{"id":400,"date":"2015-08-28T07:28:41","date_gmt":"2015-08-28T07:28:41","guid":{"rendered":"http:\/\/kusuaks7\/?p=5"},"modified":"2024-09-23T08:34:53","modified_gmt":"2024-09-23T08:34:53","slug":"reinvention-bioinformatics-big-data-applications","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/reinvention-bioinformatics-big-data-applications\/","title":{"rendered":"Reinvention of Bioinformatics with Big Data Applications"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tThe main objective of this post is to acquaint a broad readership with the technical developments in the field of bioinformatics through a discussion of at least a couple of big data applications for health sciences. As some may\u00a0know, big data has made significant inroads into many subfields of bioinformatics, such as medical informatics, imaging informatics, and sensor informatics. <\/em>Lets first get the definitions of these three categories of advanced data technologies out of the way.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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  1. Medical informatics:<\/strong> This type indicates very high volume data related to patient records which may include biometrics, history of diseases, history of medicine consumption, and medical insurance information<\/li>\n \t
  2. Imaging informatics:<\/strong> This type may include data related to X-Rays, scans, or photos of other specialized tests with granular details that result in high volume, complex, and multi-format data.<\/li>\n \t
  3. Sensor informatics:<\/strong> This type of information is primarily gathered from sensor-aided medical instruments and specialized health-monitoring machines.<\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    \n\t\t\t\t\t\t\t\t\tAdditionally, the vast collection of public health records available at government health centers or hospitals also add to the volume of big data. The major goals of big-data analytics in bioinformatics are to enhance the clinical processes, provide more efficient medical care, and improve policy implementation.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    \n\t\t\t\t\t\t\t\t\tOn the patient treatment arena, big data and analytics can utilize data models to predict diseases from observed patterns, aid in drug discovery process, can conduct analytical comparisons of omics,; can use visualization tools to understand and correlate case histories, and can provide effective solutions for medical data governance and preservation with full compliance with privacy and security policies.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    \n\t\t\t\t\t\t\t\t\tOn the healthcare management side, big data analytics can be used to assess the performance of existing medical care systems or policies; to design disease prevention and healthcare literature based on behavioral data gathered from social media; to improve the operational efficiency of healthcare management programs; and to better structure health organization policies.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    \n\t\t\t\t\t\t\t\t\tHere, at least two possible applications of big data and analytics in bioinformatics are illustrated.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    \n\t\t\t\t\t\t\t\t\tBioinformatics Data Science<\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Application 1: Big data in sensor informatics<\/strong><\/h3>\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    \n\t\t\t\t\t\t\t\t\tAs the medical world progresses more towards preventive healthcare, the entire patient lifecycle beginning with technology-aided diagnostics, selection of treatment process, and disease prevention may now be found to be gathering more steam from the recent advancements in big data technologies in bioinformatics. To this end, the industry experts are working to achieve:\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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