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Nowadays, it is quite challenging to clarify Chinese medicine's results and its internal biological foundation in the traditional Chinese medicine (TCM) field. The key to break through this problem is to make the right methodological choices. From our study, a pattern consisting of many kinds of characterizations has been found, and every characterization corresponds with its internal biological indicators. If the relationship of every characterization and its biological indicators can be structured, and of each pattern and its biological indicators, it will help us to understand TCM better. Therefore, it is a better method to construct and analyze the "pattern-characterization-biological indicator" network. The aim of this paper is to distinguish two common Chinese medicine patterns, qi deficiency pattern and qi stagnation pattern, and their characterizations in terms of internal biology by using literature-mining methods. Furthermore, the results will be validated by clinical data to examine the methodological reliability. A neuro-endocrine-immune (NEI)-related gene data dictionary and a human phenotype ontology (HPO) characterizations terminology database have been established for these two patterns. Relevant literatures about the characterizations of these two patterns can be found on PubMed. Two different literature-mining software PubMiner and GenCliP, were used on the principle of "pattern-characterization-biological indicator" co-occurrence to find the characteristic NEI gene and the chemical messenger (CM) of Qi deficiency and qi stagnation patterns and to explore the difference in the bioactive substances between the two patterns, such as Hormones, Receptors, Cytokine, Neurotransmitters, etc. Biological networks of the two patterns and their various characterizations were separately constructed by using two literature-mining methods. After integrating and analyzing all kinds of networks, we found that qi deficiency pattern genes based on the NEI network include CD4, CHAT, EPO, GCG, INS, PTH, PRL, REN, SHBG and MAOA; and the key chemical transmitters include IgA, IgM, IgG, IL6, INFα, C3, C4, IL2, T, TSH, T3, T4, NO, EPO, E2, Serotonin, Histamine, ACTH, Hydrocortisone, Insulin, Cytokine, Calcitriol, Aldosterone, Adenosine, Somatostatin, Progesterone Acetylcholine, NE and Dopamine. Qi stagnation pattern genes based on the NEI network include EGF, EGFR, INS, PRL, SHBG, SNAP25, BDNF, COMT, DRD4, CD4 and IL6; and the key chemical transmitters include T3, E2, Prolactin, Serotonin, Steroids, T, ACTH, TSH, NE, ACTH and IL6. By comparing the literature data with clinical data, we found that abnormalities of the endocrine system, especially the thyroid, adrenal gland and gonadal gland, are closely related to the occurrence of coronary heart disease (CHD). The abnormalities in the endocrine system affect the immune system and nervous system, which eventually leads to CHD. The CHD of qi deficiency pattern emphasizes the imbalance of the immune system, while that of qi stagnation patients focuses on the imbalance of the nervous system. To some extent, it is feasible to use literature-mining methods to construct and analyze the "pattern-characterization-biological indicator" network as a new method of finding syndromes for the biological indicators. It provides an advanced, effective and concise method for the objectivity and internalization of traditional medicine. However, the universality and reliability of this method will need to be further validated by other syndrome studies.

Networks are powerful representation of topological features in biological systems like protein interaction and gene regulation. In order to understand the design principles of such complex networks, the concept of network motifs emerged. Network motifs are recurrent patterns with statistical significance that can be seen as basic building blocks of complex networks. Identification of network motifs leads to many important applications, such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, protein function annotation, etc. However, identification of network motifs is challenging as it involves graph isomorphism which is computationally hard. Though this problem has been studied extensively in the literature using different computational approaches, we are far from satisfactory results. Motivated by the challenges involved in this field, an efficient and scalable network Motif Discovery algorithm based on Expansion Tree (MODET) is proposed. Pattern growth approach is used in this proposed motif-centric algorithm. Each node of the expansion tree represents a non-isomorphic pattern. The embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of the parent node through vertex addition and edge addition. Further, the proposed algorithm does not involve any graph isomorphism check and the time complexities of these processes are $O(n)$ and $O(1)$, respectively. The proposed algorithm has been tested on Protein–Protein Interaction (PPI) network obtained from the MINT database. The computational efficiency of the proposed algorithm outperforms most of the existing network motif discovery algorithms.

Based on the complex network and non-equilibrium statistic theory, the birth and death equation (BDE) is proposed for the virus spreading on biological networks. This equation, in which the Susceptible Infected Susceptible model is extended, can predict the distribution rule of infection density by generating function. It is a better approach to a real infection process.