Studying Limits of Explainability by Integrated Gradients for Gene Expression Models
This work addresses the challenge of reliable explainability in genomics for researchers, but it is incremental as it builds on existing methods without major breakthroughs.
The paper investigates the limitations of Integrated Gradients for identifying biomarkers in gene expression models, showing that feature importance rankings are insufficient for robust biomarker identification, and proposes a hierarchical simulation model and evaluation practices to address this.
Understanding the molecular processes that drive cellular life is a fundamental question in biological research. Ambitious programs have gathered a number of molecular datasets on large populations. To decipher the complex cellular interactions, recent work has turned to supervised machine learning methods. The scientific questions are formulated as classical learning problems on tabular data or on graphs, e.g. phenotype prediction from gene expression data. In these works, the input features on which the individual predictions are predominantly based are often interpreted as indicative of the cause of the phenotype, such as cancer identification. Here, we propose to explore the relevance of the biomarkers identified by Integrated Gradients, an explainability method for feature attribution in machine learning. Through a motivating example on The Cancer Genome Atlas, we show that ranking features by importance is not enough to robustly identify biomarkers. As it is difficult to evaluate whether biomarkers reflect relevant causes without known ground truth, we simulate gene expression data by proposing a hierarchical model based on Latent Dirichlet Allocation models. We also highlight good practices for evaluating explanations for genomics data and propose a direction to derive more insights from these explanations.