To illustrate the literature distribution, research power distribution, and research hotspots in the radiomics research by using knowledge mapping analysis, and to provide reference for relevant researchers.
 Methods: Bibliographies from literature regarding radiomics in Web of Science database were downloaded. BICOM 2.0.1 and SATI 3.2 were used to clean and caculate the frequency of publication year, journal, author, key word, and research institution. CiteSpace V4.4.R1 was used to build the knowledge map of scientific research collaboration network between countries/regions.Ucinet 6 was used to build the knowledge map of scientific research collaboration network between core authors and institutions. gCLUTO 1.0 was applied to construct high-frequency keywords bi-clustering map.
 Results: A total of 700 literature was screened. Since 2012 the number of publications has been growing rapidly year by year. The United States, China, and Netherlands were leaders in this field. There were 5 major scientific research institution cooperative groups and 10 major author cooperative groups. Eight research hotspots were clustered by using high-frequency key word bi-clustering analysis.
 Conclusion: Radiomics is a new field and develops very fast. More and more countries, research institutions, and researchers with multidisciplinary background are going to participate in this filed. New terminology and new methods are going to appear in the field.
China ; Cluster Analysis

Single-cell transcriptome sequencing (scRNA-seq) can resolve the expression characteristics of cells in tissues with single-cell precision, enabling researchers to quantify cellular heterogeneity within populations with higher resolution, revealing potentially heterogeneous cell populations and the dynamics of complex tissues. However, the presence of a large number of technical zeros in scRNA-seq data will have an impact on downstream analysis of cell clustering, differential genes, cell annotation, and pseudotime, hindering the discovery of meaningful biological signals. The main idea to solve this problem is to make use of the potential correlation between cells and genes, and to impute the technical zeros through the observed data. Based on this, this paper reviewed the basic methods of imputing technical zeros in the scRNA-seq data and discussed the advantages and disadvantages of the existing methods. Finally, recommendations and perspectives on the use and development of the method were provided.
Cluster Analysis ; Transcriptome

No abstract available.
Cluster Analysis* ; Humans ; Risk Factors*

No abstract available.
Blister* ; Cluster Analysis* ; Skin Diseases*

OBJECTIVETo analyze the spatial-temporal distribution of Hepatitis E (HEV) in Hunan province from 2006 to 2014.

METHODSData related to HEV cases in Hunan province from 2006 to 2014 were collected from the Infectious Diseases Reporting Information System in the formation System of Disease Prevention and Control of China. Based on ArcGIS (10.2) and SaTScan(version 9.1), spatial autocorrelation analysis and space-time clustering analysis were used to study the prevalence on HEV.

RESULTSA total of 7 124 HEV cases were reported with 3 deaths during this period. The average annual incidence rate was 1.22/10(5). Most of the cases were over 55 years old and the majority of them (54.15%) were farmers. The distribution of HEV showed differences on locations and the regions with high incidence seen in northern and western areas of Hunan. However the regions with low incidence appeared in central or southern parts of Hunan. Data from the global spatial autocorrelation analysis showed that there was space autocorrelation on the HEV incidence rates in counties (cities, districts) (Moran'I was positive,P<0.05). A total of 31 countries were found in the high-high region with most of the clusters located in northern and western Hunan. According to local indication of spatial autocorrelation analysis, 31 countries in high-high region all showed statistically significant differences (P<0.05). RESULTS from the space-time scan showed 7 space-time clustering areas, including those most likely in the western Hunan area (2012-2014); the secondary clusters in northern Hunan areas (2011-2014).

CONCLUSIONSSignificant cluster pattern was found in the distribution of HEV in Hunan province. Clusters found in northern and western of Hunan province were seen more than in other regions.

Adult ; Aged ; China ; epidemiology ; Cities ; Cluster Analysis ; Farmers ; statistics & numerical data ; Hepatitis E ; epidemiology ; Humans ; Incidence ; Middle Aged ; Prevalence ; Seroepidemiologic Studies ; Space-Time Clustering ; Spatial Analysis

DNA chips are becoming increasingly popular as a convenient way to perform vast amounts of experiments related to genes on a single chip. And the importance of analyzing the data that is provided by such DNA chips is becoming significant. A very important analysis on DNA chip data would be clustering genes to identify gene groups which have similar properties such as cancer. Clustering data for DNA chips usually deal with a large search space and has a very fuzzy characteristic. The Particle Swarm Optimization algorithm which was recently proposed is a very good candidate to solve such problems. In this paper, we propose a clustering mechanism that is based on the Particle Swarm Optimization algorithm. Our experiments show that the PSO-based clustering algorithm developed is efficient in terms of execution time for clustering DNA chip data, and thus be used to extract valuable information such as cancer related genes from DNA chip data with high cluster accuracy and in a timely manner.
Cluster Analysis ; DNA ; Oligonucleotide Array Sequence Analysis

OBJECTIVETo compare and appraise the quality of germplasm resource of Ophiopogon japonicus in Sichuan basin.

METHODAccording to the main contents and yield traits, 24 wild germplasm resources of O. japonicus from different areas of Sichuan basin were comprehensively compared by the SPSS 17.0 software with principal component analysis and cluster analysis.

RESULTThe six samples of Ziyang, Jianyang, Leshan, Yibin, Chongqing, Mianyang, their comprehensive evaluation value of quality were higher than the others, and the sample of Ziyang had the best quality, the sample of Dazhou had the least quality, the results of the cluster analysis to raw data were also shown a similar results as principal component analysis.

CONCLUSIONThe wild resources of O. japonicus in Sichuan basin is rich, there are much differences among their quality; the method, through principal component analysis to study the comprehensive evaluation of the O. japonicus quality, is reliability and the results of cluster analysis is also support the conclusions, it could be able to provide a reference to select high O. japonicus quality resources.

Cluster Analysis ; Ophiopogon ; chemistry ; Principal Component Analysis

OBJECTIVETo explore the genetic evolutionary distance between plants by using karyotype parameters identification of medicinal plants.

METHODThe cluster analysis of karyotype resemblance-near coefficient and evolutionary distance was used for 6 Bupleurum species.

RESULTThe results showed that there were the biggest karyotype resemblance-near coefficient (0.9920) and the smallest evolutionary distance (D(e) = 0.0080) between B. scorzonerifolium and B. chinense, indicating the closest relationship, and the minimum karyotype resemblance-near coefficient (0.4794) and the maximum evolutionary distance (D(e) = 0.7352) between B. smityii and B. falcatum, indicating the most distant relationship.

CONCLUSIONKaryotype was an important parameter for identification of medicinal plants because karyotype was stabilized for species. The genetic distance between in 6 species of Bupleurum species was obtained by karyotype clustering analysis of karyotype resemblance-near coefficient. There was the bigger evolutionary distance between the species which had different chromosome number.

Bupleurum ; classification ; genetics ; Cluster Analysis ; Karyotype

A new method based on convolution kernel compensation (CKC) for decomposing multi-channel surface electromyogram (sEMG) signals is proposed in this paper. Unsupervised learning and clustering function of self-organizing map (SOM) neural network are employed in this method. An initial innervations pulse train (IPT) is firstly estimated, some time instants corresponding to the highest peaks from the initial IPT are clustered by SOM neural network. Then the final IPT can be obtained from the observations corresponding to these time instants. In this paper, the proposed method was tested on the simulated signal, the influence of signal to noise ratio (SNR), the number of groups clustered by SOM and the number of highest peaks selected from the initial pulse train on the number of reconstructed sources and the pulse accuracy were studied, and the results show that the proposed approach is effective in decomposing multi-channel sEMG signals.
Algorithms ; Cluster Analysis ; Electromyography ; Neural Networks (Computer)

One approach to apply precision agriculture to optimize crop production and environmental quality is identifying management zones. In this paper, the variables of soil electrical conductivity (EC) data, cotton yield data and normalized difference vegetation index (NDVI) data in an about 15 ha field in a coastal saline land were selected as data resources, and their spatial variabilities were firstly analyzed and spatial distribution maps constructed with geostatistics technique. Then fuzzy c-means clustering algorithm was used to define management zones, fuzzy performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimal cluster numbers. Finally one-way variance analysis was performed on 224 georeferenced soil and yield sampling points to assess how well the defined management zones reflected the soil properties and productivity level. The results reveal that the optimal number of management zones for the present study area was 3 and the defined management zones provided a better description of soil properties and yield variation. Statistical analyses indicate significant differences between the chemical properties of soil samples and crop yield in each management zone, and management zone 3 presented the highest nutrient level and potential crop productivity, whereas management zone 1 the lowest. Based on these findings, we conclude that fuzzy c-means clustering approach can be used to delineate management zones by using the given three variables in the coastal saline soils, and the defined management zones form an objective basis for targeting soil samples for nutrient analysis and development of site-specific application strategies.
Cluster Analysis ; Crops, Agricultural ; Electric Conductivity ; Soil