Bioinformatics: A Revolution In Human Healthcare





Health, 04 Feb - 2018 ,

Bioinformatics:  A Revolution In Human Healthcare
Credit: molcare.org/bioinformatics

Healthcare plus Bioinformatics together is called Healthcare informatics or medical informatics or biomedical informatics or nursing informatics. It is a discipline at the intersection

Healthcare plus Bioinformatics together is called Healthcare informatics or medical informatics or biomedical informatics or nursing informatics. It is a discipline at the intersection of biological science, information science, computer science, and health care. Nowadays these are collectively called Translational Bioinformatics. Translational bioinformatics is defined by American Medical Informatics Association (AMIA) as ―”the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data into proactive, predictive, preventative, and participatory health.”

The completion of Human Genome Project in 2003 brought together biologists, statisticians, and computer scientists, collectively known as bioinformatician, to analyze the genomic data and to further pharmaco genetic research. Because of this collaboration, researchers began to better understand the genes associated with certain diseases and discover their complex biological processes. The interdisciplinary area of bioinformatics, which involves managing, analyzing, and interpreting information from biological sequences and structures, has opened doors for sophisticated technology that continues to support the automation and miniaturization of modern instruments that bear large-scale biomedical data.      

CHALLENGES

Ethical challenges:

In recent years, discrimination based on genetic information has become a major concern for patients and physicians. The privacy of genetic data is more sensitive than the privacy of clinical data, as it involves discrimination not only to the individual but also to relatives who have not been genetically tested. Further, predictions about disease conditions or medical risks, including the risk of genetic discrimination, can be made for an individual who has been genetically tested.

 A National Library of Medicine and book states that genetic testing can cost from $100 to $2,000 or more depending upon the nature and scope of the test. Given the likelihood that genetic tests will become more affordable in the future, there is an increased chance that more people will obtain their genetic profile, and the issues related to the privacy of genetic data cannot be ignored.

 Modern courts have recognized the sensitive nature of genetic information, and their recent decisions reflect a perceived need for additional protection of this type of information. Unauthorized access to medical records has been classified as unlawful invasion of privacy.”Genetic Information Nondiscrimination Act 2008” (GINA) is an Act of Congress in the United States designed to prohibit the improper use of genetic information in health insurance and employment.

Computational Challenges Associated with Genetic Data:

The main objective of bioinformatics is to increase the understanding of complex biological processes. The advancement in technology has eventually led to exponential growth in the DNA and protein sequence databases. The three major publicly available databases that serve as central repositories for DNA and protein data are Gen Bank, maintained by the National Center for Biotechnology Information (NCBI), DNA Databank of Japan, and European Molecular Biology Laboratory (EMBL), maintained by European Bioinformatics Institute. Each year, the number of sequences submitted to these databases grows. The size of Gen Bank doubled roughly every 18 months this causes the heap of data causing data rich information poor condition Due to the enormous amount of data produced, the extraction of biologically significant knowledge from the genes and their protein counterparts poses a growing computational challenge. Hence, specialized dimensionality reduction methods are needed to reduce the number of attributes (genes) and, consequently, to avoid the infamous―curse of dimensionality. Therefore there should be sophisticated data mining tools leading generation of useful information from the raw data.


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