Search Results for author: Yaoyun Zhang

Found 13 papers, 1 papers with code

Domain Invariant Learning for Gaussian Processes and Bayesian Exploration

1 code implementation18 Dec 2023 Xilong Zhao, Siyuan Bian, Yaoyun Zhang, Yuliang Zhang, Qinying Gu, Xinbing Wang, Chenghu Zhou, Nanyang Ye

We further demonstrate the effectiveness of the DIL-GP Bayesian optimization method on a PID parameters tuning experiment for a quadrotor.

Bayesian Optimization Gaussian Processes

DiffDTM: A conditional structure-free framework for bioactive molecules generation targeted for dual proteins

no code implementations24 Jun 2023 Lei Huang, Zheng Yuan, Huihui Yan, Rong Sheng, Linjing Liu, Fuzhou Wang, Weidun Xie, Nanjun Chen, Fei Huang, Songfang Huang, Ka-Chun Wong, Yaoyun Zhang

However, molecule generation targeted for dual protein targets still faces formidable challenges including protein 3D structure data requisition for model training, auto-regressive sampling, and model generalization for unseen targets.

Revisiting Automatic Question Summarization Evaluation in the Biomedical Domain

no code implementations18 Mar 2023 Hongyi Yuan, Yaoyun Zhang, Fei Huang, Songfang Huang

To better understand whether commonly used evaluation metrics are capable of evaluating automatic summarization in the biomedical domain, we conduct human evaluations of summarization quality from four different aspects of a biomedical question summarization task.

Text Generation

Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling

no code implementations2 Feb 2023 Zheng Yuan, Yaoyun Zhang, Chuanqi Tan, Wei Wang, Fei Huang, Songfang Huang

To alleviate this limitation, we propose Moleformer, a novel Transformer architecture that takes nodes (atoms) and edges (bonds and nonbonding atom pairs) as inputs and models the interactions among them using rotational and translational invariant geometry-aware spatial encoding.

Graph Neural Network Initial Structure to Relaxed Energy (IS2RE), Direct

COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model

no code implementations13 Jul 2020 Jingqi Wang, Noor Abu-el-rub, Josh Gray, Huy Anh Pham, Yujia Zhou, Frank Manion, Mei Liu, Xing Song, Hua Xu, Masoud Rouhizadeh, Yaoyun Zhang

To this end, this study aims at adapting the existing CLAMP natural language processing tool to quickly build COVID-19 SignSym, which can extract COVID-19 signs/symptoms and their 8 attributes (body location, severity, temporal expression, subject, condition, uncertainty, negation, and course) from clinical text.


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