Human-Guided Causal Knowledge Injection for Virtual Cells

• URL: http://arxiv.org/abs/2608.08430v1
• Date: Sun, 09 Aug 2026 03:00:36 GMT
• Title: Human-Guided Causal Knowledge Injection for Virtual Cells
• Abstract summary: Injecting causal graphs into virtual cells can improve the interpretability, but such graphs are usually not available in real-world applications.<n>We propose a human-guided causal knowledge injection method for virtual cells.
• Score: 29.970142871278473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Virtual cells employ machine learning models to simulate and predict cellular behaviors, serving as a critical computational framework for investigating health and disease. Injecting causal graphs into virtual cells can improve the interpretability, but such graphs are usually not available in real-world applications. Recently, many methods have been proposed to construct causal graphs from data, which group genes based on their similarities to form concepts and extract their causal relationships. However, since this automatic process is unsupervised, the causal graphs usually contain errors. In this paper, we propose a human-guided causal knowledge injection method for virtual cells. We developed a gene-similarity-aware causal graph visualization supported by a hybrid optimization algorithm to help explore both the causal relationships between concepts and the similarities between genes. Based on the exploration, we further developed a counterfactual analysis strategy supported by a counterfactual visualization and a causal path visualization to help validate and refine causal graphs. The effectiveness of our method is demonstrated through two real-world case studies, the extraction of scientifically meaningful causal insights, and positive feedback from domain experts.

Related papers

• Causal Graph Neural Networks for Healthcare [2.446787923076599]
  Causal graph neural networks address this triple crisis of distribution shift, discrimination, and inscrutability.<n>This Review examines methodological foundations spanning structural causal models, disentangled causal representation learning, and techniques for interventional prediction and counterfactual reasoning on graphs.
  arXiv  Detail & Related papers  (2025-11-04T12:34:46Z)
• Causal Discovery in Recommender Systems: Example and Discussion [3.013819656993265]
  Causality is receiving increasing attention by the artificial intelligence and machine learning communities. This paper gives an example of modelling a recommender system problem using causal graphs.
  arXiv  Detail & Related papers  (2024-09-16T13:31:04Z)
• Visual Analysis of Multi-outcome Causal Graphs [21.406338910685232]
  We introduce a visual analysis method for multiple causal graphs with different outcome variables, namely, multi-outcome causal graphs. We collaborated with medical experts to devise two comparative visualization techniques at different stages of the analysis process.
  arXiv  Detail & Related papers  (2024-07-31T08:44:34Z)
• Adaptive Online Experimental Design for Causal Discovery [9.447864414136905]
  Causal discovery aims to uncover cause-and-effect relationships encoded in causal graphs. We focus on data interventional efficiency and formalize causal discovery from the perspective of online learning. We propose a track-and-stop causal discovery algorithm that adaptively selects interventions from the graph separating system.
  arXiv  Detail & Related papers  (2024-05-19T13:26:33Z)
• Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal Perspective [15.162584339143239]
  Graph contrastive learning is a general learning paradigm excelling at capturing invariant information from diverse perturbations in graphs. Recent works focus on exploring the structural rationale from graphs, thereby increasing the discriminability of the invariant information. We propose the dimensional rationale-aware graph contrastive learning approach, which introduces a learnable dimensional rationale acquiring network and a redundancy reduction constraint.
  arXiv  Detail & Related papers  (2023-12-16T10:05:18Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• CLEAR: Generative Counterfactual Explanations on Graphs [60.30009215290265]
  We study the problem of counterfactual explanation generation on graphs. A few studies have explored counterfactual explanations on graphs, but many challenges of this problem are still not well-addressed. We propose a novel framework CLEAR which aims to generate counterfactual explanations on graphs for graph-level prediction models.
  arXiv  Detail & Related papers  (2022-10-16T04:35:32Z)
• Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning [102.9138736545956]
  Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. We develop for the first time a novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidance of node attributes and graph topologies. In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately.
  arXiv  Detail & Related papers  (2022-04-30T12:57:02Z)
• Graph Self-supervised Learning with Accurate Discrepancy Learning [64.69095775258164]
  We propose a framework that aims to learn the exact discrepancy between the original and the perturbed graphs, coined as Discrepancy-based Self-supervised LeArning (D-SLA) We validate our method on various graph-related downstream tasks, including molecular property prediction, protein function prediction, and link prediction tasks, on which our model largely outperforms relevant baselines.
  arXiv  Detail & Related papers  (2022-02-07T08:04:59Z)
• Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning [76.00395335702572]
  A central goal for AI and causality is the joint discovery of abstract representations and causal structure. Existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs. In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them.
  arXiv  Detail & Related papers  (2021-07-02T05:44:56Z)
• ACRE: Abstract Causal REasoning Beyond Covariation [90.99059920286484]
  We introduce the Abstract Causal REasoning dataset for systematic evaluation of current vision systems in causal induction. Motivated by the stream of research on causal discovery in Blicket experiments, we query a visual reasoning system with the following four types of questions in either an independent scenario or an interventional scenario. We notice that pure neural models tend towards an associative strategy under their chance-level performance, whereas neuro-symbolic combinations struggle in backward-blocking reasoning.
  arXiv  Detail & Related papers  (2021-03-26T02:42:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.