Professor and Edward Frymoyer Chair of Information Sciences and Technology
Statistical machine learning algorithms for predictive modeling; modeling and inference of biological networks; characterization and prediction of protein-protein, protein-RNA, and protein-DNA interactions.
- Center for Molecular Immunology and Infectious Disease
- Bioinformatics and Genomics
- Microbiome Center
- Neuroscience Institute
Publication TagsProteins Rna Protein Machine Learning Conformations Prediction Classifiers Amino Acid Sequence Homology Sequence Homology Learning Systems Interaction Experiments Scoring Nucleic Acid Databases Datasets Rna Binding Proteins Time Series Perovskites Model Structural Similarity Reinforcement Learning Telemedicine Kernel Homology Artificial Intelligence
Most Recent Papers
Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns to Attend to Important Variables As Well As Time Intervals
Tsung Yu Hsieh, Suhang Wang, Yiwei Sun, Vasant Honavar, 2021, on p. 607-615
Tsung Yu Hsieh, Yiwei Sun, Xianfeng Tang, Suhang Wang, Vasant G. Honavar, 2021, on p. 2270-2280
Longitudinal Deep Kernel Gaussian Process Regression
Junjie Liang, Yanting Wu, Dongkuan Xu, Vasant Honavar, 2021,
Dynamical Gaussian Process Latent Variable Model for Representation Learning from Longitudinal Data
Thanh Le, Vasant Honavar, 2020, on p. 183-188
Two-dimensional hybrid organic-inorganic perovskites as emergent ferroelectric materials
Yuchen Hou, Congcong Wu, Dong Yang, Tao Ye, Vasant G. Honavar, Adri C.T. Van Duin, Kai Wang, Shashank Priya, 2020, Journal of Applied Physics
The Virtual Data Collaboratory
Manish Parashar, Anthony Simonet, Ivan Rodero, Forough Ghahramani, Grace Agnew, Ron Jantz, Vasant Honavar, 2020, Computing in Science and Engineering on p. 79-92
Adversarial Attacks on Graph Neural Networks via Node Injections
Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung Yu Hsieh, Vasant Honavar, 2020, on p. 673-683
IScore: A novel graph kernel-based function for scoring protein-protein docking models
Cunliang Geng, Yong Jung, Nicolas Renaud, Vasant Honavar, Alexandre M.J.J. Bonvin, Li C. Xue, 2020, Bioinformatics on p. 112-121
LMLFM: Longitudinal Multi-Level Factorization Machine
Junjie Liang, Dongkuan Xu, Yiwei Sun, Vasant Honavar, 2020, on p. 4811--4818
A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution
Aria Khademi, Vasant Honavar, 2020, arXiv preprint arXiv:2008.00357
Mobile health technology evaluation
Santosh Kumar, Wendy J. Nilsen, Amy Abernethy, Audie Atienza, Kevin Patrick, Misha Pavel, William T. Riley, Albert Shar, Bonnie Spring, Donna Spruijt-Metz, Donald Hedeker, Vasant Honavar, Richard Kravitz, R. Craig Lefebvre, David C. Mohr, Susan A. Murphy, Charlene Quinn, Vladimir Shusterman, Dallas Swendeman, 2013, American Journal of Preventive Medicine on p. 228-236
Predicting RNA-Protein Interactions Using Only Sequence Information
Usha K. Muppirala, Vasant G. Honavar, Drena Dobbs, 2011, BMC Bioinformatics
Benjamin A. Lewis, Rasna R. Walia, Michael Terribilini, Jeff Ferguson, Charles Zheng, Vasant Honavar, Drena Dobbs, 2011, Nucleic Acids Research on p. D277-D282
Computational prediction of protein interfaces
Li C. Xue, Drena Dobbs, Alexandre M J J Bonvin, Vasant Honavar, 2015, FEBS Letters on p. 3516-3526
Li C. Xue, Drena Dobbs, Vasant Honavar, 2011, BMC Bioinformatics
RNABindRPlus: a predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted RNA-binding residues in proteins.
R Walia, Lingzhou Xue, K Wilkins, Yasser Elmanzalawi, D Dobbs, Vasant Honavar, 2014, PLoS One on p. e97725
Predicting protein-protein interface residues using local surface structural similarity
Rafael A. Jordan, Yasser El-Manzalawy, Drena Dobbs, Vasant Honavar, 2012, BMC Bioinformatics
Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art
Rasna R. Walia, Cornelia Caragea, Benjamin A. Lewis, Fadi Towfic, Michael Terribilini, Yasser El-Manzalawy, Drena Dobbs, Vasant Honavar, 2012, BMC Bioinformatics
Li C. Xue, Rafael A. Jordan, El Manzalawy Yasser, Drena Dobbs, Vasant Honavar, 2014, Proteins: Structure, Function and Genetics on p. 250-267
Unambiguity regularization for unsupervised learning of probabilistic grammars
Kewei Tu, Vasant Honavar, 2012, on p. 1324-1334
News Articles Featuring Vasant Honavar
Jul 17, 2019
Researchers deploy AI to detect bias in AI and humans
Researchers have developed a tool using artificial intelligence (AI) to detect unfair bias in protected areas such as race or gender. The tool could be used in finding bias in AI systems or even bias by human decisions makers, according to the researchers at Penn State and Columbia University.
Jul 17, 2019
What is bias in AI really, and why can’t AI neutralize it?
Selection algorithms everywhere are exhibiting traits that appear to be racist, sexist, and otherwise discriminatory. Have neural networks already developed their own neuropathy? Or are people somehow the problem?
Jul 15, 2019
AI to fight unfair discrimination
Researchers developed a new artificial intelligence (AI) tool for detecting unfair discrimination such as race or gender.
Jul 12, 2019
Penn State researchers develop bias-detecting technology
Researchers at Penn State and Columbia University have developed artificial intelligence technology that can detect unfair discrimination within specific demographics.
Jul 12, 2019
Artificial intelligence tool can identify gender and racial bias
Scientists have developed a new artificial intelligence (AI) tool for detecting unfair discrimination—such as on the basis of race or gender.
Jul 11, 2019
Researchers Create New AI Tool for Detecting Unfair Discrimination
Penn State and Columbia University researchers have created a new artificial intelligence tool to detect unfair discrimination based on gender and race. For example, a long-standing concern of civilized societies has been preventing unfair treatment of individuals based on gender, race, or ethnicity.
Jul 11, 2019
US researchers create AI tool that can detect discrimination on basis of race, gender
A team of researchers at Pennsylvania State and Columbia University created an artificial intelligence (AI) tool for detecting discrimination with respect to a protected attribute, such as race or gender.
Apr 02, 2019
'AI will see you now': Panel to discuss the AI revolution in health and medicine
This month’s CyberScience Seminar, organized by the Institute for CyberScience (ICS), will be held from 1:30–3 p.m. on Thursday, April 11, in 233B HUB-Robeson Center and will feature a panel of Penn State experts who will discuss the benefits — and the risks — of using AI in the healthcare industry.
Feb 20, 2019
Is artificial intelligence affecting the job market?
The term Artificial Intelligence elicits images of cyborgs and terminators; human-like robots intent on pursuing death and destruction. Hollywood representations distort what AI really is and the technological achievements that scientists are making.