There is an increasing interest in the use of Deep Learning (DL) based
methods as a supporting analytical framework in oncology. However, most direct
applications of DL will deliver models with limited transparency and
explainability, which constrain their deployment in biomedical settings. This
systematic review discusses DL models used to support inference in cancer
biology with a particular emphasis on multi-omics analysis. It focuses on how
existing models address the need for better dialogue with prior knowledge,
biological plausibility and interpretability, fundamental properties in the
biomedical domain. For this, we retrieved and analyzed 42 studies focusing on
emerging architectural and methodological advances, the encoding of biological
domain knowledge and the integration of explainability methods. We discuss the
recent evolutionary arch of DL models in the direction of integrating prior
biological relational and network knowledge to support better generalisation
(e.g. pathways or Protein-Protein-Interaction networks) and interpretability.
This represents a fundamental functional shift towards models which can
integrate mechanistic and statistical inference aspects. We introduce a concept
of bio-centric interpretability and according to its taxonomy, we discuss
representational methodologies for the integration of domain prior knowledge in
such models. The paper provides a critical outlook into contemporary methods
for explainability and interpretabiltiy used in DL for cancer. The analysis
points in the direction of a convergence between encoding prior knowledge and
improved interpretability. We introduce bio-centric interpretability which is
an important step towards formalisation of biological interpretability of DL
models and developing methods that are less problem- or application-specific.

对 42 篇研究进行了系统综述，旨在讨论深度学习模型如何支持癌症生物学的推理，并重点关注多组学分析。我们介绍了一种称为生物中心可解释性的概念，并根据其分类方法讨论了在这些模型中整合领域先验知识的表征方法。分析表明，编码先验知识和改进解释性之间存在趋同性，这是深度学习模型形式化生物解释性和发展不那么特定于问题或应用的方法的重要一步。