Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Transfer Learning of Clinical Outcomes with Molecular Data, Principles and Perspectives

Version 1 : Received: 12 October 2021 / Approved: 13 October 2021 / Online: 13 October 2021 (16:28:59 CEST)

How to cite: Kowald, A.; Barrantes, I.; Möller, S.; Palmer, D.; Murua Escobar, H.; Fuellen, G. Transfer Learning of Clinical Outcomes with Molecular Data, Principles and Perspectives. Preprints 2021, 2021100207 (doi: 10.20944/preprints202110.0207.v1). Kowald, A.; Barrantes, I.; Möller, S.; Palmer, D.; Murua Escobar, H.; Fuellen, G. Transfer Learning of Clinical Outcomes with Molecular Data, Principles and Perspectives. Preprints 2021, 2021100207 (doi: 10.20944/preprints202110.0207.v1).

Abstract

Accurate transfer learning of clinical outcomes, e.g., of the effects and side effects of drugs or other interventions, from one cellular context to another (in-vitro versus ex-vivo versus in-vivo, or across tissues), between cell-types, developmental stages, omics modalities or species, is considered tremendously useful. Ultimately, it may avoid most drug development failing in translation, despite large investments in the preclinical stages, which includes animal experiments requiring careful justification. Thus, when transferring a prediction task from a source (model) domain to a target domain, what counts is the high quality of the predictions in the target domain, requiring molecular states or processes common to both source and target that can be learned by the predictor, reflected by latent variables. These latent variables may form a compendium of knowledge that is learned in the source, to enable predictions in the target; usually, there are few, if any, labeled target training samples to learn from. Transductive learning then refers to the learning of the predictor in the source domain, transferring its outcome label calculations to the target domain, considering the same task. Inductive learning considers cases where the target predictor is performing a different yet related task as compared to the source predictor, making some labeled target data necessary. Often, there is also a need to first map the variables in the input/feature spaces (e.g. of gene names to orthologs) and/or the variables in the output/outcome spaces (e.g. by matching of labels). Transfer across omics modalities also requires that the molecular information flow connecting these modalities is sufficiently conserved. Only one of the methods for transfer learning we reviewed offers an assessment of input data, suggesting that transfer learning is unreliable in certain cases. Moreover, source domains feature their very own particularities, and transfer learning should consider these, e.g., as differences in pharmacokinetics, drug clearance or the microenvironment. In light of these general considerations, we here discuss and juxtapose various recent transfer learning approaches, specifically designed (or at least adaptable) to predict clinical (human in-vivo) outcomes based on molecular data, towards finding the right tool for a given task, and paving the way for a comprehensive and systematic comparison of the suitability and accuracy of transfer learning of clinical outcomes.

Keywords

transfer learning; classification; regression

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