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2020 – today
- 2024
[j28]Bo Peng, Srinivasan Parthasarathy, Xia Ning
:
Intention enhanced mixed attentive model for session-based recommendation. Data Min. Knowl. Discov. 38(4): 2032-2061 (2024)- [j27]Vishal Dey, Xia Ning:
Enhancing molecular property prediction with auxiliary learning and task-specific adaptation. J. Cheminformatics 16(1): 85 (2024) - [j26]Vishal Dey, Xia Ning:
Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank. J. Chem. Inf. Model. 64(10): 4071-4088 (2024) - [j25]Frazier N. Baker, Ziqi Chen, Daniel Adu-Ampratwum, Xia Ning:
RLSynC: Offline-Online Reinforcement Learning for Synthon Completion. J. Chem. Inf. Model. 64(17): 6723-6735 (2024) - [j24]Cheng Luo, Yueting Zhang, Jiayi Guo, Yuxin Hu, Guangyao Zhou, Hongjian You, Xia Ning:
SAR-CDSS: A Semi-Supervised Cross-Domain Object Detection from Optical to SAR Domain. Remote. Sens. 16(6): 940 (2024) - [j23]Bo Peng, Ziqi Chen, Srinivasan Parthasarathy, Xia Ning:
Modeling Sequences as Star Graphs to Address Over-Smoothing in Self-Attentive Sequential Recommendation. ACM Trans. Knowl. Discov. Data 18(8): 207:1-207:24 (2024) - [c39]Bo Peng, Xinyi Ling, Ziru Chen, Huan Sun, Xia Ning:
eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data. ICML 2024 - [i33]Vishal Dey, Xia Ning:
Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific Adaptation. CoRR abs/2401.16299 (2024) - [i32]Bo Peng, Xinyi Ling, Ziru Chen, Huan Sun, Xia Ning:
eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data. CoRR abs/2402.08831 (2024) - [i31]Botao Yu, Frazier N. Baker, Ziqi Chen, Xia Ning, Huan Sun:
LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset. CoRR abs/2402.09391 (2024) - [i30]Reza Averly, Xia Ning:
Entity Decomposition with Filtering: A Zero-Shot Clinical Named Entity Recognition Framework. CoRR abs/2407.04629 (2024) - 2023
- [j22]Ziqi Chen, Baoyi Zhang, Hongyu Guo, Prashant S. Emani, Trevor Clancy, Chongming Jiang, Mark Gerstein, Xia Ning, Chao Cheng, Martin Renqiang Min:
Binding peptide generation for MHC Class I proteins with deep reinforcement learning. Bioinform. 39(2) (2023) - [j21]Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy, Xia Ning:
M2: Mixed Models With Preferences, Popularities and Transitions for Next-Basket Recommendation. IEEE Trans. Knowl. Data Eng. 35(4): 4033-4046 (2023) - [c38]Arpita Saha, Maggie Samaan, Bo Peng, Xia Ning:
A Multi-Layered GRU Model for COVID-19 Patient Representation and Phenotyping from Large-Scale EHR Data. BCB 2023: 21:1-21:6 - [c37]Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy, Xia Ning:
HAM: Hybrid Associations Models for Sequential Recommendation (Extended abstract). ICDE 2023: 3873-3874 - [c36]Ziqi Chen, Martin Renqiang Min, Hongyu Guo, Chao Cheng, Trevor Clancy, Xia Ning:
T-Cell Receptor Optimization with Reinforcement Learning and Mutation Polices for Precision Immunotherapy. RECOMB 2023: 174-191 - [i29]Ziqi Chen, Martin Renqiang Min, Hongyu Guo, Chao Cheng, Trevor Clancy, Xia Ning:
T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy. CoRR abs/2303.02162 (2023) - [i28]Vishal Dey, Xia Ning:
Precision Anti-Cancer Drug Selection via Neural Ranking. CoRR abs/2306.17771 (2023) - [i27]Ziqi Chen, Bo Peng, Srinivasan Parthasarathy, Xia Ning:
Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models. CoRR abs/2308.11890 (2023) - [i26]Frazier N. Baker, Ziqi Chen, Xia Ning:
RLSynC: Offline-Online Reinforcement Learning for Synthon Completion. CoRR abs/2309.02671 (2023) - [i25]Bo Peng, Srinivasan Parthasarathy, Xia Ning:
Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential Recommendation. CoRR abs/2309.10195 (2023) - [i24]Bo Peng, Ben Burns, Ziqi Chen, Srinivasan Parthasarathy, Xia Ning:
Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation. CoRR abs/2310.01612 (2023) - [i23]Patrick J. Lawrence, Xia Ning:
Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization. CoRR abs/2310.13725 (2023) - [i22]Shunian Xiang, Patrick J. Lawrence, Bo Peng, Chienwei Chiang, Dokyoon Kim, Li Shen, Xia Ning:
Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing. CoRR abs/2310.15211 (2023) - [i21]Bo Peng, Ziqi Chen, Srinivasan Parthasarathy, Xia Ning:
Modeling Sequences as Star Graphs to Address Over-smoothing in Self-attentive Sequential Recommendation. CoRR abs/2311.07742 (2023) - 2022
- [j20]Shinji Tarumi, Wataru Takeuchi, Rong Qi, Xia Ning, Laura Ruppert, Hideyuki Ban, Daniel H. Robertson, Titus Schleyer, Kensaku Kawamoto:
Predicting pharmacotherapeutic outcomes for type 2 diabetes: An evaluation of three approaches to leveraging electronic health record data from multiple sources. J. Biomed. Informatics 129: 104001 (2022) - [j19]Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy, Xia Ning:
$\mathop {\mathtt {HAM}}$HAM: Hybrid Associations Models for Sequential Recommendation. IEEE Trans. Knowl. Data Eng. 34(10): 4838-4853 (2022) - [c35]Daniele Pala, Brian Lee, Xia Ning, Dokyoon Kim, Li Shen:
Mediation Analysis and Mixed-Effects Models for the Identification of Stage-specific Imaging Genetics Patterns in Alzheimer's Disease. BIBM 2022: 2667-2673 - [r2]Athanasios N. Nikolakopoulos, Xia Ning, Christian Desrosiers, George Karypis:
Trust Your Neighbors: A Comprehensive Survey of Neighborhood-Based Methods for Recommender Systems. Recommender Systems Handbook 2022: 39-89 - [i20]Bo Peng, Chang-Yu Tai, Srinivasan Parthasarathy, Xia Ning:
Prospective Preference Enhanced Mixed Attentive Model for Session-based Recommendation. CoRR abs/2206.01875 (2022) - [i19]Ziqi Chen, Oluwatosin R. Ayinde, James R. Fuchs, Huan Sun, Xia Ning:
G2Retro: Two-Step Graph Generative Models for Retrosynthesis Prediction. CoRR abs/2206.04882 (2022) - [i18]Bo Peng, Srinivasan Parthasarathy, Xia Ning:
Recursive Attentive Methods with Reused Item Representations for Sequential Recommendation. CoRR abs/2209.07997 (2022) - 2021
- [j18]Jordan R. Hill, Shyam Visweswaran, Xia Ning, Titus K. Schleyer:
Use, Impact, Weaknesses, and Advanced Features of Search Functions for Clinical Use in Electronic Health Records: A Scoping Review. Appl. Clin. Inform. 12(3): 417-428 (2021) - [j17]Zhiyun Ren, Bo Peng, Titus K. Schleyer, Xia Ning:
Hybrid collaborative filtering methods for recommending search terms to clinicians. J. Biomed. Informatics 113: 103635 (2021) - [j16]Ziqi Chen, Martin Renqiang Min, Srinivasan Parthasarathy, Xia Ning:
A deep generative model for molecule optimization via one fragment modification. Nat. Mach. Intell. 3(12): 1040-1049 (2021) - [j15]Arvind Nair, Xia Ning, James H. Hill:
Using recommender systems to improve proactive modeling. Softw. Syst. Model. 20(4): 1159-1181 (2021) - [c34]Patrick J. Lawrence, Xia Ning:
Improving MHC Class I Antigen Processing Prediction via Representation Learning and Cleavage Site-Specific Kernels. AMIA 2021 - [c33]Jie Liu, Jiawen Liu, Zhen Xie, Xia Ning, Dong Li:
Flame: A Self-Adaptive Auto-Labeling System for Heterogeneous Mobile Processors. SEC 2021: 80-93 - [i17]Athanasios N. Nikolakopoulos, Xia Ning, Christian Desrosiers, George Karypis:
Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems. CoRR abs/2109.04584 (2021) - [i16]Vishal Dey, Raghu Machiraju, Xia Ning:
Improving Compound Activity Classification via Deep Transfer and Representation Learning. CoRR abs/2111.07439 (2021) - 2020
- [j14]Wen-Hao Chiang, Li Shen, Lang Li, Xia Ning:
Drug-drug interaction prediction based on co-medication patterns and graph matching. Int. J. Comput. Biol. Drug Des. 13(1): 36-57 (2020) - [j13]Xia Ning, Chi Zhang, Kai Wang, Zhongming Zhao, Ewy A. Mathé:
Correction to: The International Conference on Intelligent Biology and Medicine 2019: computational methods for drug interactions. BMC Medical Informatics Decis. Mak. 20(1): 77 (2020) - [j12]Bo Peng, Xiaohui Yao, Shannon L. Risacher, Andrew J. Saykin, Li Shen, Xia Ning:
Cognitive biomarker prioritization in Alzheimer's Disease using brain morphometric data. BMC Medical Informatics Decis. Mak. 20(1): 319 (2020) - [j11]Xiaohui Yao, Tiffany Tsang, Qing Sun, Sara K. Quinney, Pengyue Zhang, Xia Ning, Lang Li, Li Shen:
Mining and visualizing high-order directional drug interaction effects using the FAERS database. BMC Medical Informatics Decis. Mak. 20-S(2): 50 (2020) - [j10]Xia Ning, Chi Zhang, Kai Wang, Zhongming Zhao, Ewy A. Mathé:
The International Conference on Intelligent Biology and Medicine 2019: computational methods for drug interactions. BMC Medical Informatics Decis. Mak. 20-S(2): 51 (2020) - [j9]Yicheng He, Junfeng Liu, Xia Ning:
Drug Selection via Joint Push and Learning to Rank. IEEE ACM Trans. Comput. Biol. Bioinform. 17(1): 110-123 (2020) - [i15]Bo Peng, Xiaohui Yao, Shannon L. Risacher, Andrew J. Saykin, Li Shen, Xia Ning:
Personalized Prioritization of Cognitive Biomarkers in Alzheimer's Disease via Learning to Rank using Brain Morphometric Data. CoRR abs/2002.07699 (2020) - [i14]Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy, Xia Ning:
HAM: Hybrid Associations Model with Pooling for Sequential Recommendation. CoRR abs/2002.11890 (2020) - [i13]Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy, Xia Ning:
M2pht: Mixed Models with Preferences and Hybrid Transitions for Next-Basket Recommendation. CoRR abs/2004.01646 (2020) - [i12]Zhiyun Ren, Bo Peng, Titus K. Schleyer, Xia Ning:
Hybrid Collaborative Filtering Models for Clinical Search Recommendation. CoRR abs/2008.01193 (2020) - [i11]Ziwei Fan, Evan Burgun, Zhiyun Ren, Titus Schleyer, Xia Ning:
Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering. CoRR abs/2008.05399 (2020) - [i10]Vishal Dey, Peter Krasniak, Minh Nguyen, Clara Lee, Xia Ning:
Understanding Breast Implant Illness via Social Media Data Analysis. CoRR abs/2008.11238 (2020) - [i9]Ziqi Chen, Martin Renqiang Min, Xia Ning:
Ranking-based Convolutional Neural Network Models for Peptide-MHC Binding Prediction. CoRR abs/2012.02840 (2020) - [i8]Ziqi Chen, Martin Renqiang Min, Srinivasan Parthasarathy, Xia Ning:
Molecule Optimization via Fragment-based Generative Models. CoRR abs/2012.04231 (2020)
2010 – 2019
- 2019
- [j8]Danai Chasioti, Xiaohui Yao, Pengyue Zhang, Samuel Lerner, Sara K. Quinney, Xia Ning, Lang Li, Li Shen:
Mining Directional Drug Interaction Effects on Myopathy Using the FAERS Database. IEEE J. Biomed. Health Informatics 23(5): 2156-2163 (2019) - [c32]Bo Peng, Xia Ning:
Deep Learning for High-Order Drug-Drug Interaction Prediction. BCB 2019: 197-206 - [c31]Bo Peng, Xiaohui Yao, Shannon L. Risacher, Andrew J. Saykin, Li Shen, Xia Ning:
Prioritization of Cognitive Assessments in Alzheimer's Disease via Learning to Rank using Brain Morphometric Data. BHI 2019: 1-4 - [c30]Zhiyun Ren, Xia Ning, Andrew S. Lan, Huzefa Rangwala:
Grade Prediction with Neural Collaborative Filtering. DSAA 2019: 1-10 - [c29]Zhiyun Ren, Xia Ning, Andrew S. Lan, Huzefa Rangwala:
Grade Prediction Based on Cumulative Knowledge and Co-taken Courses. EDM 2019 - [c28]Ziwei Fan, Evan Burgun, Titus Schleyer, Xia Ning:
Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering. ICHI 2019: 1-3 - [c27]Bo Peng, Zhiyun Ren, Xiaohui Yao, Kefei Liu, Andrew J. Saykin, Li Shen, Xia Ning:
Prioritizing Amyloid Imaging Biomarkers in Alzheimer's Disease via Learning to Rank. MBIA/MFCA@MICCAI 2019: 139-148 - [i7]Wen-Hao Chiang, Li Shen, Lang Li, Xia Ning:
Drug-drug interaction prediction based on co-medication patterns and graph matching. CoRR abs/1902.08675 (2019) - [i6]Bo Peng, Renqiang Min, Xia Ning:
CNN-based Dual-Chain Models for Knowledge Graph Learning. CoRR abs/1911.06910 (2019) - 2018
- [j7]Wen-Hao Chiang, Titus Schleyer, Li Shen, Lang Li, Xia Ning:
Pattern Discovery from High-Order Drug-Drug Interaction Relations. J. Heal. Informatics Res. 2(3): 272-304 (2018) - [c26]Lingma Lu Acheson, Xia Ning:
Enhance E-Learning through Data Mining for Personalized Intervention. CSEDU (1) 2018: 461-465 - [c25]Zhiyun Ren, Xia Ning, Huzefa Rangwala:
ALE: Additive Latent Effect Models for Grade Prediction. SDM 2018: 477-485 - [i5]Zhiyun Ren, Xia Ning, Huzefa Rangwala:
ALE: Additive Latent Effect Models for Grade Prediction. CoRR abs/1801.05535 (2018) - [i4]Yicheng He, Junfeng Liu, Lijun Cheng, Xia Ning:
Drug Selection via Joint Push and Learning to Rank. CoRR abs/1801.07691 (2018) - [i3]Wen-Hao Chiang, Li Shen, Lang Li, Xia Ning:
Drug Recommendation toward Safe Polypharmacy. CoRR abs/1803.03185 (2018) - 2017
- [j6]Junfeng Liu, Xia Ning:
Multi-Assay-Based Compound Prioritization via Assistance Utilization: A Machine Learning Framework. J. Chem. Inf. Model. 57(3): 484-498 (2017) - [j5]Junfeng Liu, Xia Ning:
Differential Compound Prioritization via Bidirectional Selectivity Push with Power. J. Chem. Inf. Model. 57(12): 2958-2975 (2017) - [c24]Junfeng Liu, Xia Ning:
Differential Compound Prioritization via Bi-Directional Selectivity Push with Power. BCB 2017: 394-399 - [c23]Zhiyun Ren, Xia Ning, Huzefa Rangwala:
Grade Prediction with Temporal Course-wise Influence. EDM 2017 - [c22]Xia Ning, Titus Schleyer, Li Shen, Lang Li:
Pattern Discovery from Directional High-Order Drug-Drug Interaction Relations. ICHI 2017: 154-162 - [c21]Xia Ning, Li Shen, Lang Li:
Predicting High-Order Directional Drug-Drug Interaction Relations. ICHI 2017: 556-561 - [e1]Raju Gottumukkala, Xia Ning, Guozhu Dong, Vijay Raghavan, Srinivas Aluru, George Karypis, Lucio Miele, Xindong Wu:
2017 IEEE International Conference on Data Mining Workshops, ICDM Workshops 2017, New Orleans, LA, USA, November 18-21, 2017. IEEE Computer Society 2017, ISBN 978-1-5386-3800-2 [contents] - [i2]Zhiyun Ren, Xia Ning, Huzefa Rangwala:
Grade Prediction with Temporal Course-wise Influence. CoRR abs/1709.05433 (2017) - 2016
- [c20]Hongteng Xu, Xia Ning, Hui Zhang, Junghwan Rhee, Guofei Jiang:
PInfer: Learning to Infer Concurrent Request Paths from System Kernel Events. ICAC 2016: 199-208 - [c19]Xiao Bian, Feng Li, Xia Ning:
Kernelized Sparse Self-Representation for Clustering and Recommendation. SDM 2016: 10-17 - [i1]Baichuan Zhang, Sutanay Choudhury, Mohammad Al Hasan, Xia Ning, Khushbu Agarwal, Sumit Purohit, Paola Gabriela Pesntez Cabrera:
Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs. CoRR abs/1601.03778 (2016) - 2015
- [c18]Jun Wang, Zhiyun Qian, Zhichun Li, Zhenyu Wu, Junghwan Rhee, Xia Ning, Peng Liu, Guofei Jiang:
Discover and Tame Long-running Idling Processes in Enterprise Systems. AsiaCCS 2015: 543-554 - [c17]Dixin Luo, Hongteng Xu, Yi Zhen, Xia Ning, Hongyuan Zha, Xiaokang Yang, Wenjun Zhang:
Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences. IJCAI 2015: 3685-3691 - [c16]Jiaji Huang, Xia Ning:
Latent Space Tracking from Heterogeneous Data with an Application for Anomaly Detection. PAKDD (1) 2015: 429-441 - [c15]Xiao Bian, Xia Ning, Geoff Jiang:
Hierarchical Sparse Dictionary Learning. ECML/PKDD (2) 2015: 687-700 - [c14]Xia Ning, George Karypis:
Recent Advances in Recommender Systems and Future Directions. PReMI 2015: 3-9 - [c13]Tzu-Chun Lin, Xia Ning:
Multi-Perspective Modeling for Click Event Prediction. RecSys Challenge 2015: 11:1-11:4 - [r1]Xia Ning, Christian Desrosiers, George Karypis:
A Comprehensive Survey of Neighborhood-Based Recommendation Methods. Recommender Systems Handbook 2015: 37-76 - 2014
- [c12]Martin Renqiang Min, Xia Ning, Chao Cheng, Mark Gerstein:
Interpretable Sparse High-Order Boltzmann Machines. AISTATS 2014: 614-622 - 2013
- [c11]Santosh Kabbur, Xia Ning, George Karypis:
FISM: factored item similarity models for top-N recommender systems. KDD 2013: 659-667 - 2012
- [j4]Fuzhen Zhuang, George Karypis, Xia Ning, Qing He, Zhongzhi Shi:
Multi-view learning via probabilistic latent semantic analysis. Inf. Sci. 199: 20-30 (2012) - [j3]Xia Ning, Michael A. Walters, George Karypis:
Improved Machine Learning Models for Predicting Selective Compounds. J. Chem. Inf. Model. 52(1): 38-50 (2012) - [j2]Xia Ning, Michael A. Walters, George Karypis:
Improved Machine Learning Models for Predicting Selective Compounds. J. Chem. Inf. Model. 52(5): 1411 (2012) - [c10]Xia Ning, George Karypis:
Sparse linear methods with side information for top-n recommendations. RecSys 2012: 155-162 - [c9]Xia Ning, George Karypis:
Sparse linear methods with side information for Top-N recommendations. WWW (Companion Volume) 2012: 581-582 - 2011
- [c8]Xia Ning, Michael A. Walters, George Karypis:
Improved machine learning models for predicting selective compounds. BCB 2011: 106-115 - [c7]Xia Ning, George Karypis:
SLIM: Sparse Linear Methods for Top-N Recommender Systems. ICDM 2011: 497-506 - [c6]Xia Ning, Yanjun Qi:
Semi-Supervised Convolution Graph Kernels for Relation Extraction. SDM 2011: 510-521 - 2010
- [c5]Pavel P. Kuksa, Yanjun Qi, Bing Bai, Ronan Collobert, Jason Weston, Vladimir Pavlovic, Xia Ning:
Semi-supervised Abstraction-Augmented String Kernel for Multi-level Bio-Relation Extraction. ECML/PKDD (2) 2010: 128-144 - [c4]Xia Ning, George Karypis:
Multi-task Learning for Recommender System. ACML 2010: 269-284 - [p1]Nikil Wale, Xia Ning, George Karypis:
Trends in Chemical Graph Data Mining. Managing and Mining Graph Data 2010: 581-606
2000 – 2009
- 2009
- [j1]Xia Ning, Huzefa Rangwala, George Karypis:
Multi-Assay-Based Structure-Activity Relationship Models: Improving Structure-Activity Relationship Models by Incorporating Activity Information from Related Targets. J. Chem. Inf. Model. 49(11): 2444-2456 (2009) - [c3]Xia Ning, George Karypis:
The Set Classification Problem and Solution Methods. SDM 2009: 847-858 - 2008
- [c2]Xia Ning, George Karypis:
The Set Classification Problem and Solution Methods. ICDM Workshops 2008: 720-729 - 2005
- [c1]Jianjun Chen, Yao Zheng, Xia Ning:
Scalable Parallel Quadrilateral Mesh Generation Coupled with Mesh Partitioning. PDCAT 2005: 966-970
Coauthor Index
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