Systems Biology (Methods in Molecular Biology)

and produce analytical results. However, although extremely

important, the Big Data Analytics function is only one of the

functions that can be achieved with the engineering techniques

involved in the Big Data area. Even more important are data

mining, data assimilation, and machine learning techniques;

Machine learning techniques are used for data analysis and pattern

recognition and thus they can play a key role in the development of

data assimilation procedure (Fig.6), data mining applications, and,

in the near future, artificial intelligence.

Data analysis can be performed after acquisition of biological

data. Correlations between different biological factors, clinical and

non-clinical data and molecular analysis variants can be performed,

giving, i.e., an estimation of the probability of developing a pathol-

ogy, given the presence of each one of the risk factors; a predictive

model can be constructed during the follow-up time to monitor

the disease’s onset.

3 Notes

1. Here, we will look at some image analysis tools to analyze and
   visualize our imaging data. Some open source software includes
   ImageJ, Fiji, Meta Morph, Amira, Imaris; these softwares are
   able to offer microscopy data analysis with direct linking to
   imaging instrumentations.
   The open source software is helpful for the image analysis
   and image informatics workflow to visualize the data in an
   easier way such as: Bioimage XD [22], Icy, Fiji [23], Cell
   profiler.
   There are image database in which the public repositories are
   available for the data associated with the number of articles
   already published. These include as follows:

model

data

assimilation

forecast

(background)

state update

(analysis)

observations

Fig. 6Schematic illustration of the data assimilation

Imaging and Systems Biology 353