Vasant Gajanan Honavar, PhD - Penn State Cancer Institute
Researcher Profile
Vasant Gajanan Honavar, PhD
Professor and Edward Frymoyer Chair of Information Sciences and Technology, College of Information Sciences and Technology
Professor of Computer Science, College of Information Sciences and Technology
Penn State Neuroscience Institute
Professor of Computer Science, College of Information Sciences and Technology
Penn State Neuroscience Institute
Scientific Program:Cancer Control
Disease Teams:
Institute for CyberScience (ICS) - Co-hire
Research Interests
- Proteins
- RNA
- Machine Learning
- Datasets
- Binding Sites
- B-Lymphocyte Epitopes
- Databases
- Genes
- Learning
- Sleep
- Animals
- Gene Expression
Recent Publications
2023
Adishesha, AS, Jakielaszek, L, Azhar, F, Zhang, P, Honavar, V, Ma, F, Belani, C, Mitra, P & Huang, SX 2023, 'Forecasting User Interests Through Topic Tag Predictions in Online Health Communities', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 7, pp. 3645-3656. https://doi.org/10.1109/JBHI.2023.3271580
Schade, MM, Roberts, DM, Honavar, VG & Buxton, OM 2023, Machine learning approaches in sleep and circadian research. in Encyclopedia of Sleep and Circadian Rhythms: Volume 1-6, Second Edition. Elsevier, pp. 53-62. https://doi.org/10.1016/B978-0-12-822963-7.00383-2
Jung, Y, Geng, C, Bonvin, AMJJ, Xue, LC & Honavar, VG 2023, 'MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein–Protein Docking Conformations', Biomolecules, vol. 13, no. 1, 121. https://doi.org/10.3390/biom13010121
Roberts, DM, Schade, MM, Master, L, Honavar, VG, Nahmod, NG, Chang, A-M, Gartenberg, D & Buxton, OM 2023, 'Performance of an open machine learning model to classify sleep/wake from actigraphy across ∼24-hour intervals without knowledge of rest timing', Sleep health. https://doi.org/10.1016/j.sleh.2023.07.001
2022
Seto, CH, Graif, C, Khademi, A, Honavar, VG & Kelling, CE 2022, 'Connected in health: Place-to-place commuting networks and COVID-19 spillovers', Health and Place, vol. 77, 102891. https://doi.org/10.1016/j.healthplace.2022.102891
Kallitsis, M, Prajapati, R, Honavar, V, Wu, D & Yen, J 2022, 'Detecting and Interpreting Changes in Scanning Behavior in Large Network Telescopes', IEEE Transactions on Information Forensics and Security, vol. 17, pp. 3611-3625. https://doi.org/10.1109/TIFS.2022.3211644
Cwiek, A, Rajtmajer, SM, Wyble, B, Honavar, V, Grossner, E & Hillary, FG 2022, 'Feeding the machine: Challenges to reproducible predictive modeling in resting-state connectomics', Network Neuroscience, vol. 6, no. 1, pp. 29-48. https://doi.org/10.1162/netn_a_00212
2021
Hsieh, TY, Wang, S, Sun, Y & Honavar, V 2021, Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns to Attend to Important Variables As Well As Time Intervals. in WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining. WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Association for Computing Machinery, Inc, pp. 607-615, 14th ACM International Conference on Web Search and Data Mining, WSDM 2021, Virtual, Online, Israel, 3/8/21. https://doi.org/10.1145/3437963.3441815
Hsieh, TY, Sun, Y, Wang, S & Honavar, V 2021, Functional autoencoders for functional data representation learning. in SIAM International Conference on Data Mining, SDM 2021. SIAM International Conference on Data Mining, SDM 2021, Siam Society, pp. 666-674, 2021 SIAM International Conference on Data Mining, SDM 2021, Virtual, Online, 4/29/21.
Liang, J, Wu, Y, Xu, D & Honavar, VG 2021, Longitudinal Deep Kernel Gaussian Process Regression. in 35th AAAI Conference on Artificial Intelligence, AAAI 2021. 35th AAAI Conference on Artificial Intelligence, AAAI 2021, vol. 10A, Association for the Advancement of Artificial Intelligence, pp. 8556-8564, 35th AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual, Online, 2/2/21.
Prajapati, R, Honavar, V, Wu, D, Yen, J & Kallitsis, M 2021, Shedding light into the darknet: scanning characterization and detection of temporal changes. in CoNEXT 2021 - Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies. CoNEXT 2021 - Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies, Association for Computing Machinery, Inc, pp. 469-470, 17th ACM International Conference on emerging Networking EXperiments and Technologies, CoNEXT 2021, Virtual, Online, Germany, 12/7/21. https://doi.org/10.1145/3485983.3493347
Hsieh, TY, Sun, Y, Tang, X, Wang, S & Honavar, VG 2021, SrVARM: State regularized vector autoregressive model for joint learning of hidden state transitions and state-dependent inter-variable dependencies from multi-variate time series. in The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021. The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021, Association for Computing Machinery, Inc, pp. 2270-2280, 2021 World Wide Web Conference, WWW 2021, Ljubljana, Slovenia, 4/19/21. https://doi.org/10.1145/3442381.3450116
2020
Sun, Y, Wang, S, Tang, X, Hsieh, TY & Honavar, V 2020, Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach. in The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020. The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020, Association for Computing Machinery, Inc, pp. 673-683, 29th International World Wide Web Conference, WWW 2020, Taipei, Taiwan, Province of China, 4/20/20. https://doi.org/10.1145/3366423.3380149
Khademi, A & Honavar, V 2020, Algorithmic Bias in Recidivism Prediction: A Causal Perspective. in AAAI 2020 - 34th AAAI Conference on Artificial Intelligence. AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, AAAI press, pp. 13839-13840, 34th AAAI Conference on Artificial Intelligence, AAAI 2020, New York, United States, 2/7/20.
Le, T & Honavar, V 2020, Dynamical Gaussian Process Latent Variable Model for Representation Learning from Longitudinal Data. in FODS 2020 - Proceedings of the 2020 ACM-IMS Foundations of Data Science Conference. FODS 2020 - Proceedings of the 2020 ACM-IMS Foundations of Data Science Conference, Association for Computing Machinery, Inc, pp. 183-188, 2020 ACM-IMS Foundations of Data Science Conference, FODS 2020, Virtual, Online, United States, 10/19/20. https://doi.org/10.1145/3412815.3416894
Geng, C, Jung, Y, Renaud, N, Honavar, V, Bonvin, AMJJ & Xue, LC 2020, 'IScore: A novel graph kernel-based function for scoring protein-protein docking models', Bioinformatics, vol. 36, no. 1, pp. 112-121. https://doi.org/10.1093/bioinformatics/btz496
Liang, J, Xu, D, Sun, Y & Honavar, V 2020, LMLFM: Longitudinal multi-level factorization machine. in AAAI 2020 - 34th AAAI Conference on Artificial Intelligence. AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, AAAI press, pp. 4811-4818, 34th AAAI Conference on Artificial Intelligence, AAAI 2020, New York, United States, 2/7/20.
Hou, Y, Wu, C, Yang, D, Ye, T, Honavar, VG, Van Duin, ACT, Wang, K & Priya, S 2020, 'Two-dimensional hybrid organic-inorganic perovskites as emergent ferroelectric materials', Journal of Applied Physics, vol. 128, no. 6, 060906. https://doi.org/10.1063/5.0016010
Renaud, N, Jung, Y, Honavar, V, Geng, C, Bonvin, AMJJ & Xue, LC 2020, 'iScore: An MPI supported software for ranking protein–protein docking models based on a random walk graph kernel and support vector machines', SoftwareX, vol. 11, 100462. https://doi.org/10.1016/j.softx.2020.100462
2019
Hsieh, TY, Sun, Y, Wang, S & Honavar, V 2019, Adaptive structural co-regularization for unsupervised multi-view feature selection. in Y Gao, R Moller, X Wu & R Kotagiri (eds), Proceedings - 10th IEEE International Conference on Big Knowledge, ICBK 2019., 8944744, Proceedings - 10th IEEE International Conference on Big Knowledge, ICBK 2019, Institute of Electrical and Electronics Engineers Inc., pp. 87-96, 10th IEEE International Conference on Big Knowledge, ICBK 2019, Beijing, China, 11/10/19. https://doi.org/10.1109/ICBK.2019.00020
Abbas, M, Matta, J, Le, T, Bensmail, H, Obafemi-Ajayi, T, Honavar, V & EL-Manzalawy, Y 2019, 'Biomarker discovery in inflammatory bowel diseases using network-based feature selection', PloS one, vol. 14, no. 11, e0225382. https://doi.org/10.1371/journal.pone.0225382
Khademi, A, Foley, D, Lee, S & Honavar, V 2019, Fairness in algorithmic decision making: An excursion through the lens of causality. in The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, Association for Computing Machinery, Inc, pp. 2907-2914, 2019 World Wide Web Conference, WWW 2019, San Francisco, United States, 5/13/19. https://doi.org/10.1145/3308558.3313559
Zhou, Y, Sun, Y & Honavar, V 2019, Improving image captioning by leveraging knowledge graphs. in Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019., 8658870, Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, Institute of Electrical and Electronics Engineers Inc., pp. 283-293, 19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019, Waikoloa Village, United States, 1/7/19. https://doi.org/10.1109/WACV.2019.00036
Honavar, V 2019, 'Machine learning in clinical care: Quo vadis?', Indian Journal of Ophthalmology, vol. 67, no. 7, pp. 985-986. https://doi.org/10.4103/ijo.IJO_1167_19
Sun, Y, Wang, S, Hsieh, TY, Tang, X & Honavar, V 2019, Megan: A generative adversarial network for multi-view network embedding. in S Kraus (ed.), Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019. IJCAI International Joint Conference on Artificial Intelligence, vol. 2019-August, International Joint Conferences on Artificial Intelligence, pp. 3527-3533, 28th International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, 8/10/19. https://doi.org/10.24963/ijcai.2019/489
Kandasamy, S, Bhattacharyya, A & Honavar, VG 2019, Minimum intervention cover of a causal graph. in 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019. 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, AAAI press, pp. 2876-2885, 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, United States, 1/27/19.
Sun, Y, Bui, N, Hsieh, TY & Honavar, V 2019, Multi-view network embedding via graph factorization clustering and co-regularized multi-view agreement. in Z Li, H Tong, F Zhu & J Yu (eds), Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018., 8637384, IEEE International Conference on Data Mining Workshops, ICDMW, vol. 2018-November, IEEE Computer Society, pp. 1006-1013, 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018, Singapore, Singapore, 11/17/18. https://doi.org/10.1109/ICDMW.2018.00145
Khademi, A, El-Manzalawy, Y, Master, L, Buxton, OM & Honavar, VG 2019, 'Personalized sleep parameters estimation from actigraphy: A machine learning approach', Nature and Science of Sleep, vol. 11, pp. 387-399. https://doi.org/10.2147/NSS.S220716
Parashar, M, Simonet, A, Rodero, I, Ghahramani, F, Agnew, G, Jantz, R & Honavar, V 2020, 'The Virtual Data Collaboratory: A Regional Cyberinfrastructure for Collaborative Data-Driven Research', Computing in Science and Engineering, vol. 22, no. 3, 8686134, pp. 79-92. https://doi.org/10.1109/MCSE.2019.2908850
Liang, J, Hu, J, Dong, S & Honavar, V 2019, Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems. in Y Song, B Liu, K Lee, N Abe, C Pu, M Qiao, N Ahmed, D Kossmann, J Saltz, J Tang, J He, H Liu & X Hu (eds), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018., 8621994, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1052-1058, 2018 IEEE International Conference on Big Data, Big Data 2018, Seattle, United States, 12/10/18. https://doi.org/10.1109/BigData.2018.8621994
Lee, S & Honavar, V 2019, 'Towards Robust Relational Causal Discovery', Proceedings of Machine Learning Research, vol. 115, pp. 345-355.
Lee, S & Honavar, V 2019, 'Towards robust relational causal discovery', Paper presented at 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, 7/22/19 - 7/25/19.