All papers were published after joining Rutgers.
Five representative papers in toxicology
1.Ciallella H, Russo D, Aleksunes L M, Grimm F, Zhu H* Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach. Environ. Sci. Technol., 2021, In press
2. Russo D P, Strickland, J, Karmaus A L, Wang W, Shende S, Hartung T, Aleksunes L M, Zhu H* Non-animal models for acute toxicity evaluations: applying data-driven profiling and read-across. Environ. Health Perspect. 2019; (127) 47001.
3. Ciallella, H, Zhu H* Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity. Chem. Res. Tox. 2019; (32) 536−547.
4. Kim M, Huang R, Sedykh A, Zhang J, Xia M, Zhu H* Mechanism profiling liver toxicants by using antioxidant response element assay data model and public big data. Environ. Health Perspect. 2016; (124) 634-641.
5. Zhu H*, Zhang J, Kim M, Boison A, Sedykh A, Moran K Big Data in Chemical Toxicity Research: the Use of High-Throughput Screening Assays to Identify Potential Toxicants. Chem. Res. Tox. 2014; (27) 1643-1651
Five representative papers in other informatics areas
1. Yan X, Sedykh A, Wang W, Yan B, Zhu H* Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations. Nature Communication, 2020; (11) 2519
2. Wang W, Sedykh A, Sun H, Zhao L, Russo D P, Zhou H, Yan B, Zhu H* Predicting Nano-bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling. ACS Nano, 2017; (11) 12641-12649.
3. Zhang L, Tan J, Han D, Zhu H* From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov Today, 2017; (22) 1680-1685.
4. Russo D P, Kim M, Wang W, Pinolini D, Shende S, Strickland J, Hartung T, Zhu H* CIIPro: A new read-across portal to fill data gaps using public large scale chemical and biological data. Bioinformatics, 2017; (33) 464-466.
5. Wang W, Kim M, Sedykh A, Zhu H* Developing enhanced blood-brain barrier permeability models: Integrating external bio-assay data in QSAR modeling. Pharm. Res. 2015; (32) 3055-3065.
* indicates the corresponding author.