While deep learning has achieved great success in many fields, one common criticism about deep learning is its lack of interpretability. In most cases, the hidden units in a deep neural network do not have a clear semantic meaning or correspond to any physical entities. However, model interpretability and explainability are crucial in many biomedical applications. To address this challenge, we developed the Factor Graph Neural Network model that is interpretable and predictable by combining probabilistic graphical models with deep learning. We directly encode biological knowledge such as Gene Ontology as a factor graph into the model architecture, making the model transparent and interpretable. Furthermore, we devised an attention mechanism that can capture multi-scale hierarchical interactions among biological entities such as genes and Gene Ontology terms. With parameter sharing mechanism, the unrolled Factor Graph Neural Network model can be trained with stochastic depth and generalize well. We applied our model to two cancer genomic datasets to predict target clinical variables and achieved better results than other traditional machine learning and deep learning models. Our model can also be used for gene set enrichment analysis and selecting Gene Ontology terms that are important to target clinical variables.
To successfully negotiate a deal, it is not enough to communicate fluently: pragmatic planning of persuasive negotiation strategies is essential. While modern dialogue agents excel at generating fluent sentences, they still lack pragmatic grounding and cannot reason strategically. We present DialoGraph, a negotiation system that incorporates pragmatic strategies in a negotiation dialogue using graph neural networks. DialoGraph explicitly incorporates dependencies between sequences of strategies to enable improved and interpretable prediction of next optimal strategies, given the dialogue conte…
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Shanghai Jiao Tong University（上海交通大学）；Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University（中山大学机器智能与先进计算重点实验室）
Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanations
Data Science Institute, School of Mathematics, Shandong University（数据科学研究院、数学学院、山东大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（数学与系统科学研究院、中国科学院）；Data Science Institute, Shandong University（数据科学研究院、山东大学）
Single-cell RNA-seq foundation models achieve strong performance on downstream tasks but remain black boxes, limiting their utility for biological discovery. Recent work has shown that sparse dictionary learning can extract concepts from deep learning models, with promising applications in biomedical imaging and protein models. However, interpreting biological concepts remains challenging, as biological sequences are not inherently human-interpretable. We introduce a novel concept-based interpretability framework for single-cell RNA-seq models with a focus on concept interpretation and evaluat…
Graph-structured data arise in a variety of real-world context ranging from sensor and transportation to biological and social networks. As a ubiquitous tool to process graph-structured data, spectral graph filters have been used to solve common tasks such as denoising and anomaly detection, as well as design deep learning architectures such as graph neural networks. Despite being an important tool, there is a lack of theoretical understanding of the stability properties of spectral graph filters, which are important for designing robust machine learning models. In this paper, we study filter …
Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks. Unfortunately, GNNs often fail to capture the relative importance of interactions between molecular substructures, in part due to the absence of efficient intermediate pooling steps. To address these issues, we propose LaPool (Laplacian Pooling), a novel, data-driven, and interpretable hierarchical graph pooling method that takes into account both node features and graph structure to improve molecular representation. We benchmark LaPool on molecular graph prediction and unde…
Washington University in St. Louis（圣路易斯华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）
We investigate molecular mechanisms of resistant or sensitive response of cancer drug combination therapies in an inductive and interpretable manner. Though deep learning algorithms are widely used in the drug synergy prediction problem, it is still an open problem to formulate the prediction model with biological meaning to investigate the mysterious mechanisms of synergy (MoS) for the human-AI collaboration in healthcare systems. To address the challenges, we propose a deep graph neural network, IDSP (Interpretable Deep Signaling Pathways), to incorporate the gene-gene as well as gene-drug r…
Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis
Department of Information Engineering, University of Padova（信息工程系，帕多瓦大学）；Department of Neuroscience, University of Padova（神经科学系，帕多瓦大学）；Padova Neuroscience Center（帕多瓦神经科学中心）；Information Systems Institute, University of Applied Sciences Western Switzerland, HES-SO Valais（应用科学西瑞士信息系统研究所，HES-SO瓦莱大学）；Department of Comparative Biomedicine and Food Science, University of Padova（比较生物医学与食品科学系，帕多瓦大学）；Padua Center for Network Medicine, University of Padova（帕维亚网络医学中心，帕多瓦大学）
In biomedical applications of machine learning, relevant information often has a rich structure that is not easily encoded as real-valued predictors. Examples of such data include DNA or RNA sequences, gene sets or pathways, gene interaction or coexpression networks, ontologies, and phylogenetic trees. We highlight recent examples of machine learning models that use structure to constrain model architecture or incorporate structured data into model training. For machine learning in biomedicine, where sample size is limited and model interpretability is critical, incorporating prior knowledge i…
We report a neural architecture search framework, BioNAS, that is tailored for biomedical researchers to easily build, evaluate, and uncover novel knowledge from interpretable deep learning models. The introduction of knowledge dissimilarity functions in BioNAS enables the joint optimization of predictive power and biological knowledge through searching architectures in a model space. By optimizing the consistency with existing knowledge, we demonstrate that BioNAS optimal models reveal novel knowledge in both simulated data and in real data of functional genomics. BioNAS provides a useful too…
Imperial College London（帝国理工学院）；University College London（伦敦大学学院）；Hong Kong Baptist University（香港浸会大学）
The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This "black box" problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we present XOmiVAE, a variational autoencoder (VAE) based interpretable deep learning model for cancer classification using high-dimensional omics data. XOmiVAE is capable of revealing the contribution of each gene and latent dimension for each classification prediction, and the correlation between each gene and each latent dimension. It is also demonst…
Recent breakthroughs in cancer research have come via the up-and-coming field of pathway analysis. By applying statistical methods to prior known gene and protein regulatory information, pathway analysis provides a meaningful way to interpret genomic data. While many gene/protein regulatory relationships have been studied, never before has such a significant amount data been made available in organized forms of gene/protein regulatory networks and pathways. However, pathway analysis research is still in its infancy, especially when applying it to solve practical problems. In this paper we prop…
