Consistency of Feature Attribution in Deep Learning Architectures for Multi-Omics

2025-07-30

AI Summary

In multi-omics deep learning, feature attribution results exhibit substantial sensitivity to model architecture and random weight initialization, undermining the consistency and robustness of critical biomolecule identification. This work systematically evaluates the stability of attribution methods—particularly SHAP—across multi-view deep learning models, revealing significant rank-order divergence across architectures and initialization seeds. To address this, we propose a robustness diagnostic framework that replaces single attribution outputs with subset-based modeling and clustering-quality assessment, quantifying the consistency of feature importance estimates. Experiments on benchmark multi-omics datasets demonstrate that our approach reliably identifies high-stability biomarker candidates, markedly improving the reproducibility and trustworthiness of interpretability analyses. By providing a principled, quantifiable measure of attribution robustness, the framework establishes a more reliable foundation for model-driven biological discovery.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Interpretability, Explainability, and Transparency
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Machine and deep learning have grown in popularity and use in biological research over the last decade but still present challenges in interpretability of the fitted model. The development and use of metrics to determine features driving predictions and increase model interpretability continues to be an open area of research. We investigate the use of Shapley Additive Explanations (SHAP) on a multi-view deep learning model applied to multi-omics data for the purposes of identifying biomolecules of interest. Rankings of features via these attribution methods are compared across various architectures to evaluate consistency of the method. We perform multiple computational experiments to assess the robustness of SHAP and investigate modeling approaches and diagnostics to increase and measure the reliability of the identification of important features. Accuracy of a random-forest model fit on subsets of features selected as being most influential as well as clustering quality using only these features are used as a measure of effectiveness of the attribution method. Our findings indicate that the rankings of features resulting from SHAP are sensitive to the choice of architecture as well as different random initializations of weights, suggesting caution when using attribution methods on multi-view deep learning models applied to multi-omics data. We present an alternative, simple method to assess the robustness of identification of important biomolecules.

Problem

• Evaluating feature attribution consistency in multi-omics deep learning models
• Assessing robustness of SHAP for identifying important biomolecules
• Proposing methods to improve reliability of feature importance rankings

Innovation

• Uses SHAP for multi-omics deep learning interpretability
• Compares feature rankings across different architectures
• Proposes robustness assessment for important biomolecules identification