What is she best known for?
The fields of study she is best known for:
- Gene
- Bioinformatics
- Genetics
The scientist’s investigation covers issues in Bioinformatics, Gene expression profiling, Computational biology, Functional genomics and Genomics.
Her study in Bioinformatics is interdisciplinary in nature, drawing from both Minimum information about a microarray experiment and Data integration.
Her study explores the link between Gene expression profiling and topics such as Microarray analysis techniques that cross with problems in Cellular differentiation and Genome.
Helen Parkinson interconnects Controlled vocabulary, Genetics, ENCODE and Data curation in the investigation of issues within Computational biology.
When carried out as part of a general Genetics research project, her work on Human genome and Penetrance is frequently linked to work in Genome-wide association study, therefore connecting diverse disciplines of study.
Her Genomics study integrates concerns from other disciplines, such as Systems biology and Data science.
Her most cited work include:
- Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. (3539 citations)
- The NHGRI GWAS Catalog, a curated resource of SNP-trait associations (2225 citations)
- The Gene Ontology Resource: 20 years and still GOing strong (1527 citations)
What are the main themes of her work throughout her whole career to date?
Helen Parkinson mostly deals with Ontology, Data science, Computational biology, Information retrieval and Bioinformatics.
The various areas that Helen Parkinson examines in her Ontology study include Annotation and World Wide Web.
Her work on Interoperability as part of general World Wide Web research is often related to Reuse, thus linking different fields of science.
Her Data science research is multidisciplinary, incorporating perspectives in Relevance, Functional genomics, Genomics, Resource and The Internet.
As a part of the same scientific study, Helen Parkinson usually deals with the Computational biology, concentrating on Phenotype and frequently concerns with Data integration.
Her Bioinformatics research is multidisciplinary, incorporating elements of Minimum information about a microarray experiment, Microarray analysis techniques and Microarray databases.
She most often published in these fields:
- Ontology (23.94%)
- Data science (22.54%)
- Computational biology (21.13%)
What were the highlights of her more recent work (between 2018-2021)?
- Computational biology (21.13%)
- Metadata (7.04%)
- Data science (22.54%)
In recent papers she was focusing on the following fields of study:
Helen Parkinson focuses on Computational biology, Metadata, Data science, Gene and Genome-wide association study.
Her Computational biology study combines topics in areas such as Gene ontology annotation, Gene expression, Gene ontology and Dementia, Disease.
Helen Parkinson studied Metadata and The Internet that intersect with Database, Data exchange and Use case.
She has researched Data science in several fields, including Field and Genetic risk.
Her study on In silico, Genome editing, Genome and Model organism is often connected to Identification as part of broader study in Gene.
Her work on Candidate gene and Bone remodeling as part of general Genetics study is frequently connected to Trait, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Between 2018 and 2021, her most popular works were:
- The Gene Ontology Resource: 20 years and still GOing strong (1527 citations)
- The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. (1035 citations)
- LifeTime and improving European healthcare through cell-based interceptive medicine. (27 citations)
In her most recent research, the most cited papers focused on:
- Gene
- Bioinformatics
- Genetics
Her primary scientific interests are in Computational biology, Metadata, Genome-wide association study, Gene and Data science.
Her work deals with themes such as In silico, Disease, Human genetics and DNA sequencing, which intersect with Computational biology.
The study incorporates disciplines such as Use case, Data exchange and The Internet in addition to Metadata.
You can notice a mix of various disciplines of study, such as Expression quantitative trait loci, Summary statistics, External Data Representation, Data access and Exome, in her Genome-wide association study studies.
Her work on Genome project, Model organism, Genome and Genome editing is typically connected to Identification as part of general Gene study, connecting several disciplines of science.
Her work is dedicated to discovering how Data science, Precision medicine are connected with Genomics and other disciplines.
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.
