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<\/p>\nThe expertise involved in data science is common but knowledge for implementation
\nvaries from problem domain to domain. Data science in any sector with sufficient data
\nshows its analytical skills for significant improvement to achieve the goal, for example,
\nbetter decision making in business, identifying better opportunities in challenging
\nsituations, etc. Molecular and system biology are among the thrust areas where data
\nanalytics plays a key role in the advancement of the domain.
\nBioinformatics primarily deals with raw biological data generated from different
\nexperiments at the cellular and molecular level to develop biological insights.
\nOut of different sub-disciplines of bioinformatics, biology and computer science are two
\nmajor areas which are supported by mathematics, statistics, physics and chemistry. Life
\nscience is the source of the ocean of biological data that need a computational
\narchitecture or framework to manage this amount of data and make it more readable
\nand accessible. The prime functionality of bioinformatics is biological data management
\nthat includes efficient data acquisition and storing, preprocessing, data filtering and
\nquality checking even data transformation and integration. The next and most important
\nthing is the analysis of these data such as formulating biological hypotheses from
\nexperimental data, domain-specific statistical analysis, applying machine learning for
\nexploring the yet to known consequences and data visualization.<\/p>\n
<\/p>\n
Significant numbers of works have been carried out to
\nunderstand the basic functionality of the human body at the cellular level. How does the cellular component of an organism react to perturbations? What type of malfunctioning can happen due to the change occurring in the cell? How is the perturbed entity different from any healthy entity within the cell of living organisms? This resourceful information is investigated through quantitative analysis for the
\nadvancement of biological science. However, the data generated at the cellular level are
\ndynamic in nature, large in size and highly dimensional. For example, a single cell of an
\norganism can generate sequence information that may contain 400 to 10000 genes. So
\nAt the organism level population scale, this data transforms into big data. To process
\nsuch sensitive noisy large-scale dynamic high-dimensional data, there is a need for
\ndeveloping a high-performance computational system that can find optimized and
\nefficiently designed analytical tools.
\nBioinformatics is an interdisciplinary field that stores, manages, analyses and interprets
\nbiological data which is fundamentally data-driven and computational. The growing
\nphase of life science research has become more data-driven, integrative and
\ncomputational, such that biomedical scientists need to gain proper Bioinformatics
\nknowledge. Acquiring at least a minimum level of computational expertise can help life
\nand computational scientists to pass on knowledge and interact with each other more
\neffectively, for the advancement of research findings.
\nContinuous training has become essential for current and future generation scientist of
\nthe life science domain. The combined response from collaborators across the world is
\nmost needed for the advancement of Bioinformatics research that eventually helps to
\nmake a sustainable solution by transforming education programs and nurturing a replacement team of trained scientists.<\/p>\n
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