What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
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 most cited work include:
- Feature subset selection using a genetic algorithm (1156 citations)
- Learn++: an incremental learning algorithm for supervised neural networks (648 citations)
- Mobile health technology evaluation: The mHealth evidence workshop (547 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (46.87%)
- Machine learning (25.86%)
- Theoretical computer science (12.12%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (46.87%)
- Machine learning (25.86%)
- Theoretical computer science (12.12%)
In recent papers he was focusing on the following fields of study:
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.
Between 2013 and 2021, his most popular works were:
- Computational prediction of protein interfaces: A review of data driven methods (90 citations)
- RNABindRPlus: a predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted RNA-binding residues in proteins. (66 citations)
- RNABindRPlus: a predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted RNA-binding residues in proteins. (66 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
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.
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