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How Much Can AI Virtual Cells Gain from Unified Perturbational Modeling?

This paper evaluates how unified perturbational modeling improves AI virtual cell predictors by integrating heterogeneous Perturb-seq datasets and achieving better performance across benchmarks.

A Generative Virtual Tissue Model Enables Computational Design of Therapeutic Perturbation Strategies

This paper introduces CIFM, a generative virtual tissue model that simulates transcriptional dynamics under therapeutic conditions to design immunotherapy strategies for cancer and autoimmune diseases.

DigiAra Computationally Designs Plant Mutants for Resistance to Microbial Infection in Arabidopsis

DigiAra designs Arabidopsis mutants with enhanced microbial resistance using a simulation-scoring-screening pipeline and hybrid gene interaction modeling.

Building Foundation Models to Characterize Cellular Interactions via Geometric Self-Supervised Learning on Spatial Genomics

This paper introduces CI-FM, a foundation model for analyzing cellular interactions in spatial genomics using geometric graph neural networks, achieving high correlation and low error in gene expression inference.

Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project

The paper evaluates variant prioritization methods for rare disease diagnosis using data from the Rare Genomes Project, finding that top models identify causal variants in most solved families and uncover potential candidates in unsolved ones.

Does Inter-Protein Contact Prediction Benefit from Multi-Modal Data and Auxiliary Tasks?

The paper investigates whether multi-modal data and auxiliary tasks improve inter-protein contact prediction, finding that multi-modal inputs enhance precision but auxiliary tasks have limited benefits.

Cross-Modality and Self-Supervised Protein Embedding for Compound-Protein Affinity and Contact Prediction

This paper introduces cross-modality and self-supervised learning for protein embeddings to improve compound-protein affinity and contact prediction.

Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction

This study predicts compound-protein affinity and contact using cross-modality protein embeddings, showing improved performance over single-modality approaches.