Research Articles
Validating Gene-Gene Relationship Predictions: From Computational Models to Biological Discovery
This article provides a comprehensive roadmap for researchers and drug development professionals on validating computationally predicted gene-gene relationships.
Beyond Single Samples: A Framework for Assessing and Ensuring Generalizability Across Tissue Types in Biomedical Research
The ability of computational models and analytical frameworks to generalize across diverse tissue types is a critical benchmark for their clinical and research utility.
Beyond the Hype: A Practical Framework for Assessing Biological Relevance in Single-Cell Foundation Model Latent Spaces
Single-cell foundation models (scFMs) promise to revolutionize biological discovery by learning universal representations from vast transcriptomic datasets.
Single-Cell Foundation Models Architectures Compared: A 2025 Guide for Biomedical Researchers
Single-cell foundation models (scFMs) are revolutionizing biological research by providing unified AI frameworks for analyzing cellular heterogeneity.
Evaluating Zero-Shot Single-Cell Foundation Model Embeddings: A Comprehensive Guide for Biomedical Researchers
Single-cell Foundation Models (scFMs) promise to revolutionize biological discovery by providing powerful, pre-trained representations of single-cell RNA sequencing data.
Benchmarking scGPT vs. scFoundation: A Comprehensive Performance Analysis for Single-Cell Biology
This article provides a systematic evaluation of two leading single-cell foundation models, scGPT and scFoundation, based on the latest benchmarking studies.
Why Your Perturbation Predictions Fail: A Troubleshooting Guide from Benchmarks to Breakthroughs
Accurately predicting cellular responses to genetic or chemical perturbations is crucial for drug discovery and therapeutic target identification.
Building Robust Data Preprocessing Pipelines for Single-Cell Foundation Model Training
This article provides a comprehensive guide for researchers and bioinformaticians on constructing effective data preprocessing pipelines for single-cell Foundation Model (scFM) training.
Mastering scGPT Hyperparameter Tuning: A Practical Guide for Enhanced Single-Cell Analysis
This comprehensive guide provides researchers, scientists, and drug development professionals with advanced strategies for hyperparameter optimization when fine-tuning scGPT, a foundational generative AI model for single-cell transcriptomics.
Optimizing Highly Variable Gene Selection for Single-Cell Foundation Model Training: A Comprehensive Guide
Selecting highly variable genes (HVGs) is a critical preprocessing step that profoundly impacts the performance and biological relevance of single-cell foundation models (scFMs).