Lenore Cowen

This paper introduces DEFT, a method that combines sequence- and structure-based approaches to improve EC number prediction accuracy and enable high-throughput enzyme repertoire annotation.

The paper introduces GIDEON, an ILP framework to identify compensatory pathways in yeast genetic interaction networks, finding new BPM gene sets with potential antifungal drug target insights.

The paper develops NanoMAP, a pipeline for analyzing nanobody repertoires, showing it outperforms standard methods in identifying coherent clonal families and detecting high-affinity nanobodies.

PRECISE improves virtual screening by combining quantized drug representations with 3D target structures to predict binding sites and enable billion-scale screening.

This paper introduces CoNCISE, a method that accelerates drug-target interaction prediction by compressing small molecule data into 15 bits per compound, achieving high accuracy and enabling rapid screening of large chemical libraries.

PHILHARMONIC infers protein interaction networks and functional organization in non-model organisms using deep learning and spectral clustering, validated in Drosophila and coral symbionts.

This study examines transcriptomic responses to sediment stress in branching and massive corals across different locations, finding species-specific and morphological differences in gene expression.

This paper introduces a faster approximation of the IsoRank method for aligning large biological networks, achieving comparable accuracy with significantly reduced computational time.

MELISSA improves protein function prediction by integrating functional labels during embedding, outperforming existing methods in yeast and human networks.

The paper develops a method to identify homologous membrane receptors in non-model corals by combining remote homology detection and functional analysis, mapping over 1000 human receptors to coral receptors.

ConPLex predicts drug-target interactions using a deep learning model that combines pre-trained protein language models with contrastive co-embedding, achieving high accuracy and scalability.

This paper presents a deep learning approach using pre-trained protein language models for drug-target interaction prediction, achieving higher accuracy and faster training than existing methods.

This paper introduces CON-Plex, a drug-target interaction model that improves specificity by distinguishing drugs from decoys using contrastive learning.

D-SCRIPT predicts protein-protein interactions from sequence using a structure-aware deep learning model, improving cross-species generalization and functional characterization of proteins.

The paper evaluates module identification methods across disease-relevant networks using GWAS data to identify top-performing algorithms and disease-associated modules.