Yusuf Roohani

PIE: Generalizing perturbation effects across unseen perturbations, contexts and datasets

Predicting differentially expressed genes and expression shifts per condition from database embeddings plus per-gene evidence, PIE reaches 1.2–3.2 times the best baseline's AUPRC on unseen contexts, perturbations and datasets.

Predicting cellular responses to perturbation across diverse contexts with STATE

State, a set-based transformer trained on over 100 million perturbed cells, predicts perturbation responses in new cellular contexts better than mean, linear and deep learning baselines.

Stack: In-Context Learning of Single-Cell Biology

SO_SCPLOWTACKC_SCPLOW is a foundation model trained on human single-cell data that enables in-context learning for predicting perturbation effects without fine-tuning.

Scalable probe-based single-cell transcriptional profiling for virtual cell perturbation mapping and synthetic biology phenotyping

ProPer-seq is a cost-effective, barcode-free single-cell perturbation profiling method that enables scalable analysis of synthetic biology perturbations and reveals transcriptional control principles.

Biomni: A General-Purpose Biomedical AI Agent

An LLM agent with a literature-mined environment of 150 tools, 105 packages and 59 databases plans in code, matching reported human accuracy on database and sequence question answering.

scBaseCount: an AI agent-curated, uniformly processed, and autonomously updated single cell data repository

scBaseCount is a single-cell RNA sequencing database that automates metadata extraction and data processing using AI, offering a large, standardized collection of gene expression data across multiple organisms and tissues.

PreciCE: Precision engineering of cell fates via data-driven multi-gene control of transcriptional networks

The paper introduces a method to control cell fates by modulating multiple genes using CRISPR tools and machine learning, demonstrating its effectiveness in stem cell differentiation.

Towards Universal Cell Embeddings: Integrating Single-cell RNA-seq Datasets across Species with SATURN

SATURN integrates single-cell RNA-seq datasets across species using deep learning to create universal cell embeddings, enabling cross-species analysis and annotation transfer.

GEARS: Predicting transcriptional outcomes of novel multi-gene perturbations

GEARS predicts transcriptional responses to single and multi-gene perturbations using single-cell RNA-sequencing data and geometric deep learning, outperforming existing methods in identifying genetic interactions.