The 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 know, big data has made significant inroads into many subfields of bioinformatics, such as medical informatics, imaging informatics, and sensor informatics. Lets first get the definitions of these three categories of advanced data technologies out of the way.
- Medical informatics: 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
- Imaging informatics: 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.
- Sensor informatics: This type of information is primarily gathered from sensor-aided medical instruments and specialized health-monitoring machines.
Additionally, 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.
On 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.
On 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.
Here, at least two possible applications of big data and analytics in bioinformatics are illustrated.
Bioinformatics Data Science
Application 1: Big data in sensor informatics
As 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:
- Innovative sensor designs with careful consideration of aesthetics, ergonomics, and usability issues
- Designing self-tracking devices for measuring the physical, metabolic, and emotional activities of the human body
- Sensor data analytics including data mining, data fusion, behavior profiling, data visualization and user feedback.
- Self tracking of social and psychological impacts of health on community
- Security and privacy policies that provide control and validation of personal wellbeing data
Application 2 Big data analytics in clinical environment
This application relates to use of big data in technologies in biomedical research and clinical environments. The tremendous explosion of biological has led to big data now being utilized by bioinformatics tools for gaining critical insights about medical research and path-breaking discoveries in clinical research. Path-breaking knowledge that was hitherto unknown has now become available because of advanced technologies. After the data discovery process, comes the extraction and management of data to correlate it to existing biomedical knowledge and use it in clinical environments. The goal set for this data discovery and integration process is nothing short of transforming knowledge into rare insights for aiding diagnosis, prognosis and treatment of diseases.
The various studies now pursued in clinical environments include mining of clinical data, integration of biological and clinical data, diagnostic and prognostic decision support systems based on multi-scale biological models, and moving bioinformatics application from the bench to the bedside.
Big Data and Personalized Medicine
You can find a lot of useful articles on the use of big data in bioinformatics in
IEEE Journal of Biomedical and Health Informatics http://jbhi.embs.org/special-issues/