A Survey of Transfer and Multitask Learning in Bioinformatics

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Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction (2016, background): “Transfer learning can help in reusing knowledge from annotated datasets to new domains [22], reducing the knowledge gap of labeled data due to heterogenous variations.”
A Review of Domain Adaptation without Target Labels (2019, background): “For some problem settings, the goal is to generalize from one model organism to another [62], such as from nematodes to fruit flies [27].”
An introduction to domain adaptation and transfer learning (2018, background): “Most often, domains correspond to different model organisms or different data-collecting research institutes [208].”
Multi-Domain Adversarial Learning (2019, methodology): “Bioinformatics is increasingly appreciating the need for domain adaptation methods (Borgwardt et al., 2006; Schweikert et al., 2008; Xu & Yang, 2011; Vallania et al., 2017).”
Multitask learning for host–pathogen protein interactions (2013, background): “For a survey of multi-task learning in computational biology, see Xu and Yang (2011).”