title: Discrete derivative: a data slicing algorithm for exploration of sharing biological networks between rheumatoid arthritis and coronary heart disease.
abstract: BACKGROUND
  One important concept in traditional Chinese medicine (TCM) is "treating different diseases with the same therapy". In TCM practice, some patients with Rheumatoid Arthritis (RA) and some other patients with Coronary Heart Disease (CHD) can be treated with similar therapies. This suggests that there might be something commonly existed between RA and CHD, for example, biological networks or biological basis. As the amount of biomedical data in leading databases (i.e., PubMed, SinoMed, etc.) is growing at an exponential rate, it might be possible to get something interesting and meaningful through the techniques developed in data mining.
  
  
  RESULTS
  Based on the large data sets of Western medicine literature (PubMed) and traditional Chinese medicine literature (SinoMed), by applying data slicing algorithm in text mining, we retrieved some simple and meaningful networks. The Chinese herbs used in treatment of both RA and CHD, might affect the commonly existed networks between RA and CHD. This might support the TCM concept of treating different diseases with the same therapy.
  
  
  CONCLUSIONS
  First, the data mining results might show the positive answer that there are biological basis/networks commonly existed in both RA and CHD. Second, there are basic Chinese herbs used in the treatment of both RA and CHD. Third, these commonly existed networks might be affected by the basic Chinese herbs. Forth, discrete derivative, the data slicing algorithm is feasible in mining out useful data from literature of PubMed and SinoMed.
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimen, Beijing 100700, China.|Information Science and Engineering School, Lanzhou University, Lanzhou 730000, China.
authors.forename: Guang
authors.initials: G
authors.lastname: Zheng
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimen, Beijing 100700, China.
authors.forename: Miao
authors.initials: M
authors.lastname: Jiang
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimen, Beijing 100700, China.
authors.forename: Xiaojuan
authors.initials: X
authors.lastname: He
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimen, Beijing 100700, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhao
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimen, Beijing 100700, China.|Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
authors.forename: Hongtao
authors.initials: H
authors.lastname: Guo
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimen, Beijing 100700, China.
authors.forename: Gao
authors.initials: G
authors.lastname: Chen
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimen, Beijing 100700, China.
authors.forename: Qinglin
authors.initials: Q
authors.lastname: Zha
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimen, Beijing 100700, China.
authors.forename: Aiping
authors.initials: A
authors.lastname: Lu