RESUMEN
The goal of this vision paper is to investigate the possible role that advanced machine learning techniques, especially deep learning (DL), could play in the reintegration of various biological disciplines. To achieve this goal, a series of operational, but admittedly very simplistic, conceptualizations have been introduced: Life has been taken as a multidimensional phenomenon that inhabits three physical dimensions (time, space, and scale) and biological research as establishing connection between different points in the domain of life. Each of these points hence denotes a position in time, space, and scale at which a life phenomenon of interest takes place. Using these conceptualizations, fragmentation of biology can be seen as the result of too few and especially too short-ranged connections. Reintegrating biology could then be accomplished by establishing more, longer ranged connections. DL methods appear to be very well suited for addressing this particular need at this particular time. Notwithstanding the numerous unsubstantiated claims regarding the capabilities of AI, DL networks represent a major advance in the ability to find complex relationships inside large data sets that would have not been accessible with traditional data analytic methods or to a human observer. In addition, ongoing advances in the automation of taking measurements from phenomena on all levels of biological organization continue to increase the number of large quantitative data sets that are available. These increasingly common data sets could serve as anchor points for making long-range connections by virtue of DL. However, connections within the domain of life are likely to be structured in a highly nonuniform fashion and hence it is necessary to develop methods, for example, theoretical, computational, and experimental, to determine linkage of biological data sets most likely to provide useful insights on a biological problem using DL. Finally, specific DL approaches and architectures should be developed to match the needs of reintegrating biology.
Asunto(s)
Aprendizaje Profundo , Animales , Biología , Aprendizaje AutomáticoRESUMEN
The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast datasets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists.
Asunto(s)
Inteligencia Artificial , Aprendizaje Automático , Agricultura , Algoritmos , Animales , TecnologíaRESUMEN
Bioinspiration-using insights into the function of biological systems for the development of new engineering concepts-is already a successful and rapidly growing field. However, only a small portion of the world's biodiversity has thus far been considered as a potential source for engineering inspiration. This means that vast numbers of biological systems of potentially high value to engineering have likely gone unnoticed. Even more important, insights into form and function that reside in the evolutionary relationships across the tree of life have not yet received attention by engineers. These insights could soon become accessible through recent developments in disparate areas of research; in particular, advancements in digitization of museum specimens, methods to describe and analyze complex biological shapes, quantitative prediction of biological function from form, and analysis of large digital data sets. Taken together, these emerging capabilities should make it possible to mine the world's known biodiversity as a natural resource for knowledge relevant to engineering. This transformation of bioinspiration would be very timely in the development of engineering, because it could yield exactly the kind of insights that are needed to make technology more autonomous, adaptive, and capable of operation in complex environments.