Nathaniel Diamant

The paper introduces SMFNet and PRISM to analyze how transcription factor assemblies influence chromatin accessibility and identifies sequence-encoded principles of combinatorial TF assembly.

The paper introduces SIGnature, a framework for scoring gene importance using attributions from scRNA-seq foundation models, and demonstrates its utility in identifying novel associations of the MS1 signature with hyperinflammatory conditions.

The paper introduces Decima, a model that predicts cell type- and condition-specific gene expression from DNA sequence using single-cell RNA sequencing data.

This paper introduces TxConformal, a framework that controls false discoveries in AI-driven therapeutic discovery by adjusting for distribution shifts and providing confidence measures for AI scores.

PaSCient learns patient-level disease representations from scRNA-seq data, enabling disease classification and downstream analyses like dimensionality reduction and patient subgroup discovery.

Nona is a multimodal masking framework that unifies sequence-to-function models, language models, and generative models for functional genomics, improving predictions and revealing privacy vulnerabilities in ATAC-seq data.

This paper introduces EVFI and DeepEVFI, machine learning methods that infer variant fitness from directed evolution data, identifying tighter binders missed by conventional approaches.

This study combines high-throughput screening with deep learning to identify novel antibacterial compounds, achieving a 90X higher hit rate than traditional methods.

The paper introduces SCimilarity, a metric learning framework for querying and comparing single-cell RNA-seq data across the human body, revealing commonalities in fibrosis-associated macrophages.