# A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data arXiv: https://arxiv.org/abs/2207.00812 ยท HTML: https://mlsanity.com/paper/2207.00812/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 132 references that are in the map. - First posted on arXiv 2 Jul 2022. ## Builds on (most cited first, 27 of 27) - [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016) - [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017) - [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018) - [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017) - [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018) - [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017) - [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018) - [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016) - [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018) - [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019) - [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017) - [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019) - [Not Just a Black Box: Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1605.01713/) (2016) - [struc2vec: Learning Node Representations from Structural Identity](https://mlsanity.com/paper/1704.03165/) (2017) - [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019) - [Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers](https://mlsanity.com/paper/1604.00825/) (2016) - [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017) - [Towards Explainable Artificial Intelligence](https://mlsanity.com/paper/1909.12072/) (2019) - [Principles and Practice of Explainable Machine Learning](https://mlsanity.com/paper/2009.11698/) (2020) - [Interpretability of machine learning based prediction models in healthcare](https://mlsanity.com/paper/2002.08596/) (2020) - [Interpretability and Explainability: A Machine Learning Zoo Mini-tour](https://mlsanity.com/paper/2012.01805/) (2020) - [A Survey on Explainability in Machine Reading Comprehension](https://mlsanity.com/paper/2010.00389/) (2020) - [A Selective Overview of Deep Learning](https://mlsanity.com/paper/1904.05526/) (2019) - [Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making](https://mlsanity.com/paper/2204.05030/) (2022) - [XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data](https://mlsanity.com/paper/2105.12807/) (2021) - [Incorporating Biological Knowledge with Factor Graph Neural Network for Interpretable Deep Learning](https://mlsanity.com/paper/1906.00537/) (2019) - [Transformers and the representation of biomedical background knowledge](https://mlsanity.com/paper/2202.02432/) (2022) ## Built on by (most cited first, 5 of 5) - [Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning](https://mlsanity.com/paper/2403.07078/) (2024) - [A Neural Network Alternative to Tree-based Models](https://mlsanity.com/paper/2410.17758/) (2024) - [TRAPS: Treatment-Assignment Prediction via Pathway-informed Stratification](https://mlsanity.com/paper/2606.09898/) (2026) - [Computational Methods and Challenges in Cell-Free DNA Analysis for Multi-Cancer Early Detection](https://mlsanity.com/paper/2606.20174/) (2026) - [An Interpretable Deep Learning Framework for Discovery and Clinical Validation of Deep Radiomic Signatures in Tumor Classification](https://mlsanity.com/paper/2607.03593/) (2026) ## Abstract (arXiv) 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.