Modular characteristics and the mechanism of Chinese medicine’s treatment of gastric cancer: a data mining and pharmacology-based identification

The development of malign cells that can grow in any part of the stomach, known as gastric cancer, is one of the most common causes of death worldwide. In order to increase the survival rate in patients with this condition, it is essential to improve the decision-making process leading to a better and more efficient selection of treatment strategies. Nowadays, with the large amount of information present in hospital institutions, it is possible to use data mining algorithms to improve the healthcare delivery. Thus, this study, using the CRISP methodology, aims to predict not only the mortality associated with this disease, but also the occurrence of any complication following surgery. A set of classification models were tested and compared in order to improve the prediction accuracy. The study showed that, on one hand, the J48 algorithm using oversampling is the best technique to predict the mortality in gastric cancer patients, with an accuracy of approximately 74%. On the other hand, the rain forest algorithm using oversampling presents the best results when predicting the possible occurrence of complications among gastric cancer patients after their in-hospital stays, with an accuracy of approximately 83%.

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Gastric Cancer Data Mining with Ordered Information

Rough Sets and Current Trends in Computing - Lecture Notes in Computer Science ◽

10.1007/3-540-45813-1_62 ◽

2002 ◽

pp. 467-478 ◽

Cited By ~ 7

Author(s):

Ning Zhong ◽

Ju-Zhen Dong ◽

Y. Y. Yao ◽

Setsuo Ohsuga

Keyword(s):

Gastric Cancer ◽

Data Mining ◽

Cancer Data

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Predicting Postoperative Complications for Gastric Cancer Patients Using Data Mining

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Intelligent Technologies for Interactive Entertainment ◽

10.1007/978-3-030-16447-8_4 ◽

2019 ◽

pp. 37-46

Author(s):

Hugo Peixoto ◽

Alexandra Francisco ◽

Ana Duarte ◽

Márcia Esteves ◽

Sara Oliveira ◽

...

Keyword(s):

Gastric Cancer ◽

Data Mining ◽

Postoperative Complications ◽

Cancer Patients ◽

Using Data ◽

Gastric Cancer Patients

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Research on Application of Data Mining Methods to Diagnosing Gastric Cancer

Advances in Data Mining. Applications and Theoretical Aspects - Lecture Notes in Computer Science ◽

10.1007/978-3-642-31488-9_3 ◽

2012 ◽

pp. 24-37 ◽

Cited By ~ 4

Author(s):

Arnis Kirshners ◽

Serge Parshutin ◽

Marcis Leja

Keyword(s):

Gastric Cancer ◽

Data Mining ◽

Mining Methods ◽

Research On Application

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Analysis of the potential correlation between gastric cancer and gastrointestinal microbiota via in-silico data mining

10.21203/rs.2.17921/v1 ◽

2019 ◽

Author(s):

Xuelu Ding ◽

Yukun Zhu ◽

Zhaoyuan She ◽

Xuewen Liu ◽

Cancan Zhang ◽

...

Keyword(s):

Gastric Cancer ◽

Data Mining ◽

Gastrointestinal Microbiota ◽

Gastrointestinal Microbiome ◽

Helicobactor Pylori ◽

Significant Difference ◽

H Pylori ◽

Gastric Cancer Patients ◽

Insight Into ◽

Gwas Catalog

Abstract Background: Emerging evidence shows the gastrointestinal microbiome might play an important role in the carcinogenesis of gastric cancer. While Helicobactor pylori has been reported to be a specific risk factor of gastric cancer, it is still controversial whether significant difference of non- H. pylori microbiota exists between gastric cancer patients and healthy control.Results: In this study, we employed multiple bioinformatic databases to excavate the potential correlation between gastrointestinal microbiome and gastric cancer. The databases involved in this investigation include HMDB, STITCH, OMIM, GWAS Catalog, WebGestalt, Toppgene, GeneMANIA. In addition, the network diagrams were built by use of Cytoscape software. Notably, our results showed that 33 common genes participate in both gastrointestinal microbiome and gastric cancer. The further analysis of these common genes suggested that there was a wide array of interactions and pathways in which the correlation between gastrointestinal microbiome and gastric cancer is involved.Conclusions: Our present study gives a bioinformatic insight into possible pathways in which the gastrointestinal microbiome play roles in gastric cancer. Future efforts are necessary to be paid to elicit the exact mechanisms as well as potential therapeutic targets of gastric cancer.

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Multiple roles of THY1 in gastric cancer based on data mining

Translational Cancer Research ◽

10.21037/tcr.2020.02.51 ◽

2020 ◽

Vol 9 (4) ◽

pp. 2748-2757

Author(s):

Yun Hu ◽

Dongmei Jin ◽

Yichan Zhou ◽

Ye Cheng ◽

Hongyong Cao ◽

...

Keyword(s):

Gastric Cancer ◽

Data Mining ◽

Multiple Roles

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Analysis of the potential correlation between gastric cancer and gastrointestinal microbiota via in-silico data mining

10.21203/rs.2.19110/v1 ◽

2019 ◽

Author(s):

Xuelu Ding ◽

Yukun Zhu ◽

Zhaoyuan She ◽

Xuewen Liu ◽

Cancan Zhang ◽

...

Keyword(s):

Gastric Cancer ◽

Data Mining ◽

Gastrointestinal Microbiota ◽

Gastrointestinal Microbiome ◽

Helicobactor Pylori ◽

Significant Difference ◽

H Pylori ◽

Gastric Cancer Patients ◽

Insight Into ◽

Gwas Catalog

Abstract Background Emerging evidence shows the gastrointestinal microbiome might play an important role in the carcinogenesis of gastric cancer. While Helicobactor pylori has been reported to be a specific risk factor of gastric cancer, it is still controversial whether significant difference of non- H. pylori microbiota exists between gastric cancer patients and healthy control.Results In this study, we employed multiple bioinformatic databases to excavate the potential correlation between gastrointestinal microbiome and gastric cancer. The databases involved in this investigation include HMDB, STITCH, OMIM, GWAS Catalog, WebGestalt, Toppgene, GeneMANIA. In addition, the network diagrams were built by use of Cytoscape software. Notably, our results showed that 33 common genes participate in both gastrointestinal microbiome and gastric cancer. The further analysis of these common genes suggested that there was a wide array of interactions and pathways in which the correlation between gastrointestinal microbiome and gastric cancer is involved.Conclusions Our present study gives a bioinformatic insight into possible pathways in which the gastrointestinal microbiome play roles in gastric cancer. Future efforts are necessary to be paid to elicit the exact mechanisms as well as potential therapeutic targets of gastric cancer.

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