is proposed first.By maximizing the total energy efficiency of D2D multicast clusters,a joint resource allocation scheme is then presented.More specifically,the energy efficiency optimization problem is constructed under the quality of service(QoS)constraints.Since the joint optimization problem is non-convex,we transform the original problem into a mixed-integer programming problem according to the Dinkelbach algorithm.Furthermore,to avoid the high computational complexity inherent in the traditional resource allocation problem,a Q-Learning based joint resource allocation and power control algorithm is proposed.Numerical results reveal that the proposed algorithm achieves better energy efficiency in terms of throughput per energy consumption.

关键词： device-to-device multicast communication clustering energy efficiency resource allocation Q-Learning

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Review of clustering Technology and Its Application in Coordinating Vehicle Subsystems

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Automotive Innovation 2023年第1期6卷 89-115页

作者： Caizhi Zhang Weifeng Huang Tong Niu Zhitao Liu Guofa Li Dongpu Cao Chongqing Automotive Collaborative Innovation Centre The State Key Laboratory of Mechanical TransmissionsChongqing UniversityChongqing 400044China State Key Laboratory of Industrial Control Technology Institute of Cyber‑Systems and ControlZhejiang UniversityHangzhou 310027China School of Vehicle and Mobility Tsinghua UniversityBeijingChina

clustering is an unsupervised learning technology,and it groups information(observations or datasets)according to similarity measures.Developing clustering algorithms is a hot topic in recent years,and this area develops rapidly with the increasing complexity of data and the volume of datasets.In this paper,the concept of clustering is introduced,and the clustering technologies are analyzed from traditional and modern perspectives.First,this paper summarizes the principles,advantages,and disadvantages of 20 traditional clustering algorithms and 4 modern algorithms.Then,the core elements of clustering are presented,such as similarity measures and evaluation index.Considering that data processing is often applied in vehicle engineering,finally,some specific applications of clustering algorithms in vehicles are listed and the future development of clustering in the era of big data is highlighted.The purpose of this review is to make a comprehensive survey that helps readers learn various clustering algorithms and choose the appropriate methods to use,especially in vehicles.

关键词： Unsupervised learning clustering Similarity measures Vehicle