Managing, Analysing, and Integrating Big Data in Medical Bioinformatics: Open Problems and Future Perspectives

Abstract

The explosion of the data both in the biomedical research and in the healthcare systems demands urgent solutions. In particular, the research in omics sciences is moving from a hypothesis-driven to a data-driven approach. Healthcare is additionally always asking for a tighter integration with biomedical data in order to promote personalized medicine and to provide better treatments. Efficient analysis and interpretation of Big Data opens new avenues to explore molecular biology, new questions to ask about physiological and pathological states, and new ways to answer these open issues. Such analyses lead to better understanding of diseases and development of better and personalized diagnostics and therapeutics. However, such progresses are directly related to the availability of new solutions to deal with this huge amount of information. New paradigms are needed to store and access data, for its annotation and integration and finally for inferring knowledge and making it available to researchers. Bioinformatics can be viewed as the “glue” for all these processes. A clear awareness of present high performance computing (HPC) solutions in bioinformatics, Big Data analysis paradigms for computational biology, and the issues that are still open in the biomedical and healthcare fields represent the starting point to win this challenge.

From systems biology to P4 medicine: applications in respiratory medicine

“…The development of new experimental tools (e.g. high-throughput next-generation sequencing, mass spectrometry-based flow cytometry or real-time molecular imaging) will generate new information but, at the same time, massive amounts of (big) data that will have to be adequately handled, analysed and interpreted [112][113][114]. In this context, RIEKEBERG and POWERS [115] recently reviewed the methodological advancements and successful applications of metabolomics, one the newest omic fields.…”

Artificial Intelligence in Biological Data

“…Medical data is now facing serious setbacks as we still have not been able to come up with statistical methods capable of dealing with noisy and missing data [51][52][53]. Due to this reason the results drawn out of an AI experiment on medical data still faces uncertainty and errors [45,[54][55][56]. Growing trend in the web world has come up with a trending new system called the 'Internet of things' (IoT) wherein several devices are interconnected and keep sharing useful sensory data and commands among them helping devices understand and respond to the external environment.…”

Confluences among Big Data, Finite Element Analysis and High Performance Computing

“…It can be a suitable solution for Big Data analysis. In GPU computing, GPUs deliver extremely high floating-point performance and massively parallelism at a very low cost (Merelli et al, 2014).…”