Author

Yu‐Chih Chen

University of PittsburghORCID 0000-0002-3875-4671Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioengineering· 1 Bioinformatics· 1 Developmental Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:bulk RNA-seq· 2 Research Focus:clustering· 2 Research Focus:differential expression· 2 Research Focus:drug response / drug discovery· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Adrian V. LeeiD· 1 shared Austin Kokoruda· 1 shared Chien-Hung ShihiD· 1 shared Cristina CebriániD· 1 shared Daniel D. BrowniD· 1 shared Edgar A. OttoiD· 1 shared Euisik YooniD· 1 shared Haiyi Mao· 1 shared

Activity by Year

2018-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 16 mutual co-author links

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1 shared paperBioengineering

Adrian V. LeeORCID

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3 papers

Recent·Most cited

High-Throughput Empirical and Virtual Screening to Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer

Yushu Ma, Chien-Hung Shih, Jinxiong Cheng, Hsiao-Chun Chen, Li‐Ju Wang, Yanhao Tan, Yuan Zhang, Daniel D. Brown, Steffi Oesterreich, Adrian V. Lee, Yu‐Chiao Chiu, Yu‐Chih Chen

This paper develops a high-throughput screening method to identify drugs targeting polyploid giant cancer cells in breast cancer, combining empirical assays with a machine learning model for virtual screening.

Analytical Chemistry · Mar 5, 2025Peer-reviewedBioengineering

LaGrACE: Estimating gene program dysregulation using latent gene regulatory network for biomedical discovery

Minxue Jia, Haiyi Mao, Mengli Zhou, Yu‐Chih Chen, Panayiotis V. Benos

LaGrACE estimates gene program dysregulation using omics data and clinical information to identify molecular subtypes and drug-response signals in diseases.

bioRxiv · Apr 29, 2024 · PreprintBioinformatics

Single-cell analysis of progenitor cell dynamics and lineage specification of the human fetal kidney

Rajasree Menon, Edgar A. Otto, Austin Kokoruda, Jian Zhou, Zidong Zhang, Euisik Yoon, Yu‐Chih Chen, Olga G. Troyanskaya, Jason R. Spence, Matthias Kretzler, Cristina Cebrián

This study uses single-cell transcriptomics to analyze human fetal kidney development, identifying 17 cell clusters and their lineage trajectories.

Development · Aug 15, 2018Peer-reviewedDevelopmental Biology