Author

Yihui Li

McGill UniversityORCID 0000-0003-3727-186XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 2

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2

Frequent Co-Authors

Amin EmadiD· 2 shared David Earl HostalleroiD· 2 shared

Activity by Year

2022-2023 · peak 1/yr

Co-Author Collaboration Network

2 top collaborators · 1 mutual co-author link

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Amin EmadORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

BioinformaticsBioinformaticsPeer-reviewedJun 1, 2023

Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?

Yihui Li, David Earl Hostallero, Amin Emad

This study evaluates interpretable deep learning models for drug response prediction, finding that explicit pathway integration reduces accuracy and that black-box models often perform best.

28 citationstop 5%

deep learningdrug response / drug discovery

𝕏 PostBluesky

BioinformaticsBioinformaticsPeer-reviewedJun 8, 2022

Looking at the BiG picture: Incorporating bipartite graphs in drug response prediction

David Earl Hostallero, Yihui Li, Amin Emad

The paper introduces two deep learning models for drug response prediction that use bipartite graphs to improve accuracy by incorporating drug structure and cell line relationships.

28 citations

deep learningdrug response / drug discovery

𝕏 PostBluesky