What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Database
- Machine learning
His primary areas of investigation include Information retrieval, Electrolyte, Inorganic chemistry, Analytical chemistry and Electrochemistry.
His studies deal with areas such as Human proteins, Proteomics, Artificial intelligence and Natural language processing as well as Information retrieval.
The study incorporates disciplines such as Word, Spelling and Information processing in addition to Natural language processing.
His study looks at the relationship between Electrolyte and topics such as Supercapacitor, which overlap with Pulsed power, Duty cycle and Composite material.
His Inorganic chemistry research integrates issues from Working electrode, Double-layer capacitance and Reference electrode.
In Analytical chemistry, he works on issues like Cyclic voltammetry, which are connected to Graphite.
His most cited work include:
- Overview of BioCreative II gene normalization. (281 citations)
- The gene normalization task in BioCreative III (190 citations)
- The COMBREX Project: Design, Methodology, and Initial Results (135 citations)
What are the main themes of his work throughout his whole career to date?
Patrick Ruch spends much of his time researching Information retrieval, Artificial intelligence, Natural language processing, Data science and Controlled vocabulary.
The Information retrieval study combines topics in areas such as Annotation, Categorization and Subject.
His work in Annotation covers topics such as Information extraction which are related to areas like Precision and recall.
His research investigates the connection with Artificial intelligence and areas like Named-entity recognition which intersect with concerns in Language model.
His Natural language processing research includes elements of Domain and Argumentative.
Patrick Ruch has researched Data science in several fields, including Health informatics and World Wide Web.
He most often published in these fields:
- Information retrieval (50.86%)
- Artificial intelligence (34.05%)
- Natural language processing (28.02%)
What were the highlights of his more recent work (between 2016-2021)?
- Information retrieval (50.86%)
- Artificial intelligence (34.05%)
- Text mining (9.05%)
In recent papers he was focusing on the following fields of study:
Patrick Ruch mainly focuses on Information retrieval, Artificial intelligence, Text mining, Annotation and Relevance.
His work on Ontology is typically connected to NeXtProt as part of general Information retrieval study, connecting several disciplines of science.
His work deals with themes such as UniProt, Named-entity recognition and Natural language processing, which intersect with Artificial intelligence.
Patrick Ruch combines subjects such as Web mining and Search engine with his study of Text mining.
He has researched Annotation in several fields, including Terminology, Internal medicine and Oncology.
His research in Relevance intersects with topics in Scalability, World Wide Web, Ranking, Workflow and Process.
Between 2016 and 2021, his most popular works were:
- UniProt: the universal protein knowledgebase in 2021 (57 citations)
- DisProt: intrinsic protein disorder annotation in 2020. (51 citations)
- Text mining to support gene ontology curation and vice versa (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Database
- Machine learning
Patrick Ruch focuses on Information retrieval, Artificial intelligence, Data curation, Annotation and Natural language processing.
Patrick Ruch interconnects Context and Named-entity recognition in the investigation of issues within Artificial intelligence.
His Data curation research includes elements of Ontology and Workflow.
His Workflow research focuses on Biological data and how it connects with Data science and Web mining.
His biological study spans a wide range of topics, including Precision medicine, Ranking and Molecular Sequence Annotation.
His Natural language processing research incorporates themes from Value, French, Categorization and Ambiguity.
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