Extracting biologically meaningful features from time-series measurements of individual animals: towards quantitative description of animal status
Our ability to describe differences between animal phenotypes is limited, especially in the context of applying nutritional models to predict performance of different genotypes in different environments. This may reflect the diffculty of measuring animal parameters relative to the ease of measuring feed characteristics. However, recent advances in on-farm technology now provide us with multiple time-series of reliably measured indicators of animal performance and status. This paper presents results from work to develop methods for extracting biologically meaningful features from such time-series that offer the potential for quantification of animal status at the level of the individual. One approach to feature extraction from time-series data is presented using milk yield data as an example. In this approach, the data are reduced to a smooth function using B-splines. These are very flexible in terms of approximating complex patterns, they can also be readily differentiated to provide derivatives that may in themselves be features with biological meaning such as milk yield acceleration. By imposing different degrees of smoothing it is possible to decompose the milk yield curve into components that describe the cow’s response to infection or nutritional challenge, as well as describing the phenotypic potential yield function of that cow. Each of these features relate to aspects of the cow’s potential and robustness. Features can also be combined across different measures, this is useful if the phenomenon we wish to describe is deemed to be latent with respect to the available measures. Taking mastitis as an example we present a proof of principle for combining time-series measures of different indicators (somatic cell count, electrical conductivity, and an enzyme in milk) to derive a latent quantity; degree of infection. These results are discussed in the context of achieving better descriptions of animal characteristics and their potential for driving nutritional models.
KeywordsB-spline smoothing latent process state-space model
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