Data science as a formal discipline is currently popular because of its tremendous commercial utility. Large companies have used several well-established computational and statistical techniques to mine high volumes of commercial and social data. The broad interest across many applications stirred the birth of data science as a field that acts as an umbrella, uniting a number of disparate disciplines using a common set of computational approaches and techniques. In some cases, these techniques were created, developed, or established in other data-driven fields (e.g., astronomy and earth science). In fact, some of these disciplines significantly predate the formal foundation of data science and have contributed to several techniques to cope with knowledge extraction from large amounts of data.
More recent biological disciplines such as macromolecular structure and genomics have inherited many of these data analytics features from genetics and other natural sciences. Genomics emerged in the 1980s at the confluence of genetics, statistics, and large-scale datasets. The tremendous advancements in nucleic acid sequencing allowed the discipline to swiftly assume one of the most prominent positions in terms of raw data scale across all the sciences. This pre-eminent role of genomics also inspired the emergence of many “-omics” terms inside and outside academia. Although today genomics is pre-eminent in terms of data scale, this may change over time due to technological developments in other areas, such as cryo-electron microscopy and personal wearable devices. Moreover, it is important to realize that many other existing data-rich areas in the biological sciences are also rapidly expanding, including image processing (including neuroimaging), macromolecular structure, health records analysis, proteomics, and the inter-relation of these large data sets, in turn, is giving rise to a new subfield termed biomedical data science.
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