Readings

Papers are organized by course module and topic. Some papers appear under multiple headings where relevant. Papers marked with ⭐ are especially recommended as entry points to a topic.

Background: Causality & Generalization in Biohealth

Module I, Weeks 1–2

• ⭐ From Statistical to Causal Learning (Schölkopf et al., 2022)
• ⭐ Causality in digital medicine (Glocker et al., 2021)
• ⭐ The Seven Tools of Causal Inference, with Reflections on Machine Learning (Pearl, 2019)
• Hernán & Robins, “Causal Inference: What If” — Chapters 1–3 (Hernán & Robins, 2020)
• ⭐ Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas (Rood et al., 2024)
• Causal modelling of gene effects from regulators to programs to traits (Ota et al., 2025)
• Causal machine learning for single-cell genomics (Tejada-Lapuerta et al., 2025)
• ⭐ Applications of machine learning in drug discovery and development (Vamathevan et al., 2019)
• Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs (Zech et al., 2018)
• Clinical AI tools must convey predictive uncertainty for each individual patient (Banerji et al., 2023)
• From development to deployment: dataset shift, causality, and shift-stable models in health AI (Subbaswamy & Saria, 2020)
• The Clinician and Dataset Shift in Artificial Intelligence (Finlayson et al., 2021)
• Buyer Beware: confounding factors and biases abound when predicting omics-based biomarkers from histological images (Dawood et al., 2024)
• Domain-Invariant Feature Learning for Patient-Level Phenotype Prediction from Single-Cell Data (Perez et al., 2025)
• Mendelian randomization: genetic anchors for causal inference in epidemiological studies (Davey Smith & Hemani, 2014)

Hybrid & Mechanistic Modeling

Module II, Week 3

Foundational Methods

• ⭐ Neural Ordinary Differential Equations (Chen et al., 2018)
• ⭐ Universal Differential Equations for Scientific Machine Learning (Rackauckas et al., 2020)
• ⭐ Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations (Raissi et al., 2019)
• Neural Controlled Differential Equations for Irregular Time Series (Kidger et al., 2020)
• Latent ODEs for Irregularly-Sampled Time Series (Rubanova et al., 2019)

Reviews & Surveys

• ⭐ Physics-informed machine learning (Karniadakis et al., 2021)
• Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (Willard et al., 2022)
• ⭐ Breiman’s Two Cultures: You Don’t Have to Choose Sides (Miller, Foti & Fox, 2021)

Clinical & Biomedical Applications

• ⭐ Learning Insulin-Glucose Dynamics in the Wild (Miller, Foti & Fox, 2020)
• ⭐ Hybrid² Neural ODE Causal Modeling and an Application to Glycemic Response (Zou et al., 2024)
• Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild (Wang & Fox, 2023)
• ⭐ Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression (Qian et al., 2021)
• Neural Pharmacodynamic State Space Modeling (Hussain et al., 2021)
• Real-time electronic health record mortality prediction during the COVID-19 pandemic: A prospective cohort study (Sottile et al., 2021)
• Conditional universal differential equations capture population dynamics and interindividual variation in c-peptide production (de Rooij et al., 2025)
• Low-dimensional neural ODEs and their application in pharmacokinetics (Bräm et al., 2023)
• Modeling personalized heart rate response to exercise and environmental factors with wearables data (Nazaret et al., 2023)
• A foundation model for continuous glucose monitoring data (Lutsker et al., 2025)
• Large-scale Training of Foundation Models for Wearable Biosignals (Abbaspourazad et al., 2023)
• Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems (Huang et al., 2024)
• Trajectory Flow Matching with Applications to Clinical Time Series Modeling (Huang et al., 2024)
• SPADE: Inferring Transcriptional Dynamics from Spatial Transcriptomics with Physics-Informed Deep Learning (Wang et al., 2025)
• Data-driven causal model discovery and personalized prediction in Alzheimer’s disease (Zheng et al., 2022)

Virtual Cell Models & Digital Twins

Modules I/III, Weeks 2 & 6

• ⭐ How to build the virtual cell with artificial intelligence: Priorities and opportunities (Bunne et al., 2024)
• ⭐ Virtual Cells as Causal World Models: A Perspective on Evaluation (Callahan et al., 2025)
• Virtual Cells Need Context, Not Just Scale (Dibaeinia et al., 2026)
• TwinCell: Large Causal Cell Model for Reliable and Interpretable Therapeutic Target Prioritisation (Morlot et al., 2026)
• X-CELL (Wang et al., 2026)
• Stack: Simulating cellular conditions via prompt engineering (Dong et al., 2026)
• Digital twin to enable the vision of precision cardiology (Corral-Acero et al., 2020)

Causal Representation Learning

Module III, Week 4

• ⭐ Towards Causal Representation Learning (Schölkopf et al., 2021)
• ⭐ Invariant Risk Minimization (Arjovsky et al., 2019)
• ⭐ Causal inference using invariant prediction (Peters et al., 2016)
• Causal Structure and Representation Learning with Biomedical Applications (Uhler et al., 2025)
• Identifiability Guarantees for Causal Disentanglement from Soft Interventions (Zhang et al., 2023)
• Identifiability Guarantees for Causal Disentanglement from Purely Observational Data (Welch et al., 2024)
• Unpaired Multi-Domain Causal Representation Learning (Sturma et al., 2023)
• Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling (Lopez et al., 2022)
• The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations (Yao et al., 2025)
• Towards Causal Foundation Model: on Duality between Causal Inference and Attention (Zhang et al., 2023)
• Use What You Know: Causal Foundation Models with Partial Graphs (Robertson et al., 2026)
• An Information Criterion for Controlled Disentanglement of Multimodal Data (Wang et al., 2024)
• Causal disentanglement for single-cell representations and controllable counterfactual generation (Gao et al., 2024)
• oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis (Ainsworth et al., 2018)

Learning from Interventional Data

Module III, Weeks 5–6

Perturbation Response Prediction

• ⭐ Learning single-cell perturbation responses using neural optimal transport (Bunne et al., 2023)
• ⭐ Learning Genetic Perturbation Effects with Variational Causal Inference (Liu et al., 2025)
• scGen predicts single-cell perturbation responses (Lotfollahi et al., 2019)
• In silico biological discovery with large perturbation models (Miladinovic et al., 2025)
• Combinatorial prediction of therapeutic perturbations using causally inspired neural networks (Gonzalez et al., 2025)
• Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients (Ma et al., 2021)
• Latent Causal Diffusions for Single-Cell Perturbation Modeling (Lorch et al., 2026)
• Predicting how perturbations reshape cellular trajectories with PerturbGen (Ly et al., 2026)
• MORPH: Predicting the Single-Cell Outcome of Genetic Perturbations Across Conditions and Data Modalities (He et al., 2025)
• Predicting cellular responses to perturbation across diverse contexts with State (Adduri et al., 2025)
• TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction (Wenkel et al., 2025)
• SynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction (Phillips et al., 2025)
• scGenePT: Is language all you need for modeling single-cell perturbations? (Istrate et al., 2024)
• Fast and scalable Wasserstein-1 neural optimal transport solver for single-cell perturbation prediction (Chen et al., 2024)
• Cross-modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport (Ryu et al., 2024)
• Mapping and reprogramming human tissue microenvironments with MintFlow (Akbarnejad et al., 2025)
• Predicting and interpreting cell-type-specific drug responses in the small-data regime using inductive priors (Alsulami et al., 2026)

Counterfactual Inference in Single Cells

• Integrating multi-covariate disentanglement with counterfactual analysis (CellDISECT) (Megas et al., 2025)
• Causal Disentanglement of Treatment Effects in Single-Cell RNA Sequencing Through Counterfactual Inference (An et al., 2025)
• Causal disentanglement for single-cell representations and controllable counterfactual generation (Gao et al., 2024)
• Causal differential expression analysis under unmeasured confounders with causarray (Du et al., 2025)
• Single-cell disentangled representations for perturbation modeling and treatment effect estimation (Sun et al., 2025)

Generative Models for Cellular Morphology

• CellFlux: Simulating Cellular Morphology Changes via Flow Matching (Zhang et al., 2025)
• Predicting cell morphological responses to perturbations using generative modeling (IMPA) (Palma et al., 2023)
• MorphoDiff: Cellular Morphology Painting with Diffusion Models (Navidi et al., 2024)
• Modeling Complex System Dynamics with Multi-Marginal Flow Matching (Rohbeck et al., 2025)

Seminal Experimental Papers

• ⭐ Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens (Dixit et al., 2016)
• ⭐ Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq (Replogle et al., 2022)

Experimental Design & Active Learning

• ⭐ Active learning for optimal intervention design in causal models (Zhang et al., 2023)

Methods & Reviews

• Optimal transport for single-cell and spatial omics (Bunne et al., 2024)

Foundation Models for Cell Biology & Biomedicine

Module III, Week 6

• ⭐ Towards multimodal foundation models in molecular cell biology (Cui et al., 2025)
• ⭐ Toward learning a foundational representation of cells and genes (Lotfollahi et al., 2024)
• Large-scale foundation model on single-cell transcriptomics (scFoundation) (Hao et al., 2024)
• Scaling Large Language Models for Next-Generation Single-Cell Analysis (Rizvi et al., 2025)
• A Cross-Species Generative Cell Atlas (TranscriptFormer) (Pearce et al., 2025)
• GREmLN: A Cellular Regulatory Network-Aware Transcriptomics Foundation Model (Zhang et al., 2025)
• ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy (Kenyon-Dean et al., 2024)
• SubCell: Proteome-aware vision foundation models for microscopy (Gupta et al., 2024)
• scVAEDer: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics (Sadria et al., 2025)
• AUTOENCODIX: a framework to train and evaluate autoencoders for biological representation learning (Joas et al., 2025)
• Group Contrastive Learning for Weakly Paired Multimodal Data (GROOVE) (Gorla et al., 2026)
• A Multimodal Foundation Model for Discovering Genetic Associations with Brain Imaging Phenotypes (Machado Reyes et al., 2024)
• GraphComm predicts cell-cell communication using a graph-based deep learning method in single-cell RNA sequencing data (So et al., 2025)

Causal Discovery

Module III, cross-cutting

• ⭐ Large-scale causal discovery using interventional data sheds light on gene network structure (Barth et al., 2025)
• Sample, estimate, aggregate: A recipe for causal discovery foundation models (Wu et al., 2024)
• DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets (Atanackovic et al., 2023)
• Efficient Differentiable Discovery of Causal Order (Chevalley et al., 2024)
• Causal gene regulatory network inference from Perturb-seq via adaptive instrumental variable modeling (Sun et al., 2026)
• Phenome-scale causal network discovery with bidirectional mediated Mendelian randomization (Brown et al., 2020)
• Data-driven causal model discovery and personalized prediction in Alzheimer’s disease (Zheng et al., 2022)
• ⭐ Neural Granger Causality (Tank et al., 2021)

Causal Transportability & External Validity

Module IV, Week 8

• ⭐ External Validity: From Do-Calculus to Transportability Across Populations (Pearl & Bareinboim, 2014)
• ⭐ Causal inference and the data-fusion problem (Bareinboim & Pearl, 2016)
• A Review of Generalizability and Transportability (Degtiar & Rose, 2023)
• Extending inferences from a randomized trial to a target population (Dahabreh et al., 2019)
• Synthetic and external controls in clinical trials (Thorlund et al., 2020)
• ⭐ Virtual Cells Need Context, Not Just Scale (Dibaeinia et al., 2026)
• Deciphering causal proteins in Alzheimer’s disease: Mendelian randomization integrated with AlphaFold3 (Yao et al., 2023)

Policy Learning & Treatment Optimization

Module IV, Week 7

• ⭐ Learning Explainable Treatment Policies with Clinician-Informed Representations: A Practical Approach (Ferstad et al., 2024)
• ⭐ Guidelines for Reinforcement Learning in Healthcare (Gottesman et al., 2019)
• Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models (Oberst & Sontag, 2019)
• Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning (Kallus & Zhou, 2020)

Mechanistic & Biological Interpretability

Module III, Week 6

• ⭐ Sparse autoencoders uncover biologically interpretable features in protein language model representations (Gujral et al., 2025)
• Learning biologically relevant features in a pathology foundation model using sparse autoencoders (Le et al., 2024)
• MechSci: Scaling Clinical Science via Mechanistic Interpretability of Multimodal Medical Foundation Models (2025)
• Beyond the Black Box: Identifiable Interpretation and Control in Generative Models via Causal Minimality (Kong et al., 2025)
• Biologically Guided Variational Inference for Interpretable Multimodal Single-Cell Integration (Arnoldt et al., 2025)
• Interpretable single-cell factor decomposition using sciRED (Pouyabahar et al., 2025)

Agentic AI & LLMs for Scientific Discovery

Module V, Week 9

• ⭐ Empowering biomedical discovery with AI agents (Gao et al., 2024)
• The Virtual Biotech: A Multi-Agent AI Framework for Therapeutic Discovery (Zhang et al., 2026)
• TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools (Gao et al., 2025)
• BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments (Roohani et al., 2024)
• Kosmos: An AI Scientist for Autonomous Discovery (Mitchener et al., 2025)
• BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model (Fallahpour et al., 2025)
• rbio1: training scientific reasoning LLMs with biological world models (Istrate et al., 2025)
• The Anatomy of a Personal Health Agent (Heydari et al., 2025)
• Causal AI Scientist: Facilitating Causal Data Science with Large Language Models (Verma et al., 2025)
• CURE-Bench: AI Reasoning for Therapeutics (Zitnik et al., 2025)
• How Well Do Multimodal Models Reason on ECG Signals? (Xu et al., 2026)

Benchmarking & Evaluation

Cross-cutting

• ⭐ Deep learning-based predictions of gene perturbation effects do not yet outperform simple linear baselines (Ahlmann-Eltze et al., 2024)
• Foundation Models Improve Perturbation Response Prediction (Cole et al., 2026)
• Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation (Torné et al., 2025)
• Limitations of cell embedding metrics assessed using drifting islands (Wang et al., 2025)
• Zero-shot evaluation reveals limitations of single-cell foundation models (Kedzierska et al., 2025)
• scGeneScope: A Treatment-Matched Single Cell Imaging and Transcriptomics Dataset and Benchmark for Treatment Response Modeling (Dapello et al., 2025)
• BixBench: a Comprehensive Benchmark for LLM-based Agents in Computational Biology (Mitchener et al., 2025)
• Random feature baselines provide distributional performance benchmarks for clinical and omic machine learning (Ellis et al., 2024)
• Out-of-distribution evaluations of channel agnostic masked autoencoders in fluorescence microscopy (Hurry et al., 2025)