Big Data: Challenge and Opportunity for Translational and Industrial Research in Healthcare

Abstract

Research and innovation are constant imperatives for the healthcare sector: medicine, biology and biotechnology support it, and more recently computational and data-driven disciplines gained relevance to handle the massive amount of data this sector is and will be generating. To be effective in translational and healthcare industrial research, big data in the life science domain need to be organized, well annotated, catalogued, correlated and integrated: the biggest the data silos at hand, the stronger the need for organization and tidiness. The degree of such organization marks the transition from data to knowledge for strategic decision making. Medicine is supported by observations and data and for certain aspects medicine is becoming a data science supported by clinicians. While medicine defines itself as personalized, quantified (precision med) or in high-definition, clinicians should be prepared to deal with a world in which Internet of People paraphrases the Internet of Things paradigm. Integrated use of electronic health records (EHRs) and quantitative data (both clinical and molecular) is a key process to develop precision medicine. Health records collection was originally designed for patient care and billing and/or insurance purposes. The digitization of health records facilitates and opens up new possibilities for science and research and they should be now collected and managed with this aim in mind. More data and the ability to efficiently handle them is a significant advantage not only for clinicians and life science researchers, but for drugs producers too. In an industrial sector spending increasing efforts on drug repurposing, attention to efficient methods to unwind the intricacies of the hugely complex reality of human physiology, such as network based methods and physical chemistry computational methods, became of paramount importance. Finally, the main pillars of industrial R&D processes for vaccines, include initial discovery, early-late pre clinics, pre-industrialization, clinical phases and finally registration-commercialization. The passage from one step to another is regulated by stringent pass/fail criteria. Bottlenecks of the R&D process are often represented by animal and human studies, which could be rationalized by surrogate in vitro assays as well as by predictive molecular and cellular signatures and models.

Assessment of health-care research and its challenges among medical doctors in Nigeria

“…10 Therefore, adopting electronic record systems is the key to effective data storage, retrieval, and advanced healthcare research in Nigeria. 11 …”

Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease

“…Author details 1 Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, 153-8904, Tokyo, Japan. 2 The Systems Biology Institute, Saisei Ikedayama Bldg. 5-10-25 Higashi Gotanda Shinagawa, 141-0022, Tokyo, Japan.…”

“…Biomedical research, especially for the field of drug discovery, is currently experiencing a global paradigm shift with artificial intelligence (AI) technologies and their application to "Big Data" in the biomedical domain [1][2][3]. The complex, non-linear, multi-dimensional nature of big prioritize candidate targets and repositionable drugs for candidate targets) from big data volumes.…”

EWASex: an efficient R-package to predict sex in epigenome-wide association studies

“…More and more researchers have begun using high-throughput chips or arrays for analyzing biological materials. This has led to an exponential increase in genomic studies (Rossi and Grifantini, 2018) with datasets from large scale cohorts stored in publicly available repositories such as the Gene Expression Omnibus (GEO) (Barrett et al, 2012), which are usually bundled with annotations such as gender. Epigenome-wide association study (EWAS) analyzing DNA methylation (DNAm) is a well-accepted approach (Rakyan et al, 2011;Lappalainen and Greally, 2017) and popular for studying epigenetics in disease etiology (Greenberg and Bourc'his, 2019).…”