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This is particularly true in the in silico science of bioinformatics, where biological data can be processed by Web Services.
The need for dimension reduction, which is the process to reduce unnecessary features from the original data, arises because biological data can be massive, with tens of thousands of features to be explored.
The flexibility in TargetMine structure ensures that different types of biological data can be readily added and analysed to generate new hypotheses for further investigation.
Molecular level of biological data can be constructed into system level of data as biological networks.
The use cases given below are some examples of how various biological data can be stored in the ND module and other related Chado modules.
Combining KABP and biological data can be helpful to get information about risk and protective behaviour in individuals with different serostatus.
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They also added that irregularities in biological passport data can be caused by tiredness and ill health.
The structure of biological multidomain data can be expressed in a small data model, described in detail in Rohn et al. (2009).
A wide variety of quantitative biological dynamics data can be directly obtained from experimental measurements by using live-cell imaging and digital image processing; for example, cell division dynamics in Caenorhabditis elegans can be extracted from four-dimensional (4D) microscopic images (Bao et al., 2006; Giurumescu et al., 2012; Kyoda et al., 2013; Sarov et al., 2012).
With robust annotation or identification, biological interpretation of data can be performed for a single study and data from different studies performed in the same laboratory or different research groups can be compared.
In this study we demonstrated how pathway analysis results, which are often considered a final step in the biological interpretation of transcriptomics data, can be used and combined in a biological network to gain more insights in the interplay and relation between processes.
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