Researcher Profile

Researcher Profile

Vasant Gajanan Honavar, PhD

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
Scientific Program:Cancer Control
Disease Teams:
Institute for CyberScience (ICS) - Co-hire
vuh14@psu.edu

Research Interests

  • Proteins
  • RNA
  • Machine Learning
  • Datasets
  • Binding Sites
  • B-Lymphocyte Epitopes
  • Databases
  • Genes
  • Animals
  • Gene Expression
  • Amino Acid Sequence
  • Amino Acids

Recent Publications

2020

Geng, C, Jung, Y, Renaud, N, Honavar, V, Bonvin, AMJJ, Xue, LC & Valencia, A 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
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.
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', Paper presented at 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, 7/22/19 - 7/25/19.

2018

Hu, J, Liang, J, Kuang, Y & Honavar, V 2018, 'A user similarity-based Top-N recommendation approach for mobile in-application advertising', Expert Systems With Applications, vol. 111, pp. 51-60. https://doi.org/10.1016/j.eswa.2018.02.012
Hsieh, TY, EL-Manzalawy, Y, Sun, Y & Honavar, V 2018, Compositional Stochastic Average Gradient for Machine Learning and Related Applications. in H Yin, P Novais, D Camacho & AJ Tallón-Ballesteros (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11314 LNCS, Springer Verlag, pp. 740-752, 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, Madrid, Spain, 11/21/18. https://doi.org/10.1007/978-3-030-03493-1_77
Abbas, M, Le, T, Bensmail, H, Honavar, VG & Elmanzalawi, Y 2018, Microbiomarkers Discovery in Inflammatory Bowel Diseases using Network-Based Feature Selection. in ACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. ACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Association for Computing Machinery, Inc, pp. 172-177, 9th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2018, Washington, United States, 8/29/18. https://doi.org/10.1145/3233547.3233602
El-Manzalawy, Y, Hsieh, TY, Shivakumar, M, Kim, D & Honavar, V 2018, 'Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data 06 Biological Sciences 0604 Genetics', BMC Medical Genomics, vol. 11, 71. https://doi.org/10.1186/s12920-018-0388-0
Gur, S & Honavar, VG 2018, PATENet: Pairwise Alignment of Time Evolving Networks. in P Perner (ed.), Machine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10934 LNAI, Springer Verlag, pp. 85-98, 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018, New York, United States, 7/15/18. https://doi.org/10.1007/978-3-319-96136-1_8
Jung, Y, EL-Manzalawy, Y, Dobbs, D & Honavar, VG 2019, 'Partner-specific prediction of RNA-binding residues in proteins: A critical assessment', Proteins: Structure, Function and Bioinformatics, vol. 87, no. 3, pp. 198-211. https://doi.org/10.1002/prot.25639
Khademi, A, El-Manzalawy, Y, Buxton, OM & Honavar, V 2018, Toward personalized sleep-wake prediction from actigraphy. in 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018. 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 414-417, 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018, Las Vegas, United States, 3/4/18. https://doi.org/10.1109/BHI.2018.8333456
Dyer, R, Honavar, V, Leavens, GT, Nguyen, HA, Nguyen, TN & Rajan, H 2018, 'Welcome to the WASPI workshop', WASPI 2018 - Proceedings of the 1st ACM SIGSOFT International Workshop on Automated Specification Inference, Co-located with FSE 2018, pp. III.

2017

Lee, S & Honavar, V 2017, 'A kernel conditional independence test for relational data', Paper presented at 33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017, Sydney, Australia, 8/11/17 - 8/15/17.
Honavar, VG, Yelick, K, Nahrstedt, K, Rushmeier, H, Rexford, J, Hill, MD, Bradley, E & Mynatt, E 2017, Advanced Cyberinfrastructure for Science, Engineering, and Public Policy. Computing Community Consortium.
Hager, GD, Bryant, R, Horvitz, E, Mataric, M & Honavar, V 2017, Advances in Artificial Intelligence Require Progress Across all of Computer Science. Computing Community Consortium.
Barocas, S, Bradley, E, Honavar, V & Provost, F 2017, Big Data, Data Science, and Civil Rights. Computing Community Consortium.
El-Manzalawy, Y, Dobbs, D & Honavar, VG 2017, In silico prediction of linear B-cell epitopes on proteins. in Methods in Molecular Biology. Methods in Molecular Biology, vol. 1484, Humana Press Inc., pp. 255-264. https://doi.org/10.1007/978-1-4939-6406-2_17
Lee, S & Honavar, V 2017, 'Self-discrepancy conditional independence test', Paper presented at 33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017, Sydney, Australia, 8/11/17 - 8/15/17.
Walia, RR, El-Manzalawy, Y, Honavar, VG & Dobbs, D 2017, Sequence-based prediction of RNA-binding residues in proteins. in Methods in Molecular Biology. Methods in Molecular Biology, vol. 1484, Humana Press Inc., pp. 205-235. https://doi.org/10.1007/978-1-4939-6406-2_15
El-Manzalawy, Y, Buxton, O & Honavar, V 2017, Sleep/wake state prediction and sleep parameter estimation using unsupervised classification via clustering. in I Yoo, JH Zheng, Y Gong, XT Hu, C-R Shyu, Y Bromberg, J Gao & D Korkin (eds), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 718-723, 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, Kansas City, United States, 11/13/17. https://doi.org/10.1109/BIBM.2017.8217742
Xue, LC, Rodrigues, JPGLM, Dobbs, D, Honavar, V & Bonvin, AMJJ 2017, 'Template-based protein-protein docking exploiting pairwise interfacial residue restraints', Briefings in bioinformatics, vol. 18, no. 3, pp. 458-466. https://doi.org/10.1093/bib/bbw027

2016

Lee, S & Honavar, V 2016, A characterization of Markov equivalence classes of Relational Causal Models under path semantics. in D Janzing & A Ihler (eds), 32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016. 32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016, Association For Uncertainty in Artificial Intelligence (AUAI), pp. 387-396, 32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016, Jersey City, United States, 6/25/16.
Honavar, VG, Hill, MD & Yelick, K 2016, Accelerating Science: A Computing Research Agenda. Computing Community Consortium.
El-Manzalawy, Y, Abbas, M, Malluhi, Q & Honavar, V 2016, 'FastRNABindR: Fast and accurate prediction of protein-RNA interface residues', PloS one, vol. 11, no. 7, e0158445. https://doi.org/10.1371/journal.pone.0158445
Bui, N, Le, T & Honavar, V 2016, Labeling actors in multi-view social networks by integrating information from within and across multiple views. in R Ak, G Karypis, Y Xia, XT Hu, PS Yu, J Joshi, L Ungar, L Liu, A-H Sato, T Suzumura, S Rachuri, R Govindaraju & W Xu (eds), Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016., 7840654, Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016, Institute of Electrical and Electronics Engineers Inc., pp. 616-625, 4th IEEE International Conference on Big Data, Big Data 2016, Washington, United States, 12/5/16. https://doi.org/10.1109/BigData.2016.7840654
Lee, S & Honavar, V 2016, On learning causal models from relational data. in 30th AAAI Conference on Artificial Intelligence, AAAI 2016. 30th AAAI Conference on Artificial Intelligence, AAAI 2016, AAAI press, pp. 3263-3270, 30th AAAI Conference on Artificial Intelligence, AAAI 2016, Phoenix, United States, 2/12/16.
El-Manzalawy, Y, Munoz, EE, Lindner, SE & Honavar, V 2016, 'PlasmoSEP: Predicting surface-exposed proteins on the malaria parasite using semisupervised self-training and expert-annotated data', Proteomics, vol. 16, no. 23, pp. 2967-2976. https://doi.org/10.1002/pmic.201600249
Dobbs, D, Brenner, SE, Honavar, VG, Jernigan, RL, Laederach, A & Morris, Q 2016, Regulatory RNA. in Pacific Symposium on Biocomputing 2016, PSB 2016. World Scientific Publishing Co. Pte Ltd, pp. 429-432, 21st Pacific Symposium on Biocomputing, PSB 2016, Big Island, United States, 1/4/16.
Santhanam, GR, Basu, S & Honavar, V 2016, Representing and Reasoning with Qualitative Preferences: Tools and Applications. in WW Cohen, RJ Brachman & P Stone (eds), Representing and Reasoning with Qualitative Preferences: Tools and Applications. Synthesis Lectures on Artificial Intelligence and Machine Learning, vol. 31, Morgan and Claypool Publishers, pp. 1-154. https://doi.org/10.2200/S00689ED1V01Y201512AIM031
Santhanam, GR, Basu, S & Honavar, V 2016, Representing and reasoning with qualitative preferences: Tools and applications. Synthesis Lectures on Artificial Intelligence and Machine Learning, vol. 10, Morgan and Claypool Publishers.
Bui, N, Yen, J & Honavar, V 2016, 'Temporal causality analysis of sentiment change in a cancer survivor network', IEEE Transactions on Computational Social Systems, vol. 3, no. 2, 7539545, pp. 75-87. https://doi.org/10.1109/TCSS.2016.2591880

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