Abhinav Adduri

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.

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.

A reasoning LLM combining ESM3 embeddings with GO-GPT term predictions produces protein function annotations that experts rated tie-or-better than UniProt in 79% of cases (SFT model).

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.

The paper introduces contextualized network inference to estimate sample-specific transcriptional networks for 7000 tumors, improving accuracy and enabling novel prognostic subtyping.

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.

The paper introduces MASPR, a machine learning method using protein language models to predict adenylation domain substrate specificities, enabling the prediction of hybrid NRP-PK natural products.

Ornaments improves allele-specific expression estimation by correcting mapping biases, outperforming existing tools in speed and accuracy.