Motivation: Despite advances in the computational analysis of high-throughput
molecular profiling assays (e.g. transcriptomics), a dichotomy exists between
methods that are simple and interpretable, and ones that are complex but with
lower degree of interpretability. Furthermore, very few methods deal with
trying to translate interpretability in biologically relevant terms, such as
known pathway cascades. Biological pathways reflecting signalling events or
metabolic conversions are Small improvements or modifications of existing
algorithms will generally not be suitable, unless novel biological results have
been predicted and verified. Determining which pathways are implicated in
disease and incorporating such pathway data as prior knowledge may enhance
predictive modelling and personalised strategies for diagnosis, treatment and
prevention of disease.
Results: We propose a novel prior-knowledge-based deep auto-encoding
framework, PAAE, together with its accompanying generative variant, PAVAE, for
RNA-seq data in cancer. Through comprehensive comparisons among various
learning models, we show that, despite having access to a smaller set of
features, our PAAE and PAVAE models achieve better out-of-set reconstruction
results compared to common methodologies. Furthermore, we compare our model
with equivalent baselines on a classification task and show that they achieve
better results than models which have access to the full input gene set.
Another result is that using vanilla variational frameworks might negatively
impact both reconstruction outputs as well as classification performance.
Finally, our work directly contributes by providing comprehensive
interpretability analyses on our models on top of improving prognostication for
translational medicine.

本研究提出了一种基于先验知识的深度自编码框架（PAAE）及其生成变体（PAVAE），用于 RNA-seq 在癌症中的数据。通过与其他学习模型的比较，我们证明了 PAAE 和 PAVAE 模型不仅具有更好的重构结果，而且在分类任务中比有全面输入基因集的模型产生更好的结果，并对模型进行了全面的可解释性分析，从而提高了转化医学的预测能力。