Data Science & Analytics
Imageomics Institute
Description
The traits that characterize living organisms—in particular, their morphology, physiology, behavior and genetic make-up—enable them to cope with forces of the physical as well as the biological and social environments that impinge on them. Moreover, since function follows form, traits provide the raw material upon which natural selection operates, thus shaping evolutionary trajectories and the history of life. Interestingly, most living organisms, from microscopic microbes to charismatic megafauna, reveal themselves visually and are routinely captured in copious images taken by humans from all walks of life. The resulting massive amount of image data has the potential to further our understanding of how multifaceted traits of organisms shape the behavior of individuals, collectives, populations, and the ecological communities they live in, as well as the evolutionary trajectories of the species they comprise. Images are increasingly the currency for documenting the details of life on the planet, and yet traits of organisms, known or novel, cannot be readily extracted from them. Just like with genomic data two decades ago, our ability to collect data at the moment far outstripts our ability to extract biological insight from it. The Institute will establish a new field of IMAGEOMICS, in which biologists utilize machine learning algorithms (ML) to analyze vast stores of existing image data—especially publicly funded digital collections from national centers, field stations, museums and individual laboratories—to characterize patterns and gain novel insights on how function follows form in all areas of biology to expand our understanding of the rules of life on Earth and how it evolves.
RENCI's Role
RENCI is responsible for developing approaches for the use of bio-ontologies and other structured biological knowledge in machine-learning-based image analysis for species identification, species classification, and morphological trait extraction. This includes incorporation of ontologies into machine-learning algorithms, as well as application of ontologies to create structured output that is Findable, Accessible, Interoperable, and Reusable (FAIR).