  Machine learning techniques are steadily becoming more important in modern
biology, and are used to build predictive models, discover patterns, and
investigate biological problems. However, models trained on one dataset are
often not generalizable to other datasets from different cohorts or
laboratories, due to differences in the statistical properties of these
datasets. These could stem from technical differences, such as the measurement
technique used, or from relevant biological differences between the populations
studied. Domain adaptation, a type of transfer learning, can alleviate this
problem by aligning the statistical distributions of features and samples among
different datasets so that similar models can be applied across them. However,
a majority of state-of-the-art domain adaptation methods are designed to work
with large-scale data, mostly text and images, while biological datasets often
suffer from small sample sizes, and possess complexities such as heterogeneity
of the feature space. This Review aims to synthetically discuss domain
adaptation methods in the context of small-scale and highly heterogeneous
biological data. We describe the benefits and challenges of domain adaptation
in biological research and critically discuss some of its objectives,
strengths, and weaknesses through key representative methodologies. We argue
for the incorporation of domain adaptation techniques to the computational
biologist's toolkit, with further development of customized approaches.
