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Lead Section

Discovery science (or 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 involves hypothesis formation before experimental data is closely examined. Discovery science involves the process of inductive reasoning or using observations to make generalisations.

Discovery science can be applied to a range of science-related fields, for example, cancer medicine, proteomics, psychology, and psychiatry.

Overview

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 the required tools are limited in the sense that they are inaccessible or too costly because the related technology is still being developed.

A common tool used in discovery science is data mining, applied to data in a range of fields of study. In discovery science, data mining follows the general trend of computational theory and computer use being increased in the fields of science. Specialised machine learning algorithms are employed in newer methods of data mining, allowing for automated hypothesis formation as well as automated theorem proving.

Applications

According to the AACR Cancer Progress Report 2021, discovery science has the potential to drive clinical breakthroughs. Discovery science has converged with clinical medicine and cancer genomics, and this convergence has been accelerated by recent advances in genome technologies and genomic information.

An example of discovery science being enhanced for human brain function can be seen in the 1000 Functional Connectomes Project (FCP) ( http://fcon_1000.projects.nitrc.org/ ). This project was launched in 2009 as a way of generating and collecting functional magnetic resonance imaging (fMRI) data from over 1,000 individuals.

Discovery science underlies key discoveries and development of new therapies for medicine, therefore proving important for advancing critical care. This includes numerous discoveries which 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. A robust discovery science infrastructure allows for the possibility of biological research, in addition to a therapeutic approach for genetic disorders.

Another example of discovery science is proteomics, a technology-driven and technology limited discovery science. Technologies for proteomic analysis provide information that is useful in discovery science. Proteome analysis as a discovery science is applicable in biotechnology, e.g., 1) it allows for the discovery of biochemical pathways which can identify targets for therapies, and 2) assists in developing new processes for manufacturing biological materials.

Methodology

Discovery science is usually a complex process, and consequently does not follow a simple linear cause and effect pattern. This means that outcomes are uncertain, and it is expected to have disappointing results as a fundamental part of discovery science. In particular, this may apply to medicine for the critically ill, where disease syndromes may be complex and multi-factorial.

In psychiatry, studying complex relationships between brain and behaviour requires a large-scale science. This calls for a need to conceptually switch from hypothesis-driven studies to hypothesis-generating research which is discovery-based. Normally, discovery-based approaches for research are initially hypothesis-free, however, hypothesis testing can be elevated to a new level that effectively supports traditional hypothesis-driven studies.

Researchers hope that combining integrative analyses of data from a range of different levels can result in new classification approaches to enable personalised interventions. Some biologists, such as Leroy Hood, have suggested that the model of ‘discovery science’ is a model which certain research fields are heading towards. For example, it is believed that we can discover more information about gene function through the evolution of data-mining tools.

Discovery-based approaches are often referred to as “big data” approaches, because of the large-scale datasets that they involve analyses of. Big data includes large-scale homogenous study designs and highly variant datasets, and can be further divided into different kinds of data sets. For example, in neuropsychiatric studies, big data can be categorised as ‘broad’ or ‘deep’ data. Broad data is complex and heterogenous, as it is collected from multiple sources (e.g., labs and institutions) and uses different kinds of standards. On the other hand, deep data is collected at multiple levels, e.g., from genes to molecules, cells, circuits, behaviours, and symptoms. Broad data allows for population level inferences to be made; deep data is required for personalised medicine. However, combining broad and deep data and storing them in large-scale databases makes it practically impossible to rely on traditional statistical approaches. Instead, the use of discovery-based big data approaches can allow for the generation of hypotheses and offer an analytical tool with high-throughput for pattern recognition and data mining. It is in this way that discovery-based approaches can provide insight into causes and mechanisms of the area of study.

Although discovery-based and data-driven big data approaches can inform understanding of mechanisms behind the topic of concern, the success of these approaches depends on the integrated analyses of the various types of relevant data, and the resultant insight provided. For example, when researching psychiatric dysfunction, it is important to integrate vast and complex data such as brain imagining, genomic data and behavioural data to uncover any brain-behaviour connections that are relevant to psychiatric dysfunction. Therefore, there are challenges to integrating data and developing mining tools. Furthermore, validation of results is a big challenge for discovery-based science. Although it is possible for results to be statistically validated by independent datasets, tests of functionality affect ultimate validation. Collaborative efforts are therefore critical for success.