Text Mining for Finding Functional Community of Related Genes Using TCM Knowledge

Wu, Zhaohui; Zhou, Xin; Liu, Baoyan; Chen, Junli

doi:10.1007/978-3-540-30116-5_42

Cited by 17 publications

(10 citation statements)

References 27 publications

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“…Wu et al [20] present a novel text mining approach to explore the gene relationships, as well as the spatial functional molecule interaction network from the MED-LINE database. The TCM literature used in their experiments contains more than 500,000 articles since 1984, and these materials are processed by an efficient bootstrapping method to extract syndrome-disease relationships.…”

Section: Related Workmentioning

confidence: 99%

KISTCM: knowledge discovery system for traditional Chinese medicine

et al. 2008

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Objective: Traditional Chinese Medicine (TCM) provides an alternative method for achieving and maintaining good health. Due to the increasing prevalence of TCM and the large volume of TCM data accumulated though thousands of years, there is an urgent need to efficiently and effectively explore this information and its hidden rules with knowledge discovery in database (KDD) techniques. This paper describes the design and development of a knowledge discovery system for TCM as well as the newly proposed KDD techniques integrated in this system. data mining techniques, primarily including a medicine dependency relationship discovery algorithm, an efficacy dimension reduction algorithm based on neural networks, a method for exploring the relationships between formulae and syndromes using gene expression programming (GEP), and an approach for discovering the properties in terms of nature, taste and meridian based on the herbal dosage by employing the effect degree function to calculate the effect of each property.Results: Representative experimental cases are used to evaluate the system performance. Encouraging results are obtained, including rules previously unknown to algorithm designers and experiment runners. Experiments demonstrate that KISTCM has powerful knowledge discovery and data analysis capabilities, and is a useful tool for discovering the underlying rules in formulae. Our proposed techniques successfully discover hidden knowledge from TCM data, which is a new direction in knowledge discovery. From TCM experts' perspective, the accuracy of data analysis for KISTCM is an improvement, and these results compare favorably to other existing TCM data mining techniques. The system could be expected to be useful in the practice of TCM, e.g., assisting TCM physicians in prescribing formulae or automatically distinguishing between minister and assistant herbs in a formula.

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Section: Related Workmentioning

confidence: 99%

KISTCM: knowledge discovery system for traditional Chinese medicine

et al. 2008

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“…Also the bibliographic TCM literature and modern biomedical literature (i.e. MEDLINE) are integrated to generate novel syndrome-gene associations [16] . These KDD methods have found some meaningful latent knowledge in the huge clinical data.…”

Section: A Data Mining In Tcmmentioning

confidence: 99%

A hierarchical symptom-herb topic model for analyzing traditional Chinese medicine clinical diabetic data

Zhang

Zhou

Huang

et al. 2010

2010 3rd International Conference on Biomedical Engineering and Informatics

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Traditional Chinese medicine (TCM) is a clinical medicine. The huge clinical data from the daily clinical process which keeps to TCM theories and principles, is the core empirical knowledge source for TCM research. Induction of the common knowledge or regularities from the large-scale clinical data is a vital task for both theoretical and clinical research of TCM. Topic model have recently shown much success for text analysis and information retrieval by extracting latent topics from text collection. In this paper, we propose a hierarchical symptomherb topic model (HSHT), to automatically extract the hierarchical latent topic structures with both symptoms and their corresponding herbs in the TCM clinical data. The HSHT model is one of the extensions of hierarchical latent Dirichlet allocation model (hLDA) and Link latent Dirichlet allocation (LinkLDA). The proposed HSHT model is used for extracting the hierarchical structure of symptoms with their corresponding herbs in clinical type 2 diabetes mellitus (T2DM). We get one meaningful supertopic with common symptoms and commonly used herbs and some meaningful subtopics denoted T2DM complications with corresponding symptoms and their commonly used herbs. The results indicate some important medical groups corresponding to the companioned diseases in the T2DM inpatients. And then the results show that there exactly exist TCM diagnosis and treatment sub-categories and the personalized therapies to T2DM. Furthermore, it manifested that the HSHT model is useful for establishing of the TCM clinical guidelines based on the TCM clinical data.

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“…Many of data mining methods have been used in the Modernization of Traditional Chinese Medicine. Text mining method is used for finding functional community of TCM Knowledge [18]. Mining compatibility rules are used for TCM databases [19].…”

Section: Relate Workmentioning

confidence: 99%

MapReduce-Based Pattern Finding Algorithm Applied in Motif Detection for Prescription Compatibility Network

Liu

Jiang

Chen

et al. 2009

Lecture Notes in Computer Science

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Abstract. Network motifs are basic building blocks in complex networks. Motif detection has recently attracted much attention as a topic to uncover structural design principles of complex networks. Pattern finding is the most computationally expensive step in the process of motif detection. In this paper, we design a pattern finding algorithm based on Google MapReduce to improve the efficiency. Performance evaluation shows our algorithm can facilitates the detection of larger motifs in large size networks and has good scalability. We apply it in the prescription network and find some commonly used prescription network motifs that provide the possibility to further discover the law of prescription compatibility.

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Text Mining for Finding Functional Community of Related Genes Using TCM Knowledge

Cited by 17 publications

References 27 publications

KISTCM: knowledge discovery system for traditional Chinese medicine

KISTCM: knowledge discovery system for traditional Chinese medicine

A hierarchical symptom-herb topic model for analyzing traditional Chinese medicine clinical diabetic data

MapReduce-Based Pattern Finding Algorithm Applied in Motif Detection for Prescription Compatibility Network

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