Discovery science (also known as discovery-based science) is a scientific methodology which aims to find new patterns, correlations, and form hypotheses through the analysis of large-scale experimental data. The term “discovery science” encompasses various fields of study, including basic, translational, and computational science and research. Discovery-based methodologies are commonly contrasted with traditional scientific practice, the latter involving hypothesis formation before experimental data is closely examined. Discovery science involves the process of inductive reasoning or using observations to make generalisations, and can be applied to a range of science-related fields, e.g., medicine, proteomics, hydrology, psychology, and psychiatry.
Overview
Purpose
Discovery science places an emphasis on 'basic' discovery, which can fundamentally change the status quo. For example, in the early years of water resources research, the use of discovery science was demonstrated by seeking to elucidate phenomena that was, until that point, unexplained. It did not matter how unusual these ideas may have been perceived to be. In this sense, discovery science is based on the attitude that ‘‘we must not allow our concepts of the earth, in so far as they transcend the reach of observation, to root themselves so deeply and so firmly in our minds that the process of uprooting them causes mental discomfort" (as stated by Davis in 1926). For discovery science to be utilised, there is a need to revert to creating and testing genuine hypotheses, rather than focusing on praising concepts that are already familiar. While researchers commonly feel that new hypotheses will naturally emerge inductively from curiosity in the relevant field, it should be acknowledged that hypotheses can be generated by models. Additionally, deductive testing must involve field observation, so that imperfect answers can be substituted with questions that are more clearly defined.
Tools
Hypothesis-driven studies can be transformed into discovery-driven studies with the help of newly available tools and technology-driven life science research. These tools have allowed for new questions to be asked, and new paradigms to be considered, particularly in the field of biology. However, some of these required tools are limited in the sense that they are inaccessible or too costly because the related technology is still being developed.
Data mining is the most common tool used in discovery science, and is applied to data from diverse fields of study such as DNA analysis, climate modelling, nuclear reaction modelling, and others. The use of data mining in discovery science follows a general trend of increasing use of computers and computational theory in all fields of science, and newer methods of data mining employ specialised machine learning algorithms for automated hypothesis forming and automated theorem proving.
Applications
While computational methods are gaining interest, there is a decline in efforts to support critical care through basic and translational science, i.e., forms of discovery science which are essential for advancing understanding of pathophysiology. A loss of interest in basic and translational science may lead to a failure to discover and develop new therapies, which could have an impact on the critically ill. Within critical care, there is an aim to renew emphasis on basic, translational science through platforms such as medical journals and conferences, as well as the critical care medical curricula. Advances in discovery-based science thereby underlie key discoveries and development in medicine, constituting a 'pipeline' for leading-edge medical development.
Medicine
According to the AACR Cancer Progress Report 2021, discovery science has the potential to drive clinical breakthroughs. Since discovery science underlies key discoveries and development of new therapies for medicine, it remains important for advancing critical care. Numerous discoveries have increased life span and productivity, and decreased health-related costs, thereby revolutionising medical care. Resultantly, return on investment for discovery science has proven to be high. For example, its combination of computational methods with knowledge on inflammatory and genomic pathways has resulted in optimised clinical trials. Ultimately, discovery science is currently enabling a transition to the era of personalised medicine for treating complex syndromes, e.g., sepsis and ARDS. With a robust infrastructure, discovery science can resultantly revolutionise medical care and biological research.