Srivatsan Raghavan

Single-cell perturbation prediction near experimental reproducibility with GENESIS

GENESIS predicts single-cell perturbation responses from experimental conditions, outperforming existing models and approaching experimental reproducibility.

Evaluating the role of pre-training dataset size and diversity on single-cell foundation model performance

This study evaluates how pre-training dataset size and diversity affect single-cell foundation model performance on zero-shot and fine-tuned tasks using 22.2 million cells.

Beyond alignment: synergistic integration is required for multimodal cell foundation models

This paper introduces a metric to evaluate synergistic integration in multimodal cell models and shows when it benefits tasks over unimodal approaches.

Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data

This paper introduces NCLUSION, a nonparametric clustering method for scRNA-seq that identifies marker genes and clusters simultaneously, showing improved performance and reduced runtime.

Adaptive resampling for improved machine learning in imbalanced single-cell datasets

This paper introduces an adaptive resampling method to improve machine learning performance on imbalanced single-cell datasets, showing better results in gene expression reconstruction, cell type classification, and perturbation response prediction.

Generation of a biliary tract cancer cell line atlas reveals molecular subtypes and therapeutic targets

This study creates a biliary tract cancer cell line atlas using multi-omics data to identify molecular subtypes and therapeutic targets.

Hierarchical cross-entropy loss improves atlas-scale single-cell annotation models

This paper introduces a hierarchical cross-entropy loss to improve single-cell annotation models by aligning them with biological hierarchies, achieving better out-of-distribution performance.

A knockoff calibration method to avoid over-clustering in single-cell RNA-sequencing

This paper introduces a method to prevent over-clustering in scRNA-seq by controlling double-dipping, improving differential expression analysis accuracy.

Consequences of training data composition for deep learning models in single-cell biology

This study examines how training data composition affects deep learning models in single-cell transcriptomics, finding poor generalization to new cell types and benefits from including embryonic stem cell differentiation data.

Mutation and cell state compatibility is required and targetable in Ph+ acute lymphoblastic leukemia minimal residual disease

This study identifies mutation and transcriptional state compatibility in Ph+ ALL MRD, showing that targeting these interactions can overcome BCR::ABL1 independence and improve MRD monitoring.

Compressed phenotypic screens for complex multicellular models and high-content assays

The paper introduces a method to compress phenotypic screens using computational deconvolution, demonstrating increased efficiency and applicability to complex multicellular models.