Biomarker interaction selection and disease detection based on multivariate gain ratio.

BACKGROUND: Disease detection is an important aspect of biotherapy. With the development of biotechnology and computer technology, there are many methods to detect disease based on single biomarker. However, biomarker does not influence disease alone in some cases. It's the interaction between biomarkers that determines disease status. The existing influence measure I-score is used to evaluate the importance of interaction in determining disease status, but there is a deviation about the number of variables in interaction when applying I-score. To solve the problem, we propose a new influence measure Multivariate Gain Ratio (MGR) based on Gain Ratio (GR) of single-variate, which provides us with multivariate combination called interaction. RESULTS: We propose a preprocessing verification algorithm based on partial predictor variables to select an appropriate preprocessing method. In this paper, an algorithm for selecting key interactions of biomarkers and applying key interactions to construct a disease detection model is provided. MGR is more credible than I-score in the case of interaction containing small number of variables. Our method behaves better with average accuracy [Formula: see text] than I-score of [Formula: see text] in Breast Cancer Wisconsin (Diagnostic) Dataset. Compared to the classification results [Formula: see text] based on all predictor variables, MGR identifies the true main biomarkers and realizes the dimension reduction. In Leukemia Dataset, the experiment results show the effectiveness of MGR with the accuracy of [Formula: see text] compared to I-score with accuracy [Formula: see text]. The results can be explained by the nature of MGR and I-score mentioned above because every key interaction contains a small number of variables in Leukemia Dataset. CONCLUSIONS: MGR is effective for selecting important biomarkers and biomarker interactions even in high-dimension feature space in which the interaction could contain more than two biomarkers. The prediction ability of interactions selected by MGR is better than I-score in the case of interaction containing small number of variables. MGR is generally applicable to various types of biomarker datasets including cell nuclei, gene, SNPs and protein datasets.

[Approaches for developing a system model for health evaluation based on body constitutions of traditional Chinese medicine and order parameters].

From the point of view of systems science, human body can be considered as a complex system, and the human health system is a subsystem of it. Systems science conducts investigation in a holistic manner. As a theoretical method, it deals with the operation and evolution of systems from the macroscopic perspective, so this theory is similar to phenomenological theory of traditional Chinese medicine (TCM) in methodology. Naturally, numerous theories of systems science can be used in research of the human health systems of TCM. In this paper, the authors introduced synergetic, a theory of modern systems science, and its slaving principle, and in particular, analyzed the concept of order parameters related to the slaving principle and the relationship between body constitutions of TCM and order parameters. The body constitution of TCM can be treated as a slow variable in the human health systems. By using synergetic, the authors established a model of the human health system based on body constitutions of TCM. As an application of the model, the authors illustrated the argumentation in the theory of constitution being separable, the theory of a relationship between constitution and disease, and the theory of a recuperable constitution. To some extent, this work has made links between the TCM theory of body constitution and modern systems science, and it will offer a new thought for modeling the human health system.