scholarly journals Research on Classification of Primary Liver Cancer Syndrome Based on Data Mining Technology

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Jiwei Fang ◽  
Jianfeng Li
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Liver Cancer ◽  
Primary Liver Cancer ◽  
Hierarchical Cluster ◽  
Cancer Syndrome ◽  
Mining Technology ◽  
Computer Data ◽  
Cancer Syndromes

This study is based on the analysis of the status quo of the research on liver cancer syndromes, starting with the clinical objective and true four-diagnosis information of TCM inpatients with primary liver cancer, using computer data mining technology to analyze and summarize the syndrome rules from the bottom to the top. Let the data itself show the essence of liver cancer syndrome. First, with the help of hierarchical cluster analysis, we can understand the general characteristics through the rough preliminary classification of the four-diagnosis information of liver cancer patients. Then, with the help of the emerging and mature hidden structure model analysis in recent years, through data modeling, the classification of common syndromes of liver cancer and the corresponding relationship with the four-diagnosis information are comprehensively analyzed. Finally, considering the inherent shortcomings of implicit structure and hierarchical clustering based on the assumption that there is a unique one-to-one correspondence between the four diagnostic information factors and the class (or hidden class) when classifying, we plan to use factor analysis and joint cluster analysis, as supplementary means to further explore the classification of liver cancer syndromes and the corresponding relationship with the four-diagnosis information.

scholarly journals Structured Laser Illumination Planar Imaging Based Classification of Ground Coffee Using Multivariate Chemometric Analysis

2016 ◽  
Vol 8 (3) ◽  
pp. 32 ◽  
Author(s):  
Olivier K. Bagui ◽  
Kenneth A. Kaduki ◽  
Edouard Berrocal ◽  
Jeremie T. Zoueu
Keyword(s):  
Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Planar Imaging ◽  
Reliable Measurement ◽  
Laser Illumination ◽  
Coffee Beans ◽  
Chemometric Techniques ◽  
Ground Coffee

<p class="1Body">Most commercially available ground coffees are processed from Robusta or Arabica coffee beans. In this work, we report on the potential of Structured Laser Illumination Planar Imaging (SLIPI) technique for the classification of five types of Robusta and Arabica commercial ground coffee samples (Familial, Belier, Brazil, Colombia and Malaga). This classification is made, here, from the measurement of the extinction coefficient µ<sub>e</sub> and of the optical depth OD by means of SLIPI. The proposed technique offers the advantage of eliminating the light intensity from photons which have been multiply scattered in the coffee solution, leading to an accurate and reliable measurement of µ<sub>e</sub>. Data analysis uses the chemometric techniques of Principal Component Anaysis (PCA) for variable selection and Hierarchical Cluster Analysis (HCA) for classification. The chemometric model demonstrates the potential of this approach for practical assessment of coffee grades by correctly classifying the coffee samples according to their species.</p>

IHPBA concordant classification of primary liver cancer: working group report

2003 ◽  
Vol 10 (1) ◽  
pp. 26-30 ◽  
Author(s):  
Masatoshi Makuuchi ◽  
Jacques Belghiti ◽  
Giulio Belli ◽  
Sheung-Tat Fan ◽  
Joseph Wan Yee Lau ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Working Group ◽  
Primary Liver Cancer ◽  
Group Report

scholarly journals Systematic Elucidation of the Potential Mechanisms of Core Chinese Materia Medicas in Treating Liver Cancer Based on Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhulin Wu ◽  
Lina Yang ◽  
Li He ◽  
Lianan Wang ◽  
Lisheng Peng
Keyword(s):  
Data Mining ◽  
Liver Cancer ◽  
Signaling Pathways ◽  
Target Genes ◽  
Primary Liver Cancer ◽  
Network Pharmacology ◽  
Data Mining Technique ◽  
Antitumor Effects ◽  
Bioactive Ingredients ◽  
Common Target

Objective. In this study, the data mining method was used to screen the core Chinese materia medicas (CCMMs) against primary liver cancer (PLC), and the potential mechanisms of CCMMs in treating PLC were analyzed based on network pharmacology. Methods. Traditional Chinese medicine (TCM) prescriptions for treating PLC were obtained from a famous TCM doctor in Shenzhen, China. According to the data mining technique, the TCM Inheritance Support System (TCMISS) was applied to excavate the CCMMs in the prescriptions. Then, bioactive ingredients and corresponding targets of CCMMs were collected using three different TCM online databases, and target genes of PLC were obtained from GeneCards and OMIM. Afterwards, common targets of CCMMs and PLC were screened. Furthermore, a network of CCMMs bioactive ingredients and common target gene was constructed by Cytoscape 3.7.1, and gene ontology (GO) and signaling pathways analyses were performed to explain the mechanism of CCMMs in treating PLC. Besides, protein-protein interaction (PPI) analysis was used to identify key target genes of CCMMs, and the prognostic value of key target genes was verified using survival analysis. Results. A total of 15 high-frequency Chinese materia medica combinations were found, and CCMMs (including Paeoniae Radix Alba, Radix Bupleuri, Macrocephalae Rhizoma, Coicis Semen, Poria, and Curcumae Radix) were identified by TCMISS. A total of 40 bioactive ingredients (e.g., quercetin, kaempferol, and naringenin) of CCMMs were obtained, and 202 common target genes of CCMMs and PLC were screened. GO analysis indicated that biological processes of CCMMs were mainly involved in response to drug, response to ethanol, etc. Pathway analysis demonstrated that CCMMs exerted its antitumor effects by acting on multiple signaling pathways, including PI3K-Akt, TNF, and MAPK pathways. Also, some key target genes of CCMMs were determined by PPI analysis, and four genes (MAPK3, VEGFA, EGF, and EGFR) were found to be correlated with survival in PLC patients. Conclusion. Based on data mining and network pharmacology methods, our results showed that the therapeutic effect of CCMMs on PLC may be realized by acting on multitargets and multipathways related to the occurrence and development of PLC.

Application of Data Mining Technology Based on Apriori Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 2036-2039
Author(s):  
Jian Xing Chen
Keyword(s):  
Data Mining ◽  
Computer Simulation ◽  
Association Rule ◽  
Computer Time ◽  
Data Mining Algorithm ◽  
Apriori Algorithm ◽  
Mining Technology ◽  
Computer Data ◽  
Mining Algorithm ◽  
Step Distance

With the continuous expansion of computer simulation scale, the demand for data mining algorithm is also more and more big. The difficulties in computer data mining technology are focused on algorithm development. Apriori algorithm is a kind of computer data mining algorithm which can greatly improve the computational efficiency. The algorithm uses association rule, which can avoid repeated frequently by layer scanning, reducing the computer time. This paper uses Apriori algorithm to design the data mining parameter optimization model of computer 3D human biology simulation, and applies to improve the step three jump. Through the simulation we found step distance appropriate, it provides technical reference for the application of computer simulation technology in sports.

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