Note: Asterisk * indicates publications on tenure track. Circle o indicates PI’s students or interns. Dagger   indicates publications as a mentor of PI’s students or interns. PI’s name is bold

[19] Aaron C. Moberly, Patrick J. Lawrence, Terrin N. Tamati, and Xia Ning*. Use of machine learning to predict adult cochlear implant benefitusing reliable change index. 2023 Conference on Implantable Auditory Prostheses, May 2023.

[18] Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy, and Xia Ning*. HAM: Hybrid associations model with pooling for sequentialrecommendation. The 39th IEEE International Conference on Data Engineering, April 2023.

[17] Ryoma Kawakami, Doug Scharre, and Xia Ning*. Detection of cognitive impairment from eSAGE cognitive data using machine learning.Alzheimer’s Association International Conference (AAIC), July 2022.

[16] Sophia L TumSuden, Brian N Lee, Jingxuan Bao, Xia Ning*, Dokyoon Kim, and Shen Li. Metabolite QTL analysis of ROSMAP and ADNI prioritized sequencing data identifies the C14:2 genetic locus on Chr 2. Alzheimer’s Association International Conference (AAIC), Apr. 2022.

[15] Payal Chakraborty, Xia Ning*, David Kline, Abigail B. Shoben, William C. Miller, and Abigail Norris Turner. Using natural language processing (NLP) and machine learning (ML) methods to predict topics included in 2019 Ohio syphilis disease intervention specialist (DIS)records. 2022 STD Prevention Conference, Feb. 2022.

[14] Jackson Dooley, Brian Lee, Augustin Liu, Lei Wang, Xia Ning*, Lang Li, and Li Shen. Identifying drug interaction effects on myopathy at the group level. Pacific Symposium on Biocomputing (PSB), 2022.

[13] Wenrong Chen, Elijah N. McCool, Liangliang Sun, Xia Ning*, and Xiaowen Liu. A neural network model for predicting the retention and migration time of proteoforms in top-down mass spectrometry. ASMS Conference on Mass Spectrometry and Allied Topics, 2021.

[12] Ziqi Chen, Martin Renqiang Min, and Xia Ning*. Ranking-based convolutional neural network models for peptide-MHC binding prediction.AMIA Informatics Summit, 2021.

[11] Vishal Dey and Xia Ning*. Compound prioritization via ranking and graph representation learning. AMIA Informatics Summit, 2021.

[10] Patrick Lawrence and Xia Ning*. Improving MHC Class I antigen processing prediction via representation learning and cleavage site-specific kernels. AMIA Annual Symposium, 2021.

[9] Vishal Dey, Clara Lee, and Xia Ning*. Understanding breast implant illness via social media data analysis. AMIA Informatics Summit, 2020.

[8] Andrew Cardona, Xia Ning*, S Smart, P Chandrasekaran, C Wei, B Mccarthy, D Lee, and SV Raman. Left ventricular dysfunction, not myocardial injury, drives use of cardioprotective medications in acute myocarditis: insights from machine learning. EuroCMR, 2019.

[7] Ziwei Fan, Evan Burgun, Titus Schleyer, and Xia Ning*. Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering. The 7th IEEE International Conference on Healthcare Informatics, 2019.

[6] Wen-Hao Chiang and Xia Ning*. Drug-drug interaction prediction based on co-medication patterns and graph matching. InInternational School and Conference on Network Science, NetSci’18, 2018.

[5] Danai Chasioti, Xiaohui Yao, Pengyue Zhang, Sara Quinney, Xia Ning*, Lang Li, and Li Shen. Mining and visualizing the network of directional drug interaction effects. In International School and Conference on Network Science, NetSci’17, 2017.

[4] Danai Chasioti, Xiaohui Yao, Pengyue Zhang, Xia Ning*, Lang Li, and Li Shen. Mining directional drug interaction effects on myopathy using the faers database. In Pacific Symposium on Biocomputing, PSB’17, 2017.

[3] Xia Ning*, Lang Li, and Li Shen. Pattern discovery from directional high-order drug-drug interaction relations. In International School and Conference on Network Science, NetSci’17, 2017.

[2] Xia Ning. Data mining and machine learning methods for chemical informatics. In SIAM International Conference on Data MiningDoctoral Forum, SDM’12, 2012.

[1] Xia Ning and George Karypis. Sparse linear methods with side information for top-n recommendations. In Proceedings of the 21st World Wide Web Conference (Companion Volume), WWW’12, pages 581–582, 2012.