Here, we developed the first R library for MTL, offering a comprehensive machine learning pipeline that covers several types of MLT algorithms and can be easily applied to high-dimensional data.

Previous implementations of MTL have focused on knowledge transfer via regularization ( Zhou et al., 2011), Bayesian methods ( Greenlaw et al., 2017) or deep architectures ( Yang and Hospedales, 2016). For such applications, multi-task learning offers the possibility to directly explore illness-related biological profiles that are linked across data modalities and therefore a new route toward the identification of biomarker signatures. Previous research has already shown that for such illnesses, integrative multi-omics open a new avenue for identification of etiological mechanisms, for example by taking into account genetic, expression and methylation data simultaneously. Ī strong motivation to develop biomedical MTL applications stems from the necessity to integrate diverse data sources to explore the biological underpinning of complex illnesses, such as schizophrenia. Since its inception ( Caruana, 1998), MTL has been used in numerous data-intensive research areas, including biomedical informatics ( Feriante, 2015 Li et al., 2016 Widmer and Ratsch, 2012 Xu et al., 2011 Yuan et al., 2016 Zhou et al., 2013), speech and natural language processing, image processing and computer vision, as well as web based applications.

Multi-task learning (MTL) is a machine learning technique that explores and exploits the relatedness across a set of different learning tasks.

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