Yunan Luo

SynFit improves protein fitness prediction by learning shared and property-specific sequence representations, outperforming existing methods in multi-objective optimization.

EPERep enhances protein function annotation for remote homologs by using evolutionary input to improve prediction accuracy for underrepresented proteins.

This paper introduces DETANGO, a deep learning framework that disentangles mutation effects on protein stability and function, identifying stable-but-inactive variants and functionally critical residues.

The paper introduces SPURS, a deep learning framework for protein stability prediction that integrates generative models and achieves accurate, scalable, and generalizable results.

This paper introduces MSRep, a deep learning framework that improves protein function annotation by addressing class imbalance through neural collapse-inspired representations, achieving better performance for both common and rare function classes.

This paper introduces ConFit, a method to train protein language models with limited experimental data for fitness prediction, showing improved accuracy and efficiency in low-N learning scenarios.

This paper introduces GraNA, a deep learning framework for supervised biological network alignment that improves functional annotation transfer between species.

The paper identifies a molecular-level representation of odors that predicts receptor, neural, and behavioral responses across species and shows metabolic relationships in odor space.

PenLight improves protein annotation by integrating 3D structure and sequence data using graph neural networks and contrastive learning.

scPretrain improves cell type classification by using self-supervised learning with both annotated and unannotated cells across 60 datasets.

This paper presents a data-driven drug repositioning framework that identifies CVL218, a PARP1 inhibitor, as a potential therapeutic for COVID-19 through in silico and experimental validation.

cancerAlign improves tumor stratification by aligning tumors across cancer types using adversarial learning and consensus clustering, showing better results than methods that cluster within single cancer types.

This study identifies multiple SARS-CoV-2 strains within and across hosts using bulk RNA sequencing and consensus sequences, revealing within-host diversity, recombination, and geographic strain variation.

This paper introduces ECNet, a deep learning model that uses evolutionary context to predict protein variant fitness, outperforming existing methods on multiple datasets.

The paper introduces a meta-learning framework to predict phosphorylation sites for kinases with limited experimental data, showing improved transferability and accuracy.

Opal improves metagenomic binning by using low-density hashing to achieve faster and more accurate identification of sequencing read origins compared to existing methods.

DeepSignal predicts kinase and SH2 domain substrate specificity using deep learning, outperforming existing methods and identifying cancer-related mutations and pathways.

This paper introduces DTINet, a network integration method for predicting drug-target interactions and repositioning drugs, showing improved performance and validated novel COX inhibitor applications.