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Free keywords:
Quantitative Biology, Quantitative Methods, q-bio.QM,Statistics, Machine Learning, stat.ML
Abstract:
Over the last years, huge resources of biological and medical data have
become available for research. This data offers great chances for machine
learning applications in health care, e.g. for precision medicine, but is also
challenging to analyze. Typical challenges include a large number of possibly
correlated features and heterogeneity in the data. One flourishing field of
biological research in which this is relevant is epigenetics. Here, especially
large amounts of DNA methylation data have emerged. This epigenetic mark has
been used to predict a donor's 'epigenetic age' and increased epigenetic aging
has been linked to lifestyle and disease history. In this paper we propose an
adaptive model which performs feature selection for each test sample
individually based on the distribution of the input data. The method can be
seen as partially blind domain adaptation. We apply the model to the problem of
age prediction based on DNA methylation data from a variety of tissues, and
compare it to a standard model, which does not take heterogeneity into account.
The standard approach has particularly bad performance on one tissue type on
which we show substantial improvement with our new adaptive approach even
though no samples of that tissue were part of the training data.
