5 papers
VCLMU: Mechanism-Centric Virtual Cell World Modeling for Perturbation Response
Yuwei Miao, Azim Dehghani Amirabad, Scott Oloff +3
Predicting cellular responses to genetic perturbations is a central capability for virtual cells and a key step toward computational modeling of biological interventions. Most exis…
Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression
Alberto Caron, Tianyu Cui, Dmytro S. Lituiev +13
Codon optimization, the process of selecting synonymous codons to improve mRNA translation efficiency and protein expression, is central to therapeutic protein production and mRNA…
BioBO: Biology-informed Bayesian Optimization for Perturbation Design
Yanke Li, Tianyu Cui, Tommaso Mansi +2
Efficient design of genomic perturbation experiments is crucial for accelerating drug discovery and therapeutic target identification, yet exhaustive perturbation of the human geno…
Geometric Hyena Networks for Large-scale Equivariant Learning
Artem Moskalev, Mangal Prakash, Junjie Xu +3
Processing global geometric context while preserving equivariance is crucial when modeling biological, chemical, and physical systems. Yet, this is challenging due to the computati…
InfoSEM: A Deep Generative Model with Informative Priors for Gene Regulatory Network Inference
Tianyu Cui, Song-Jun Xu, Artem Moskalev +4
Inferring Gene Regulatory Networks (GRNs) from gene expression data is crucial for understanding biological processes. While supervised models are reported to achieve high performa…