Yasha Ektefaie

This paper introduces AutoSPECTRA, a protocol for auditing generalizability of scientific foundation models across biological domains.

This paper evaluates active learning strategies for whole-cell antibiotic discovery, finding an optimal approach that significantly improves hit rates and generalizability across bacterial species.

Fleming is an AI agent that identifies lead compounds for Mycobacterium tuberculosis by combining discriminative and generative models, achieving higher discrimination and hit rates than alternative approaches.

This paper shows that standard protein language models do not support evolutionary reasoning, but a new model trained with phylogenetic supervision performs better on tree reconstruction and clustering.

This paper introduces a multimodal foundation model for predicting protein phenotypes using sequence, structure, and natural language, showing competitive performance and novel insights into disease-related proteins.

This paper evaluates how well deep learning models generalize to new molecular sequences using a new framework called SPECTRA across 18 datasets and 19 models.

This study benchmarks zero-shot single-cell foundation model embeddings for perturbation effect prediction and finds they do not consistently improve over baseline models under distribution shift.

This paper presents a deep convolutional neural network to predict antibiotic resistance in M. tuberculosis isolates, achieving state-of-the-art accuracy and enabling interpretable feature analysis.

This study analyzes global M. tuberculosis whole genome sequences to trace the temporal and geographic patterns of drug resistance acquisition and its correlation with country wealth.