Author

Amanda Zeng

Washington University in St. Louis

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Bioinformatics· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:bulk RNA-seq· 2 Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Organism:human· 2 Research Focus:cell-cell communication· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1

Frequent Co-Authors

Fuhai LiiD· 3 shared Jiarui FengiD· 3 shared Philip PayneiD· 3 shared David G. DeNardoiD· 1 shared Heming ZhangiD· 1 shared Li DingiD· 1 shared Ryan Courtney FieldsiD· 1 shared Simon Peter GoedegebuureiD· 1 shared

Activity by Year

2020-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

Click any collaborator node to inspect or filter shared papers

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

Curved chords = mutual co-authorship

3 shared papersBioinformatics

Fuhai LiORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

SC2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data

Jiarui Feng, Simon Peter Goedegebuure, Amanda Zeng, Ye Bi, Ting Wang, Philip Payne, Li Ding, David G. DeNardo, William G. Hawkins, Ryan Courtney Fields, Fuhai Li

SC2MeNetDrug is a tool that identifies cell-cell signaling targets and predicts drugs using scRNA-seq data to analyze disease microenvironments.

PLoS Computational Biology · Jan 5, 2024Peer-reviewedBioinformatics

Signaling interaction link prediction using deep graph neural networks integrating protein-protein interactions and omics data

Jiarui Feng, Amanda Zeng, Yixin Chen, Philip Payne, Fuhai Li

The paper introduces a deep learning model to predict signaling cascades by integrating PPI data and omics data, validated using curated KEGG pathways.

bioRxiv · Dec 23, 2020 · PreprintBioinformatics

Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model

Heming Zhang, Jiarui Feng, Amanda Zeng, Philip Payne, Fuhai Li

This study introduces DeepSignalingSynergy, a simplified deep learning model that predicts tumor cell response to drug combinations using cancer signaling pathways, showing feasibility with NCI ALMANAC data.

bioRxiv · Apr 10, 2020 · PreprintBioinformatics