Genomics and Applied Biology 2017, Vol.8, No.5, 30-48
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break No.8 which accounts for 1% of the whole human genome. China is the 6th country following USA, UK,
France, German and Japan to have participated in Human Genome Project (Wu, 2009). During the period, DNA
chip technology was developed at the right moment to high-efficiently and quickly test and analyse a large sum of
genetic information. Post-genome era came around when human gene map was portrayed completely in 2000.
Functional genomics became the focus of genomic research. New technologies like two dimensional gel
electrophoresis and DNA chips were developed and applied. It concentrated on cognizing and analyzing the
genetic and non-genetic sequences and their functions of the whole genome in attempt to make out the
encyclopedia to help interpret the profound DNA language. In 2010, there were burst keywords like
metagenomics and comparative genomics. Metagenomics takes microorganism DNA extracted from
environmental samples as study objects to construct the metagenomic library. It screens and searches for new
physiological activators to acquire information about micro-organic genetic diversity and molecular ecology from
the environment (Huang et al., 2009). Comparative genomics mainly compares the whole genome of different
species and comprehends the functional and developmental correlation of the whole genome. In addition,
post-genome era has made proteomics more popular. Proteomics is mainly about a comprehensive study of the
properties of protein, providing theoretical basis and solution for the clarification and attack of many disease
mechanisms at the protein level. It has also brought revolution to the medical field. Keywords like
pharmacogenomics and individualized treatment have been extensively mentioned in references. In the course of
clinical treatment, it is often found that different patients have different therapeutic effects and side effects on the
same drug. Pharmacogenomics, based on gene theory, studies the relationship between the gene itself and its
mutants and its drug effects. Through the gene detection of the patients, the individualized treatment plan is
provided according to its genotype, so as to improve the efficacy and reduce the occurrence of adverse drug
reactions. In recent years, the development of the treatment on non-small cell lung cancer depends largely on the
research and application of pharmacogenomics. The direct motivation of Human Genome Project is to solve the
basic genetic problems of human diseases including cancer, and to achieve early prevention and treatment, so as to
reduce the risk of disease. In addition, in recent years, traditional Chinese medicine genomics which takes Chinese
medicine raw species as study objects has also developed rapidly. With the help of techniques like gene chips and
bio-informatics, the targets of traditional Chinese medicine and their mechanism of action can be explored to
determine the effective parts of traditional Chinese medicine, identify Chinese herbs, distinguish genuine herbs,
screen new drugs and shorten the cycle and finally open up a new path for the modernization of traditional
Chinese medicine (Xing et al., 2007) (Figure 7). In 2014, the concept of big data burst. In the same year, Google
marched towards genomics and joined Global Alliance for Genomics and Health in order to gather the resources
in this field and build databases based on big data to solve the problem of interpreting the results of complex gene
detection.
1.4 Visualized map analysis of co-citation
Visualized co-citation map was portrayed by CiteSpace to study the key references used in the field of genomics.
At present, CiteSpace is still unable to carry out the literature co-citation analysis of the literature in CNKI, so this
study only analyzes the English literature. Because of the huge amount of data cited in English literature, this
study is divided into three time periods to analyze the cited literature in the English literature-the first 5 years
(1985-2009), the second 5 years (2010-2014) and recent two years (2015 and 2016). According to the document
quantity and cluster analysis Q value, Selection Criteria is set as Top 10, Top 30, Top 100, respectively (Figure 11;
Figure 12; Figure 13). Q value is 0.7171, 0.5164 and 0.6984, respectively, which indicates the cluster structure is
apparent. The main clusters and top terms of reference co-citation network in the three periods are listed below
(Table 9; Table 10; Table 11).
Table 12 lists the top 10 literature with highest centrality. Literature with highest centrality means that papers
occupy an important position in structure, that is, they play an important role in connecting other nodes or several
different clusters. These documents can be regarded as a landmark in the field of genomics (Chen et al., 2014).
