Big data: the next frontier for innovation in therapeutics and healthcare

By N. Issa, S. Byers, S. Dakshanamurthy. 2014

169 citations recorded by the source corpus.

Advancements in genomics and personalized medicine not only effect healthcare delivery from patient and provider standpoints, but also reshape biomedical discovery. We are in the era of the ‘-omics’, wherein an individual’s genome, transcriptome, proteome and metabolome can be scrutinized to the finest resolution to paint a personalized biochemical fingerprint that enables tailored treatments, prognoses, risk factors, etc. Digitization of this information parlays into ‘big data’ informatics-driven evidence-based medical practice. While individualized patient management is a key beneficiary of next-generation medical informatics, this data also harbors a wealth of novel therapeutic discoveries waiting to be uncovered. ‘Big data’ informatics allows for networks-driven systems pharmacodynamics whereby drug information can be coupled to cellular- and organ-level physiology for determining whole-body outcomes. Patient ‘-omics’ data can be integrated for ontology-based data-mining for the discovery of new biological associations and drug targets. Here we highlight the potential of ‘big data’ informatics for clinical pharmacology.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Research Priorities for Robust and Beneficial Artificial Intelligence (2015, neutral): “, interact with the right to privacy? How will privacy risks interact with cybersecurity and cyberwarfare [23]? Our ability to take full advantage of the synergy between AI and big data will depend in part on our ability to manage and preserve privacy [24, 25].”
Data colonialism through accumulation by dispossession: New metaphors for daily data (2015, unknown): “The teleological nature of this understanding is seen most clearly in the common metaphor of ‘big data’ – and the ‘digital’ in general – as new frontiers to be explored, expanded, and conquered (Manyika et al., 2011; Rheingold, 1993).”
Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0 (2020, background): “personalized healthcare, both at individual and population level [100, 97, 101].”
Challenges and Opportunities of Big Data in Health Care: A Systematic Review (2016, background): “Open-source technology increases accessibility to and transparency of the data [12,25,26,30,35].”
Learning analytics: drivers, developments and challenges (2012, unknown): “Society is faced with the growing challenge posed by ‘big data’, ‘datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyse’ (Manyika et al., 2011, p.1).”
The role of Information and Communication Technologies in healthcare: taxonomies, perspectives, and challenges (2018, methodology): “The concept of personalization and user centrality is further emphasized when applied to P4 Medicine [259, 206, 129, 127, 138], where the main data source considered is the genetic information of each individual.”
The rise of "big data" on cloud computing: Review and open research issues (2015, unknown): “For instance, [7] defined big data as “the amount of data just beyond technology's capability to store, manage, and process efficiently.”
Exploring the influence of big data on city transport operations: a Markovian approach (2017, methodology): “The use of Geographical Information System (GIS) as a source of big data is also examined by McKinsey Global Institute (James et al., 2011) who also indicates sensors, GPS and social media as new data sources.”