Predicting how a cell's transcriptome shifts when a gene is silenced: a solo entry to the Virtual Cell Challenge 2026, built in the open.
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Updated
Nov 5, 2026 - Python
Predicting how a cell's transcriptome shifts when a gene is silenced: a solo entry to the Virtual Cell Challenge 2026, built in the open.
Zero-shot CRISPRi perturbation response prediction in unseen cell lines, with evaluated uncertainty. Virtual Cell Challenge 2026 entry
What a genome-scale CRISPRi screen can and cannot discover. 50 Hallmark programs x 9,837 knockdowns in K562. Four evaluations, three negative, all four reported.
Unified genetic engineering platform: CRISPR, protein engineering, synthetic biology, gene therapy, genomics, metabolic engineering, AI/ML, biosecurity. 331 tests, TDD, OSS.
Adversarial-debate LLM agent for predicting CRISPRi perturbation effects on gene expression (Genentech MLGenX BioReasoning Challenge)
🌱 Designing CRISPRi experiments in bacteria
Computational design and adversarial validation of synthetic gene circuits targeting PDAC transcription factors, trained from scratch on real public genomics data
Reproducible uncertainty-aware multicriteria prioritization of MALAT1 CRISPRi guides in A549 cells.
Phenotypic screening of a CRISPRi strain library [Smith et 2017, Mol Syst Biol (2017)13:913] for acetic acid tolerance targeting essential and respiratory essential genes in Saccharomyces cerevisiae
Reproducible CRISPRi/Perturb-seq analysis of an ENTPD1/CD39 T-cell program in rheumatoid arthritis and methotrexate treatment
AI-assisted platform for designing CRISPR experiments — from target selection to bench-ready protocols
PyTorch classifier predicting CRISPRi guide identity from single-cell expression (Adamson et al. 2016 Perturb-seq)
Arc Institute Virtual Cell Challenge 2026 实战仓库:零样本预测 CRISPRi 扰动后的单细胞基因表达原始计数,包含竞赛资料、模型审计和可运行教程。
Figure code and data for Johnson et al. 2021, J Am Chem Soc 143(31):12003-12013, doi:10.1021/jacs.1c02622. CRISPRi chemical genomics identifying IleRS as the target of thiomarinol
A Triangulated Causal Machine Learning Framework Decodes Super-Enhancer Regulatory Networks and Actionable Vulnerabilities in Triple-Negative Breast Cancer
Repository containing the code and methods for Bosch & DeJesus et al. 2021
Virtual cell: predicting transcriptional responses to unseen gene knockdowns (PerturbFormer + conditional flow matching) - biomedical informatics course project
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