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2025

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D-Index
36
Citations
7937
World Ranking
785
National Ranking
42

Computer Science

D-Index
41
Citations
8837
World Ranking
8710
National Ranking
529

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Cao Xiao is a researcher affiliated with General Electric (United Kingdom) based in the United Kingdom. Their primary field of study is Computer Science, with a significant number of publications centered on Artificial Intelligence, molecular biology, computational theory and mathematics, materials chemistry, and radiology, nuclear medicine, and imaging.

The scientist's work spans several key topics within the intersection of technology and biomedical research. These include:

  • Machine Learning in Healthcare
  • Computational Drug Discovery Methods
  • Topic Modeling
  • Biomedical Text Mining and Ontologies
  • Machine Learning in Materials Science
  • Artificial Intelligence in Healthcare and Education
  • Artificial Intelligence in Healthcare

Cao Xiao has contributed to numerous publications, with frequent appearances in venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Patterns
  • Bioinformatics
  • Journal of the American Medical Informatics Association

Among recent papers associated with their research activity are:

  • MolTrans: Molecular Interaction Transformer for drug-target interaction prediction, 2020, Bioinformatics
  • Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review, 2020, Computers in Biology and Medicine
  • DeepPurpose: a deep learning library for drug-target interaction prediction, 2020, Bioinformatics
  • Artificial intelligence foundation for therapeutic science, 2022, Nature Chemical Biology
  • SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization, 2021, Bioinformatics

Throughout their research career, Cao Xiao has collaborated frequently with other scientists, including:

  • Jimeng Sun
  • Lucas M. Glass
  • Tianfan Fu
  • Kexin Huang
  • Brandon Theodorou

Best Publications

  • FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

    Jie Chen;Tengfei Ma;Cao Xiao

  • Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.

    Cao Xiao;Edward Choi;Jimeng Sun

  • Patient Subtyping via Time-Aware LSTM Networks

    Inci M. Baytas;Cao Xiao;Xi Zhang;Fei Wang

  • MolTrans: Molecular interaction transformer for drug target interaction prediction

    Kexin Huang;Cao Xiao;Lucas M Glass;Jimeng Sun

  • Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review.

    Shenda Hong;Yuxi Zhou;Junyuan Shang;Cao Xiao

  • DeepPurpose: a deep learning library for drug-target interaction prediction.

    Kexin Huang;Tianfan Fu;Lucas M Glass;Marinka Zitnik

  • Pre-training of Graph Augmented Transformers for Medication Recommendation

    Junyuan Shang;Tengfei Ma;Cao Xiao;Jimeng Sun

  • GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination

    Junyuan Shang;Cao Xiao;Tengfei Ma;Hongyan Li

  • Detecting Clusters of Fake Accounts in Online Social Networks

    Cao Xiao;David Mandell Freeman;Theodore Hwa

  • Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders

    Tengfei Ma;Cao Xiao;Jiayu Zhou;Fei Wang

  • MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare

    Edward Choi;Cao Xiao;Walter F. Stewart;Jimeng Sun

  • HiTANet: Hierarchical Time-Aware Attention Networks for Risk Prediction on Electronic Health Records

    Junyu Luo;Muchao Ye;Cao Xiao;Fenglong Ma

  • Data-Driven Subtyping of Parkinson's Disease Using Longitudinal Clinical Records: A Cohort Study.

    Xi Zhang;Jingyuan Chou;Jian Liang;Cao Xiao

  • SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization.

    Yue Yu;Kexin Huang;Chao Zhang;Lucas M Glass

  • Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development

    Kexin Huang;Tianfan Fu;Wenhao Gao;Yue Zhao

  • CASTER: Predicting Drug Interactions with Chemical Substructure Representation

    Kexin Huang;Cao Xiao;Trong Nghia Hoang;Lucas M. Glass

  • An RNN Architecture with Dynamic Temporal Matching for Personalized Predictions of Parkinson's Disease.

    Chao Che;Cao Xiao;Jian Liang;Bo Jin

  • Readmission prediction via deep contextual embedding of clinical concepts.

    Cao Xiao;Tengfei Ma;Adji Bousso Dieng;David Meir Blei

  • SkipGNN: predicting molecular interactions with skip-graph networks.

    Kexin Huang;Cao Xiao;Lucas M. Glass;Marinka Zitnik

  • STAN: spatio-temporal attention network for pandemic prediction using real-world evidence.

    Junyi Gao;Rakshith Sharma;Cheng Qian;Lucas M Glass

  • Unsupervised Sequential Outlier Detection With Deep Architectures

    Weining Lu;Yu Cheng;Cao Xiao;Shiyu Chang

  • Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders

    Tengfei Ma;Jie Chen;Cao Xiao

Frequent Co-Authors

Jimeng Sun
Jimeng Sun University of Illinois at Urbana-Champaign
M. Brandon Westover
M. Brandon Westover Harvard University
Marinka Zitnik
Marinka Zitnik Harvard University
Jiayu Zhou
Jiayu Zhou Michigan State University
Wanpracha Art Chaovalitwongse
Wanpracha Art Chaovalitwongse University of Arkansas at Fayetteville
Nicholas D. Sidiropoulos
Nicholas D. Sidiropoulos University of Virginia
Leman Akoglu
Leman Akoglu Carnegie Mellon University
Justin Romberg
Justin Romberg Georgia Institute of Technology
Jure Leskovec
Jure Leskovec Stanford University

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