2018 - Fellow of the American Association for the Advancement of Science (AAAS)
2018 - ACM Distinguished Member
2006 - ACM Senior Member
Vasant Honavar mainly focuses on Artificial intelligence, Machine learning, Computational biology, Pattern recognition and Data mining. Artificial intelligence and Drug design are frequently intertwined in his study. His biological study spans a wide range of topics, including Exploit and Training set.
His Computational biology study integrates concerns from other disciplines, such as Genetics, DNA microarray, Protein sequencing and A protein. In the field of Pattern recognition, his study on Kernel overlaps with subjects such as Subsequence. Vasant Honavar usually deals with Data mining and limits it to topics linked to Feature vector and Data warehouse and Distributed knowledge.
His primary areas of study are Artificial intelligence, Machine learning, Theoretical computer science, Pattern recognition and Data mining. His Natural language processing research extends to Artificial intelligence, which is thematically connected. His Machine learning study combines topics in areas such as Classifier, Algorithm, Knowledge extraction and Taxonomy.
His work deals with themes such as Ontology and Set, which intersect with Theoretical computer science. Ontology is closely attributed to Knowledge acquisition in his study. Vasant Honavar interconnects Semantics and Semantic Web in the investigation of issues within Description logic.
His scientific interests lie mostly in Artificial intelligence, Machine learning, Theoretical computer science, Computational biology and Big data. His Artificial intelligence study combines topics from a wide range of disciplines, such as Protein structure, Protein structure prediction, Sequence alignment and Pattern recognition. His work in the fields of Artificial neural network overlaps with other areas such as Actigraphy.
The concepts of his Theoretical computer science study are interwoven with issues in Graph, Network embedding, Correctness, Graph and Causal model. His research in Computational biology intersects with topics in Docking, Bioinformatics, Identification, Peptide sequence and Feature selection. His study focuses on the intersection of Big data and fields such as Data science with connections in the field of Information processing and Data management.
Vasant Honavar mainly investigates Computational biology, Artificial intelligence, Docking, Theoretical computer science and Machine learning. Vasant Honavar combines subjects such as Identification, Epitope, Antigen, Peptide sequence and Feature selection with his study of Computational biology. His Artificial intelligence research incorporates elements of Protein structure and Protein structure prediction.
His studies deal with areas such as A protein and Protein–protein interaction as well as Docking. His research integrates issues of Graph, Network embedding, Open-world assumption and Private information retrieval in his study of Theoretical computer science. His Machine learning research is multidisciplinary, relying on both Classifier, Prima facie and Online community.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Feature subset selection using a genetic algorithm
J. Yang;V. Honavar.
IEEE Intelligent Systems & Their Applications (1998)
Learn++: an incremental learning algorithm for supervised neural networks
R. Polikar;L. Upda;S.S. Upda;V. Honavar.
systems man and cybernetics (2001)
Mobile health technology evaluation: The mHealth evidence workshop
Santosh Kumar;Wendy J. Nilsen;Amy Abernethy;Audie Atienza.
American Journal of Preventive Medicine (2013)
Predicting linear B-cell epitopes using string kernels.
Yasser El-Manzalawy;Drena Dobbs;Vasant Honavar.
Journal of Molecular Recognition (2008)
Predicting RNA-Protein Interactions Using Only Sequence Information
Usha K Muppirala;Vasant G Honavar;Drena Dobbs.
BMC Bioinformatics (2011)
Constructive neural-network learning algorithms for pattern classification
R. Parekh;J. Yang;V. Honavar.
IEEE Transactions on Neural Networks (2000)
Grammar Inference Automata Induction and Language Acquisition
Rajesh Parekh;Vasant Honavar.
Intelligent agents for intrusion detection
G.G. Helmer;J.S.K. Wong;V. Honavar;L. Miller.
Information Technology (1998)
Lightweight agents for intrusion detection
Guy Helmer;Johnny S. K. Wong;Vasant G. Honavar;Les Miller.
Journal of Systems and Software (2003)
RNABindR: a server for analyzing and predicting RNA-binding sites in proteins.
Michael Terribilini;Jeffry D. Sander;Jae-Hyung Lee;Peter Zaback.
Nucleic Acids Research (2007)
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