Ping Zhang  —  Publications

2024

• SepsisLab: Early sepsis prediction with uncertainty quantification and active sensing
  Changchang Yin, Pin-Yu Chen, Bingsheng Yao, Dakuo Wang, Jeffrey Caterino, Ping Zhang
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024 (Acceptance rate: 141/738 = 19.1%, applied data science track, oral presentation. Selected Media Coverage: Ohio State News, Tech Explorist, Inside Precision Medicine, Scientific Inquirer, News Medical, HCPLive, The Microbiologist, Medical Dialogues, WSYX “ABC 6”, Columbus Business First)
  

• SSM-Net: Semi-supervised multi-task network for joint lesion segmentation and classification from pancreatic EUS images
  Jiajia Li, Pingping Zhang, Xia Yang, Lei Zhu, Teng Wang, Ping Zhang, Ruhan Liu, Bin Sheng, Kaixuan Wang
  Artificial Intelligence In Medicine (AIIM), 2024 (Impact factor: 7.5)
  

• Use of deep learning to identify peripheral arterial disease cases from narrative clinical notes
  Shantanu Dev, Andrew Zolensky, Hanaa Dakour Aridi, Catherine Kelty, Mackenzie K. Madison, Anush Motaganahalli, Benjamin S. Brooke, Brian Dixon, Malaz Boustani, Zina Ben Miled, Ping Zhang, Andrew A. Gonzalez
  Journal of Surgical Research, 303:699–708, 2024
  

• Applications of artificial intelligence to myeloproliferative neoplasms: A narrative review
  Andrew Srisuwananukorn, Jordan Krull, Qin Ma, Ping Zhang, Alexander Pearson, Ronald Hoffman
  Expert Review of Hematology, 2024
  

• Using multiple drug similarity networks to promote adverse drug event detection
  Biswajit Padhi, Ruoqi Liu, Yuedi Yang, Xueqiao Peng, Lang Li, Pengyue Zhang, Ping Zhang
  Heliyon, 10:e39728, 2024
  

• Machine learning in spine oncology: A narrative review
  Seth B. Wilson, Jacob Ward, Vikas Munjal, Chi Shing Adrian Lam, Mayur Patel, Ping Zhang, David S. Xu, Vikram B. Chakravarthy
  Global Spine Journal, 2024
  

• HRDC challenge: A public benchmark for hypertension and hypertensive retinopathy classification from fundus images
  Bo Qian, Xiangning Wang, Zhouyu Guan, Dawei Yang, Anran Ran, Tingyao Li, Zheyuan Wang, Yang Wen, Xinming Shu, Jinyang Xie, Shichang Liu, Guanyu Xing, Julio Silva-Rodriguez, Riadh Kobbi, Ping Li, Tingli Chen, Lei Bi, Jinman Kim, Weiping Jia, Huating Li, Jing Qin, Ping Zhang, Ching-Yu Cheng, Pheng-Ann Heng, Tien Yin Wong, Carol Y. Cheung, Yih-Chung Tham, Nadia Magnenat Thalmann, Bin Sheng
  The Visual Computer, 2024
  

• DDE: Deep dynamic epidemiological modeling for infectious illness development forecasting in multi-level geographic entities
  Ruhan Liu, Jiajia Li, Yang Wen, Huating Li, Ping Zhang, Bin Sheng, David Dagan Feng
  Journal of Healthcare Informatics Research, 2024 (Impact factor: 5.9)
  

• Predictive modeling with temporal graphical representation on electronic health records
  Jiayuan Chen, Changchang Yin, Yuanlong Wang, Ping Zhang
  International Joint Conference on Artificial Intelligence (IJCAI), 2024 (Acceptance rate: 791/5651 = 14.0%, Main track, oral presentation)
  

• Non-stationary domain generalization: Theory and algorithm
  Thai-Hoang Pham, Xueru Zhang, Ping Zhang
  The Conference on Uncertainty in Artificial Intelligence (UAI), 2024 (Acceptance rate: 201/744 = 27.0%)
  

• CURE: A deep learning framework pre-trained on large-scale patient data for treatment effect estimation
  Ruoqi Liu, Pin-Yu Chen, Ping Zhang
  Patterns 5:100973, 2024 (Impact factor: 6.5. Preview on Patterns. Selected Media Coverage: Ohio State News, Clinical Research News, HealthITAnalytics, AZoRobotics)
  

• Rethinking human-AI collaboration in complex medical decision making: A case study in sepsis diagnosis
  Shao Zhang, Jianing Yu, Xuhai Xu, Changchang Yin, Yuxuan Lu, Bingsheng Yao, Melanie Tory, Lace M Padilla, Jeffrey Caterino, Ping Zhang, Dakuo Wang
  ACM CHI Conference on Human Factors in Computing Systems (CHI), 2024 (Acceptance rate: 1060/4028 = 26.3%)
  

• KG-TREAT: Pre-training for treatment effect estimation by synergizing patient data with knowledge graphs
  Ruoqi Liu, Lingfei Wu, Ping Zhang
  AAAI Conference on Artificial Intelligence (AAAI), 2024 (Acceptance rate: 2342/9862 = 23.7%, main track, oral presentation)
  

• Stimulant drugs and stimulant use disorder
  Wenyu Song, Aurelien Simona, Ping Zhang, David Bates, Richard Urman
  Anesthesiology Clinics, 42(1):103–115, 2024
  

• Multimodal risk prediction with physiological signals, medical images and clinical notes
  Yuanlong Wang, Changchang Yin, Ping Zhang
  Heliyon, 10:e26772, 2024
  

• DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images
  Bo Qian, Hao Chen, Xiangning Wang, Zhouyu Guan, Tingyao Li, Yang Wen, Haoxuan Che, Gitaek Kwon, Jaeyoung Kim, Sungjin Choi, Seoyoung Shin, Felix Krause, Markus Unterdechler, Junlin Hou, Rui Feng, Yihao Li, Mostafa El Habib Daho, Dawei Yang, Qiang Wu, Ping Zhang, Xiaokang Yang, Yiyu Cai, Gavin Siew Wei Tan, Carol Y Cheung, Wei-Ping Jia, Huating Li, Yih Chung Tham, Tien Yin Wong, Bin Sheng
  Patterns 5:100929, 2024 (Impact factor: 6.5. DRAC, a UW-OCTA dataset for diabetic retinopathy)
  

2023

• Heterogeneous treatment effect estimation with subpopulation identification for personalized medicine in opioid use disorder
  Seungyeon Lee, Ruoqi Liu, Wenyu Song, Ping Zhang
  IEEE International Conference on Data Mining (ICDM), 2023 (Acceptance rate: 200/1003 = 19.9%, short paper, oral presentation)
  

• The application of artificial intelligence in the management of sepsis
  Jie Yang, Sicheng Hao, Jiajie Huang, Tianqi Chen, Ruoqi Liu, Ping Zhang, Mengling Feng, Yang He, Wei Xiao, Yucai Hong, Zhongheng Zhang
  Medical Review 3(5):369–380, 2023.

• Stable clinical risk prediction against distribution shift in electronic health records
  Seungyeon Lee, Changchang Yin, Ping Zhang
  Patterns 4:100828, 2023 (Impact factor: 6.5)
  

• Human-centered design and evaluation of AI-empowered clinical decision support systems: a systematic review
  Liuping Wang, Zhan Zhang, Dakuo Wang, Weidan Cao, Xiaomu Zhou, Ping Zhang, Jianxing Liu, Xiangmin Fan, Feng Tian
  Frontiers in Computer Science 5:1187299, 2023
  

• Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis
  Ruoqi Liu, Katherine Hunold, Jeffrey Caterino, Ping Zhang
  Nature Machine Intelligence 5:421–431, 2023 (Impact factor: 25.898. Selected Media Coverage: Ohio State News, HealthITAnalytics, MIT Technology Review Chinese Edition. Featured on AMIA 2024 Informatics Summit Artificial Intelligence and Data Science Year-in-Review)
  

• Fairness and accuracy under domain generalization
  Thai-Hoang Pham, Xueru Zhang, Ping Zhang
  International Conference on Learning Representations (ICLR), 2023 (Acceptance rate: 1564/4922 = 31.8%)
  

• TMM-Nets: Transferred multi- to mono-modal generation for lupus retinopathy diagnosis
  Ruhan Liu, Tianqin Wang, Huating Li, Ping Zhang, Jing Li, Xiaokang Yang, Dinggang Shen, Bin Sheng
  IEEE Transactions on Medical Imaging (TMI) 42(4):1083–1094, 2023 (Impact factor: 11.037)
  

• A fair and interpretable network for clinical risk prediction: A regularized multi-view multi-task learning approach
  Thai-Hoang Pham, Changchang Yin, Laxmi Mehta, Xueru Zhang, Ping Zhang
  Knowledge and Information Systems (KAIS) 65:1487–1521, 2023
  

• Data imputation for clinical trial emulation: A case study on impact of intracranial pressure monitoring for traumatic brain injury
  Zhizhen Zhao, Ruoqi Liu, Jonathan Groner, Henry Xiang, Ping Zhang
  American Medical Informatics Association Informatics Summit (AMIA Summit), 2023
  

2022

• DREAM: Domain invariant and contrastive representation for sleep dynamics
  Seungyeon Lee, Thai-Hoang Pham, Ping Zhang
  IEEE International Conference on Data Mining (ICDM), 2022 (Acceptance rate: 174/870 = 20.0%, short paper, oral presentation)
  

• Deconfounding actor-critic network with policy adaptation for dynamic treatment regimes
  Changchang Yin, Ruoqi Liu, Jeffrey Caterino, Ping Zhang
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022 (Acceptance rate: 254/1695 = 15.0%, research track, oral presentation)
  

• Predicting age-related macular degeneration progression with contrastive attention and time-aware LSTM
  Changchang Yin, Sayoko Moroi, Ping Zhang
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022 (Acceptance rate: 195/753 = 25.9%, applied data science track, oral presentation)
  

• Review of applied health informatics courses in a multidisciplinary biomedical informatics department
  Tasneem Motiwala, Ping Zhang, Megan Gregory, Naleef Fareed, Xia Ning, Kevin Coombes, Gabrielle Kokanos, Courtney Hebert
  Learning Health Systems 6(4):e10336, 2022
  

• DeepDRiD: Diabetic retinopathy—grading and image quality estimation challenge
  Ruhan Liu, Xiangning Wang, Qiang Wu, Ling Dai, Xi Fang, Tao Yan, Jaemin Son, Shiqi Tang, Jiang Li, Zijian Gao, Adrian Galdran, J.M. Poorneshwaran, Hao Liu, Jie Wang, Yerui Chen, Prasanna Porwal, Gavin Siew Wei Tan, Xiaokang Yang, Chao Dai, Haitao Song, Mingang Chen, Huating Li, Weiping Jia, Dinggang Shen, Bin Sheng, Ping Zhang
  Patterns 3:100512, 2022 (DeepDRiD, a labeled dataset for diabetic retinopathy)
  

• Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing
  Thai-Hoang Pham, Yue Qiu, Jiahui Liu, Steven Zimmer, Eric O'Neill, Lei Xie, Ping Zhang
  Patterns 3:100441, 2022 (Preview and Editorial on Patterns)
  

• Mixed-weight neural bagging for detecting m6A modifications in SARS-CoV-2 RNA sequencing
  Ruhan Liu, Liang Ou, Bin Sheng, Pei Hao, Ping Li, Xiaokang Yang, Guangtao Xue, Lei Zhu, Yuyang Luo, Ping Zhang, Po Yang, Huating Li, Dagan Feng
  IEEE Transactions on Biomedical Engineering (TBME) 69(8):2557–2568, 2022
  

• mHealth interventions for self-management of hypertension: a framework and systematic review on engagement, interactivity, and tailoring
  Weidan Cao, M. Wesley Milks, Xiaofu Liu, Megan Gregory, Daniel Addison, Ping Zhang, Lang Li
  JMIR mHealth and uHealth, 10(3):e29415, 2022
  

• A computational framework for identifying age risks in drug-adverse event pairs
  Zhizhen Zhao, Ruoqi Liu, Lei Wang, Lang Li, Chi Song, Ping Zhang
  American Medical Informatics Association Informatics Summit (AMIA Summit), 2022
  

• FAME: fragment-based conditional molecular generation for phenotypic drug discovery
  Thai-Hoang Pham, Lei Xie, Ping Zhang
  SIAM International Conference on Data Mining (SDM), 2022 (Acceptance rate: 83/298 = 27.8%, regular paper, oral presentation)
  

2021

• Cardiac complication risk profiling for cancer survivors via multi-view multi-task learning
  Thai-Hoang Pham, Changchang Yin, Laxmi Mehta, Xueru Zhang, Ping Zhang
  IEEE International Conference on Data Mining (ICDM), 2021 (Acceptance rate: 98/990 = 9.9%, regular paper, oral presentation. Selected as one of the best papers and invited to KAIS Journal Special Issue)
  

• Temporal clustering with external memory network for disease progression modeling
  Zicong Zhang, Changchang Yin, Ping Zhang
  IEEE International Conference on Data Mining (ICDM), 2021 (Acceptance rate: 98/990 = 9.9%, regular paper, oral presentation)
  

• Examining the drivers of racial/ethnic disparities in non-adherence to antihypertensive medications and mortality due to heart disease and stroke: a county-level analysis
  Macarius Donneyong, Michael Fischer, Michael Langston, Joshua Joseph, Paul Juarez, Ping Zhang, David Kline
  International Journal of Environmental Research and Public Health 18:12702, 2021
  

• Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases
  Qianlong Wen, Ruoqi Liu, Ping Zhang
  BMC Medical Informatics and Decision Making 21:263, 2021
  

• Contrastive attention for automatic medical report generation
  Fenglin Liu, Changchang Yin, Xian Wu, Shen Ge, Ping Zhang, Xu Sun
  Findings of Annual Meeting of the Association for Computational Linguistics (Findings of ACL), 2021
  

• NHBS-Net: A feature fusion attention network for ultrasound neonatal hip bone segmentation
  Ruhan Liu, Mengyao Liu, Bin Sheng, Huating Li, Ping Li, Haitao Song, Ping Zhang, Lixin Jiang, Dinggang Shen
  IEEE Transactions on Medical Imaging (TMI) 40(12):3446–3458, 2021 (Impact factor: 10.048)
  

• A survival model generalized to regression learning algorithms
  Yuanfang Guan, Hongyang Li, Daiyao Yi, Dongdong Zhang, Changchang Yin, Keyu Li, Ping Zhang
  Nature Computational Science 1:433–440, 2021
  

• TransICD: Transformer based code-wise attention model for explainable ICD coding
  Biplob Biswas, Thai-Hoang Pham, Ping Zhang
  International Conference on Artificial Intelligence in Medicine (AIME), 2021
  

• Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram
  Dongdong Zhang, Samuel Yang, Xiaohui Yuan, Ping Zhang
  iScience 24:102373, 2021 (Impact factor: 5.458)
  

• Brain atlas guided attention U-net for white matter hyperintensity segmentation
  Zicong Zhang, Kimerly Powell, Changchang Yin, Shilei Cao, Dani Gonzalez, Yousef Hannawi, Ping Zhang
  American Medical Informatics Association Informatics Summit (AMIA Summit), 2021 (Buckeye AI, one of the top solutions for WMH Segmentation Challenge)
  

• A pharmacovigilance study of pharmacokinetic drug interactions using a translational informatics discovery approach
  Lei Wang, Aditi Shendre, Chien-Wei Chiang, Weidan Cao, Xia Ning, Ping Zhang, Pengyue Zhang, Lang Li
  British Journal of Clinical Pharmacology (BJCP) 88(4):1471–1481, 2021
  

• A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing
  Thai-Hoang Pham, Yue Qiu, Jucheng Zeng, Lei Xie, Ping Zhang
  Nature Machine Intelligence 3:247–257, 2021 (Impact factor: 15.508. Selected Media Coverage: Nature Machine Intelligence, Ohio State News, ReachMD, Technology Networks, DeepLearning.AI)
  

• An interpretable deep-learning model for early prediction of sepsis in the emergency department
  Dongdong Zhang, Changchang Yin, Katherine Hunold, Xiaoqian Jiang, Jeffrey Caterino, Ping Zhang
  Patterns 2:100196, 2021 (Buckeye AI, one of the winning teams for 2019 DII National Data Science Challenge)
  

• A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data
  Ruoqi Liu, Lai Wei, Ping Zhang
  Nature Machine Intelligence 3:68–75, 2021 (Impact factor: 15.508. Selected Media Coverage: Nature Reviews Drug Discovery, Ohio State News, Bio-IT World, NEO.LIFE, First Report Managed Care, Verywell Health, Drug Discovery News, MIT Technology Review Chinese Edition)
  

2020

• An interpretable risk prediction model for healthcare with pattern attention
  Sundreen Asad Kamal, Changchang Yin, Buyue Qian, Ping Zhang
  BMC Medical Informatics and Decision Making 20:307, 2020
  

• Estimating individual treatment effects with time-varying confounders
  Ruoqi Liu, Changchang Yin, Ping Zhang
  IEEE International Conference on Data Mining (ICDM), 2020 (Acceptance rate: 91/930 = 9.8%, regular paper, oral presentation)
  

• Document classification for COVID-19 literature
  Bernal Jiménez Gutiérrez, Juncheng Zeng, Dongdong Zhang, Ping Zhang, Yu Su
  Findings of Empirical Methods in Natural Language Processing (Findings of EMNLP), 2020
  

• Finding causal mechanistic drug-drug interactions from observational data
  Sanjoy Dey, Ping Zhang, Mohamed Ghalwash, Chandramouli Maduri, Daby Sow, Zach Shahn
  American Medical Informatics Association Annual Symposium (AMIA), 2020
  

• Combining structured and unstructured data for predictive models: a deep learning approach
  Dongdong Zhang, Changchang Yin, Jucheng Zeng, Xiaohui Yuan, Ping Zhang
  BMC Medical Informatics and Decision Making 20:280, 2020
  

• Marrying medical domain knowledge with deep learning on electronic health records: a deep visual analytics approach
  Rui Li, Changchang Yin, Samuel Yang, Buyue Qian, Ping Zhang
  Journal of Medical Internet Research (JMIR) 22(9):e20645, 2020 (Impact factor: 5.034. Featured on AMIA 2021 Year-in-Review)
  

• Identifying sepsis subphenotypes via time-aware multi-modal auto-encoder
  Changchang Yin, Ruoqi Liu, Dongdong Zhang, Ping Zhang
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020 (Acceptance rate: 216/1279 = 16.9%, research track, oral presentation)
  

• Graph embedding on biomedical networks: methods, applications and evaluations
  Xiang Yue, Zhen Wang, Jingong Huang, Srinivasan Parthasarathy, Soheil Moosavinasab, Yungui Huang, Simon Lin, Wen Zhang, Ping Zhang, Huan Sun
  Bioinformatics 36(4):1241–1251, 2020 (Impact factor: 5.610)
  

2019

• Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports
  Ruoqi Liu, Ping Zhang
  BMC Medical Informatics and Decision Making 19:279, 2019
  

• A systematic framework for drug repurposing based on literature mining
  Gaocai Dong, Ping Zhang, Jingya Yang, Dongdong Zhang, Jing Peng
  IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019
  

• Domain knowledge guided deep learning with electronic health records
  Changchang Yin, Rongjian Zhao, Buyue Qian, Xin Lv, Ping Zhang
  IEEE International Conference on Data Mining (ICDM), 2019 (Acceptance rate: 95/1046 = 9.1%, regular paper, oral presentation)
  

• PerDREP: Personalized drug effectiveness prediction from longitudinal observational data
  Sanjoy Dey, Ping Zhang, Daby Sow, Kenney Ng
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019 (Acceptance rate: 111/1179 = 9.4%, research track, oral presentation)
  

2018

• Predicting adverse drug reactions through interpretable deep learning framework
  Sanjoy Dey, Heng Luo, Achille Fokoue, Jianying Hu, Ping Zhang
  BMC Bioinformatics 19(Suppl 21):476, 2018
  

• Systematic analysis of drug combinations that mitigate adverse drug reactions
  Jaehee Shim, Heng Luo, Ping Zhang, Ying Li
  IBM Journal of Research and Development 62(6):7, 2018
  

• Molecular docking for prediction and interpretation of adverse drug reactions
  Heng Luo, Achille Fokoue, Nalini Singh, Lun Yang, Jianying Hu, Ping Zhang
  Combinatorial Chemistry & High Throughput Screening 21(5): 314–322, 2018
  

• Interpretable drug target prediction using deep neural representation
  Kyle Yingkai Gao, Achille Fokoue, Heng Luo, Arun Iyengar, Sanjoy Dey, Ping Zhang
  International Joint Conference on Artificial Intelligence (IJCAI), 2018
  

• A trusted health care data analytics cloud platform
  Arun Iyengar, Ashish Kundu, Upendra Sharma, Ping Zhang
  International Conference on Distributed Computing Systems (ICDCS), 2018
  

• Cell-specific prediction and application of drug-induced gene expression profiles
  Rachel Hodos, Ping Zhang, Hao-Chih Lee, Qiaonan Duan, Zichen Wang, Neil Clark, Avi Ma'ayan, Fei Wang, Brian Kidd, Jianying Hu, David Sontag, Joel Dudley
  Pacific Symposium on Biocomputing (PSB), 2018
  

2017

• Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions
  Ibrahim Abdelaziz, Achille Fokoue, Oktie Hassanzadeh, Ping Zhang, Mohammad Sadoghi
  Journal of Web Semantics 44:104–117, 2017
  

• Exploiting electronic health records to mine drug effects on laboratory test results
  Mohamed Ghalwash, Ying Li, Ping Zhang, Jianying Hu
  ACM International Conference on Information and Knowledge Management (CIKM), 2017
  

• Precision cohort finding with outcome-driven similarity analytics: a case study of patients with atrial fibrillation
  Haifeng Liu, Xiang Li, Guotong Xie, Xin Du, Ping Zhang, Chengming Gu, Jingyi Hu
  World Congress on Medical Health Informatics (MedInfo), 2017
  

• DrugPathSeeker: interactive UI for exploring drug-ADR relation via pathways
  Janu Verma, Heng Luo, Jianying Hu, Ping Zhang
  IEEE Pacific Visualization Symposium (PacificVis), 2017
  

• Computational drug discovery with dyadic positive-unlabeled learning
  Yashu Liu, Shuang Qiu, Ping Zhang, Pinghua Gong, Fei Wang, Guoliang Xue, Jieping Ye
  SIAM International Conference on Data Mining (SDM), 2017
  

• Polyadic regression and its application to chemogenomics
  Ioakeim Perros, Fei Wang, Ping Zhang, Peter Walker, Richard Vuduc, Jyotishman Pathak, Jimeng Sun
  SIAM International Conference on Data Mining (SDM), 2017
  

• Multitask dyadic prediction and its application in prediction of adverse drug-drug interaction
  Bo Jin, Haoyu Yang, Cao Xiao, Ping Zhang, Xiaopeng Wei, Fei Wang
  AAAI Conference on Artificial Intelligence (AAAI), 2017
  

• Adverse drug reaction prediction with symbolic latent dirichlet allocation
  Cao Xiao, Ping Zhang, W. Art Chaovalitwongse, Jianying Hu, Fei Wang
  AAAI Conference on Artificial Intelligence (AAAI), 2017
  

• Integrating temporal pattern mining in ischemic stroke prediction and treatment pathway discovery for atrial fibrillation
  Shijing Guo, Xiang Li, Haifeng Liu, Ping Zhang, Xin Du, Guotong Xie, Fei Wang
  AMIA Joint Summits on Translational Science (AMIA Summits), 2017

2016

• DPDR-CPI, a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome
  Heng Luo, Ping Zhang, Xi Hang Cao, Dizheng Du, Hao Ye, Hui Huang, Can Li, Shengying Qin, Chunling Wan, Leming Shi, Lin He, Lun Yang
  Scientific Reports 6:35996, 2016
  

• Integrated machine learning approaches for predicting ischemic stroke and thromboembolism in atrial fibrillation
  Xiang Li, Haifeng Liu, Xin Du, Ping Zhang, Gang Hu, Guotong Xie, Shijing Guo, Meilin Xu, Xiaoping Xie
  American Medical Informatics Association Annual Symposium (AMIA), 2016
  

• Data-driven prediction of beneficial drug combinations in spontaneous reporting systems
  Ying Li, Ping Zhang, Zhaonan Sun, Jianying Hu
  American Medical Informatics Association Annual Symposium (AMIA), 2016
  

• Using frequent item set mining and feature selection methods to identify interacted risk factors - the atrial fibrillation case study
  Xiang Li, Haifeng Liu, Xin Du, Gang Hu, Guotong Xie, Ping Zhang
  Medical Informatics Europe (MIE), 2016
  

• Predicting drug-drug interactions through large-scale similarity-based link prediction
  Achille Fokoue, Mohammad Sadoghi, Oktie Hassanzadeh, Ping Zhang
  Extended Semantic Web Conference (ESWC), 2016 (Best In-Use/Industrial Paper Award)
  

• Risk prediction with electronic health records: a deep learning approach
  Yu Cheng, Fei Wang, Ping Zhang, Jianying Hu
  SIAM International Conference on Data Mining (SDM), 2016
  

• Predicting drug-drug interactions through similarity-based link prediction over web data
  Achille Fokoue, Oktie Hassanzadeh, Mohammad Sadoghi, Ping Zhang
  International Conference Companion on World Wide Web (WWW Demo), 2016

2015

• Label propagation prediction of drug-drug interactions based on clinical side effects (supplementary materials)
  Ping Zhang, Fei Wang, Jianying Hu, Robert Sorrentino
  Scientific Reports 5:12339, 2015

2014

• Systematic prediction of drug combinations based on clinical side-effects
  Hui Huang, Ping Zhang, Xiaoyan A. Qu, Philippe Sanseau, Lun Yang
  Scientific Reports 4:7160, 2014
  

• DDI-CPI, a server that predicts Drug-Drug Interactions through implementing the Chemical-Protein Interactome
  Heng Luo, Ping Zhang, Hui Huang, Jialiang Huang, Emily Kao, Leming Shi, Lin He, Lun Yang
  Nucleic Acids Research 42(Web Server issue):W46–W52, 2014
  

• Exploring the associations between drug side-effects and therapeutic indications
  Fei Wang, Ping Zhang, Nan Cao, Jianying Hu, Robert Sorrentino
  Journal of Biomedical Informatics 51:15–23, 2014
  

• Predicting changes in hypertension control using electronic health records from a chronic disease management program
  Jimeng Sun, Candace D McNaughton, Ping Zhang, Adam Perer, Aris Gkoulalas-Divanis, Joshua C Denny, Jacqueline Kirby, Thomas Lasko, Alexander Saip, Bradley A Malin
  Journal of the American Medical Informatics Association 21(2):337–344, 2014
  

• Towards drug repositioning: a unified computational framework for integrating multiple aspects of drug similarity and disease similarity (supplementary materials)
  Ping Zhang, Fei Wang, Jianying Hu
  American Medical Informatics Association Annual Symposium (AMIA), 2014
  

• Clinical risk prediction by exploring high-order feature correlations
  Fei Wang, Ping Zhang, Xiang Wang, Jianying Hu
  American Medical Informatics Association Annual Symposium (AMIA), 2014
  

• Clinical risk prediction with multilinear sparse logistic regression
  Fei Wang, Ping Zhang, Buyue Qian, Xiang Wang, Ian Davidson
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2014
  

• Towards personalized medicine: leveraging patient similarity and drug similarity analytics
  Ping Zhang, Fei Wang, Jianying Hu, Robert Sorrentino
  AMIA Joint Summits on Translational Science (AMIA Summits), 2014 (Nomination for Marco Ramoni Distinguished Paper Award)

2013

• Learning by aggregating experts and filtering novices: a solution to crowdsourcing problems in bioinformatics
  Ping Zhang, Weidan Cao, Zoran Obradovic
  BMC Bioinformatics 14(Suppl 12):S5, 2013
  

• Exploring the relationship between drug side-effects and therapeutic indications (supplementary materials)
  Ping Zhang, Fei Wang, Jianying Hu, Robert Sorrentino
  American Medical Informatics Association Annual Symposium (AMIA), 2013
  

• Computational drug repositioning by ranking and integrating multiple data sources (supplementary materials)
  Ping Zhang, Pankaj Agarwal, Zoran Obradovic
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2013

2012 and Earlier

• Semi-supervised learning on single-view datasets by integration of multiple co-trained classifiers
  Jelena Slivka, Ping Zhang, Aleksandar Kovacevic, Zora Konjovic, Zoran Obradovic
  International Conference on Machine Learning and Applications (ICMLA), 2012
  

• Integration of multiple annotators by aggregating experts and filtering novices
  Ping Zhang, Zoran Obradovic
  IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2012
  

• Unsupervised integration of multiple protein disorder predictors: the method and evaluation on CASP7, CASP8 and CASP9 data
  Ping Zhang, Zoran Obradovic
  Proteome Science 9(Suppl 1):S12, 2011
  

• Learning from inconsistent and unreliable annotators by a Gaussian mixture model and Bayesian information criteria
  Ping Zhang, Zoran Obradovic
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2011
  

• Unsupervised integration of multiple protein disorder predictors
  Ping Zhang, Zoran Obradovic
  IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2010