Vasant Honavar

Huck Chair in Biomedical Data Sciences and AI; Professor and Edward Frymoyer Chair of Information Sciences and Technology

Vasant Honavar

Research Summary

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.

Huck Affiliations

Links

Publication Tags

These publication tags are generated from the output of this researcher. Click any tag below to view other Huck researchers working on the same topic.

Proteins Machine Learning Health Protein Rna Experiments Scanning Sequence Homology Telescopes Artificial Intelligence Datasets Conformations Therapeutics Social Media Classifier Rna Binding Proteins Decision Making Sleep Evidence Visualization Model Ovarian Neoplasms Education Reinforcement Learning Graph In Graph Theory

Most Recent Publications

Forecasting User Interests Through Topic Tag Predictions in Online Health Communities

Amogh Subbakrishna Adishesha, Lily Jakielaszek, Fariha Azhar, Peixuan Zhang, Vasant Honavar, Fenglong Ma, Chandra Belani, Prasenjit Mitra, Sharon Xiaolei Huang, 2023, IEEE Journal of Biomedical and Health Informatics on p. 3645-3656

Machine learning approaches in sleep and circadian research

Margeaux M. Schade, Daniel M. Roberts, Vasant G. Honavar, Orfeu M. Buxton, 2023, on p. 53-62

MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein–Protein Docking Conformations

Yong Jung, Cunliang Geng, Alexandre M.J.J. Bonvin, Li C. Xue, Vasant G. Honavar, 2023, Biomolecules

Performance Evaluation of a 24-hour Sleep-Wake State Classifier Derived from Research-Grade Actigraphy

DM Roberts, MM Schade, A Chang, Vasant Honavar, D Gartenberg, O Buxton, 2022, Sleep on p. A46-

Child Overdoses Amid the Deaths-of-Despair Epidemic: Racial and Ethnic Differences in Intergenerational and Network Diffusion

C Graif, Christopher , Vasant Honavar, 2022, on p. https://paa.confex.com/paa/2022/meetingapp.cgi/Paper/26722

Variational Graph Auto-Encoders for Heterogeneous Information Network

Abhishek Dalvi, Ayan Acharya, Jing Gao, Vasant Honavar, 2022, on p. 12

Detecting and Interpreting Changes in Scanning Behavior in Large Network Telescopes

Michalis Kallitisis, Michalis Kallitsis, Rupesh Prajapati, Rupesh , Vasant Honavar, Dinghao Wu, John Yen, 2022, IEEE Transactions on Information Forensics and Security on p. pp. 3611-3625

Connected in health: Place-to-place commuting networks and COVID-19 spillovers

Christopher Seto, Corina Graif, Aria Khademi, Claire Kelling, Vasant G. Honavar, Claire E. Kelling, Vassant Honavar, 2022, Health and Place

Interconnected Places: Commuting Networks and COVID-19 Spillovers

Christopher Seto, Aria Khademi, C Graif, Vasant Honavar, 2022, American Journal of Epidemiology

Shedding light into the darknet: scanning characterization and detection of temporal changes

Rupesh Prajapati, Vasant Honavar, Dinghao Wu, John Yen, Michalis Kallitsis, 2021, on p. 469-470

Most-Cited Papers

Mobile health technology evaluation: The mHealth evidence workshop

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

Computational prediction of protein interfaces: A review of data driven methods

Li C. Xue, Drena Dobbs, Alexandre M J J Bonvin, Vasant Honavar, 2015, FEBS Letters on p. 3516-3526

RNABindRPlus: A predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted RNA-binding residues in proteins

Rasna R. Walia, Li C. Xue, Katherine Wilkins, Yasser El-Manzalawy, Drena Dobbs, Vasant Honavar, 2014, PLoS One on p. e97725

Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach

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

Fairness in algorithmic decision making: An excursion through the lens of causality

Aria Khademi, David Foley, Sanghack Lee, Vasant Honavar, 2019, on p. 2907-2914

Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data 06 Biological Sciences 0604 Genetics

Yasser El-Manzalawy, Tsung Yu Hsieh, Manu Shivakumar, Dokyoon Kim, Vasant Honavar, 2018, BMC Medical Genomics on p. 71

In silico prediction of linear B-cell epitopes on proteins

Yasser El-Manzalawy, Drena Dobbs, Vasant G. Honavar, 2017, on p. 255-264

Improving image captioning by leveraging knowledge graphs

Yimin Zhou, Yiwei Sun, Vasant Honavar, 2019, on p. 283-293

Megan: A generative adversarial network for multi-view network embedding

Yiwei Sun, Suhang Wang, Tsung Yu Hsieh, Xianfeng Tang, Vasant Honavar, 2019, on p. 3527-3533

News Articles Featuring Vasant Honavar

Sep 16, 2022

Millennium Café series to feature special editions in October, November

The Millennium Café, held every Tuesday by the Materials Research Institute (MRI) featuring two talks by Penn State researchers that serve as an exchange of ideas and solutions, will hold three special sessions in October and November.

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Oct 04, 2021

Vasant Honavar named Huck Chair in Biomedical Data Sciences and AI

Vasant Honavar, professor in the College of Information Sciences and Technology, has been named the Dorothy Foehr Huck and J. Lloyd Huck Chair in Biomedical Data Sciences and Artificial Intelligence by the University’s Huck Institutes of the Life Sciences.

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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.

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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?

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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.

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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.

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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.

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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.

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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.

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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.​

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