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Clinical data mining of phenotypic network in angina pectoris of coronary heart disease.

Chen J, Lu P, Zuo X, Shi Q, Zhao H, Luo L, Yi J, Zheng C, Yang Y, Wang W - Evid Based Complement Alternat Med (2012)

Bottom Line: Coronary heart disease (CHD) is the leading causes of morbidity and mortality in China.In this paper, we proposed four MI-based association algorithms to analyze phenotype networks of CHD, and established scale of syndromes to automatically generate the diagnosis of patients based on their phenotypes.We also compared the change of core syndromes that CHD were combined with other diseases, and presented the different phenotype spectra.

View Article: PubMed Central - PubMed

Affiliation: Beijing University of Chinese Medicine, 11 Bei San Huan Dong Lu, ChaoYang District, Beijing 100029, China.

ABSTRACT
Coronary heart disease (CHD) is the leading causes of morbidity and mortality in China. The diagnosis of CHD in Traditional Chinese Medicine (TCM) was mainly based on experience in the past. In this paper, we proposed four MI-based association algorithms to analyze phenotype networks of CHD, and established scale of syndromes to automatically generate the diagnosis of patients based on their phenotypes. We also compared the change of core syndromes that CHD were combined with other diseases, and presented the different phenotype spectra.

No MeSH data available.


Related in: MedlinePlus

The phenotype networks for AP built by the four MI-based algorithms.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig2: The phenotype networks for AP built by the four MI-based algorithms.

Mentions: 107 phenotypes were observed and collected from clinical data under the strict quality control. In this process, there was no intervention of subjective factors. It was objective descriptions of patients' symptoms. Mutual information (MI) from complex system was used to describe association between phenotypes. The association data was consolidated into adjacency matrix and then converted into the format that Pajek software required. Pajek software 2.0 was used to analyze the node degrees of the phenotypes. With the command of “Layout-Energy-Kamada-Kawai-Separate Components,” we drew the phenotype networks according to different colors and different degrees. The principles of network adjustment were delete the isolated nodes, mediate positions of other nodes with manual operation. Nodes and edges of the network could not be deleted. Then, we exported the network figures in Bitmap format. In Figure 2, the phenotypes networks were made up of the centre network (red colors) and the surrounding networks with different colors. In Figures 2(a)to 2(d), networks with the same colors reflected the same syndromes. For example, a combination of eyestrain, tinnitus, night sweat, dry mouth, bitter taste in the mouse, and burning sensation of five centres means Yin deficiency according to TCM theory (Figure 2(a)). By using this clue, the four networks involved seven syndromes, that is, Qi deficiency syndrome, Yin deficiency syndrome, Yang deficiency syndrome, Spleen deficiency syndrome, Blood stasis syndrome, Tan-Zhuo syndrome, Qi stagnation syndrome. What is more, there were two other cases needed to be explained. Firstly, the numbers of nodes that reflected “heart syndrome” were small, and these nodes were not in the presence of all the phenotypes networks. So the heat syndrome was not classified as the main syndromes. Secondly, emaciation and insomnia were not the specific responses of syndromes in clinical process. There two phenotypes may appear in patients with different syndromes. We therefore denoted them with another color. In order to express more clearly, we had already added the legend in the revised paper.


Clinical data mining of phenotypic network in angina pectoris of coronary heart disease.

Chen J, Lu P, Zuo X, Shi Q, Zhao H, Luo L, Yi J, Zheng C, Yang Y, Wang W - Evid Based Complement Alternat Med (2012)

The phenotype networks for AP built by the four MI-based algorithms.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3378973&req=5

fig2: The phenotype networks for AP built by the four MI-based algorithms.
Mentions: 107 phenotypes were observed and collected from clinical data under the strict quality control. In this process, there was no intervention of subjective factors. It was objective descriptions of patients' symptoms. Mutual information (MI) from complex system was used to describe association between phenotypes. The association data was consolidated into adjacency matrix and then converted into the format that Pajek software required. Pajek software 2.0 was used to analyze the node degrees of the phenotypes. With the command of “Layout-Energy-Kamada-Kawai-Separate Components,” we drew the phenotype networks according to different colors and different degrees. The principles of network adjustment were delete the isolated nodes, mediate positions of other nodes with manual operation. Nodes and edges of the network could not be deleted. Then, we exported the network figures in Bitmap format. In Figure 2, the phenotypes networks were made up of the centre network (red colors) and the surrounding networks with different colors. In Figures 2(a)to 2(d), networks with the same colors reflected the same syndromes. For example, a combination of eyestrain, tinnitus, night sweat, dry mouth, bitter taste in the mouse, and burning sensation of five centres means Yin deficiency according to TCM theory (Figure 2(a)). By using this clue, the four networks involved seven syndromes, that is, Qi deficiency syndrome, Yin deficiency syndrome, Yang deficiency syndrome, Spleen deficiency syndrome, Blood stasis syndrome, Tan-Zhuo syndrome, Qi stagnation syndrome. What is more, there were two other cases needed to be explained. Firstly, the numbers of nodes that reflected “heart syndrome” were small, and these nodes were not in the presence of all the phenotypes networks. So the heat syndrome was not classified as the main syndromes. Secondly, emaciation and insomnia were not the specific responses of syndromes in clinical process. There two phenotypes may appear in patients with different syndromes. We therefore denoted them with another color. In order to express more clearly, we had already added the legend in the revised paper.

Bottom Line: Coronary heart disease (CHD) is the leading causes of morbidity and mortality in China.In this paper, we proposed four MI-based association algorithms to analyze phenotype networks of CHD, and established scale of syndromes to automatically generate the diagnosis of patients based on their phenotypes.We also compared the change of core syndromes that CHD were combined with other diseases, and presented the different phenotype spectra.

View Article: PubMed Central - PubMed

Affiliation: Beijing University of Chinese Medicine, 11 Bei San Huan Dong Lu, ChaoYang District, Beijing 100029, China.

ABSTRACT
Coronary heart disease (CHD) is the leading causes of morbidity and mortality in China. The diagnosis of CHD in Traditional Chinese Medicine (TCM) was mainly based on experience in the past. In this paper, we proposed four MI-based association algorithms to analyze phenotype networks of CHD, and established scale of syndromes to automatically generate the diagnosis of patients based on their phenotypes. We also compared the change of core syndromes that CHD were combined with other diseases, and presented the different phenotype spectra.

No MeSH data available.


Related in: MedlinePlus