MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning
X Tang, A Zou, Z Zhang, Y Zhao, X Zhang, A Cohan, M Gerstein (2024). In Findings of the Association for Computational Linguistics: ACL

Struc-Bench: Are Large Language Models Really Good at Generating Complex Structured Data?
X Tang, Y Zong, J Phang, Y Zhao, W Zhou, A Cohan, M Gerstein (2024). In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies Short Papers: 12–34

ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Lauren Hong, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, Maosong Sun (2024). The Twelfth International Conference on Learning Representations (ICLR 2024).

Investigating Data Contamination in Modern Benchmarks for Large Language Models
C Deng, Y Zhao, X Tang, M Gerstein, A Cohan. (2024). In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Volume 1 (Long Papers): 8706–8719

GersteinLab at MEDIQA-Chat 2023: Clinical Note Summarization from Doctor-Patient Conversations through Fine-tuning and In-context Learning
Xiangru Tang, Andrew Tran, Jeffrey Tan, Mark Gerstein (2023). Proceedings of the 5th Clinical Natural Language Processing Workshop.

Aligning factual consistency for clinical studies summarization through reinforcement learning
Xiangru Tang, Arman Cohan, Mark Gerstein (2023). Proceedings of the 5th Clinical Natural Language Processing Workshop.

Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering
T Li, H Guo, F Grazioli, MB Gerstein, MR Min (2023). NeurIPS 2023.

Privacy-preserving Model Training for Disease Prediction Using Federated Learning with Differential Privacy.
A Khanna, V Schaffer, G Gursoy, M Gerstein (2022). Annu Int Conf IEEE Eng Med Biol Soc 2022: 1358-1361.

Hierarchical PAC-Bayes Bounds via Deep Probabilistic Programming
J Warrell, MB Gerstein (2019). Bayesian Deep Learning Workshop at NeurIPS.

Knowledge Based Factorized High Order Sparse Learning Models
S Purushotham, MR Min, CJ Kuo, M Gerstein (2015). NIPS Workshop on Machine Learning in Computational Biology.

Proposed social and technological solutions to issues of data privacy in personal genomics
D Greenbaum, A Harmanci, M Gerstein (2014). IEEE IEEE International Symposium on Ethics in Science, Technology and Engineering.

Ensemble Learning Based Sparse High-Order Boltzmann Machine for Unsupervised Feature Interaction Identification
MR Min, X Ning, Y Qi, C Cheng, A Bonner, M Gerstein (2014). NIPS Workshop on Machine Learning in Computational Biology.

Interpretable Sparse High-Order Boltzmann Machines
MR Min, X Ning, C Cheng, M Gerstein (2014). JMLR W&CP 33:614-622 (AISTATS 2014).

Interpretable Sparse High-Order Boltzmann Machines for Transcription Factor Interaction Identification
MR Min, X Ning, C Cheng, M Gerstein (2013). NIPS Workshop on Machine Learning in Computational Biology.

Inferring Protein-Protein Interactions Using Interaction Network Topologies
A Paccanaro, V Trifonov, H Yu, M Gerstein (2005). International Joint Conference on Neural Networks (IJCNN, Jul. 31-Aug. 4, Montreal, Canada), pages 161 - 166, vol. 1

An XML-Based Approach to Integrating Heterogeneous Yeast Genome Data
KH Cheung, D Pan, A Smith, M Seringhaus, SM Douglas, M Gerstein (2004). International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences (METMBS); pp 236-242

Using iterative dynamic programming to obtain accurate pairwise and multiple alignments of protein structures.
M Gerstein, M Levitt (1996). Proc Int Conf Intell Syst Mol Biol 4: 59-67.

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