The expertise involved in data science is common but knowledge for implementation
varies from problem domain to domain. Data science in any sector with sufficient data
shows its analytical skills for significant improvement to achieve the goal, for example,
better decision making in business, identifying better opportunities in challenging
situations, etc. Molecular and system biology are among the thrust areas where data
analytics plays a key role in the advancement of the domain.
Bioinformatics primarily deals with raw biological data generated from different
experiments at the cellular and molecular level to develop biological insights.
Out of different sub-disciplines of bioinformatics, biology and computer science are two
major areas which are supported by mathematics, statistics, physics and chemistry. Life
science is the source of the ocean of biological data that need a computational
architecture or framework to manage this amount of data and make it more readable
and accessible. The prime functionality of bioinformatics is biological data management
that includes efficient data acquisition and storing, preprocessing, data filtering and
quality checking even data transformation and integration. The next and most important
thing is the analysis of these data such as formulating biological hypotheses from
experimental data, domain-specific statistical analysis, applying machine learning for
exploring the yet to known consequences and data visualization.
Significant numbers of works have been carried out to
understand 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
advancement of biological science. However, the data generated at the cellular level are
dynamic in nature, large in size and highly dimensional. For example, a single cell of an
organism can generate sequence information that may contain 400 to 10000 genes. So
At the organism level population scale, this data transforms into big data. To process
such sensitive noisy large-scale dynamic high-dimensional data, there is a need for
developing a high-performance computational system that can find optimized and
efficiently designed analytical tools.
Bioinformatics is an interdisciplinary field that stores, manages, analyses and interprets
biological data which is fundamentally data-driven and computational. The growing
phase of life science research has become more data-driven, integrative and
computational, such that biomedical scientists need to gain proper Bioinformatics
knowledge. Acquiring at least a minimum level of computational expertise can help life
and computational scientists to pass on knowledge and interact with each other more
effectively, for the advancement of research findings.
Continuous training has become essential for current and future generation scientist of
the life science domain. The combined response from collaborators across the world is
most needed for the advancement of Bioinformatics research that eventually helps to
make a sustainable solution by transforming education programs and nurturing a replacement team of trained scientists.