Research Articles
The Promise and Pitfalls of Zero-Shot Single-Cell Foundation Models in Perturbation Prediction
Single-cell foundation models (scFMs), pretrained on millions of cells, promise to revolutionize the in-silico prediction of cellular responses to genetic and drug perturbations.
Optimizing Single-Cell Foundation Model Hyperparameters: A Task-Specific Guide for Biomedical Research
Single-cell foundation models (scFMs) represent a transformative technology for analyzing cellular heterogeneity, but their effective application hinges on proper hyperparameter optimization.
Preventing Data Leakage in scFM Benchmarking: A Guide for Robust and Reproducible Drug Discovery
This article addresses the critical challenge of data leakage in single-cell force microscopy (scFM) benchmarking for drug discovery.
Navigating High Sparsity in scRNA-seq Data: A Comprehensive Guide to Single-Cell Foundation Models
Single-cell RNA sequencing (scRNA-seq) has revolutionized biological research by enabling the study of gene expression at cellular resolution.
Training Single-Cell Foundation Models: A Computational Guide to Architectures, Data, and Best Practices
This article provides a comprehensive guide for researchers and drug development professionals on the computational resources and methodologies required to train single-cell foundation models (scFMs).
Dataset Size and scFM Performance: A Practical Guide for Biomedical Researchers
Single-cell foundation models (scFMs) represent a transformative advancement for analyzing cellular heterogeneity, yet their effective application is critically dependent on dataset size and quality.
Batch Integration of Single-Cell Data with Foundation Models: A 2025 Guide for Biomedical Researchers
The integration of single-cell data across batches, studies, and modalities is a critical challenge in modern biomedical research.
Zero-Shot Learning in Single-Cell Foundation Models: A Realistic Assessment of Capabilities and Limitations for Biomedical Research
This article provides a comprehensive analysis of the zero-shot capabilities of single-cell foundation models (scFMs), which are large-scale AI models pre-trained on millions of single-cell transcriptomes.
Single-Cell Foundation Models: Revolutionizing Drug Sensitivity Prediction in Precision Oncology
This article explores the transformative role of single-cell foundation models (scFMs) in predicting drug sensitivity, a critical challenge in precision medicine.
Decoding Cellular Logic: A Comprehensive Guide to Single-Cell Gene Regulatory Network Inference
This article provides a thorough exploration of gene regulatory network (GRN) inference from single-cell data, a key methodology for understanding the transcriptional programs that define cell identity and function.