What is she best known for?
The fields of study she is best known for:
- Gene
- Genetics
- Internal medicine
The scientist’s investigation covers issues in Landmark, Phenotype, Anatomy, Craniosynostosis and Genetics.
Her Landmark study is focused on Artificial intelligence in general.
Her study in Phenotype is interdisciplinary in nature, drawing from both Morphological integration, Ecology, Craniofacial and Cell biology.
Her Craniosynostosis research incorporates elements of Odds ratio and Skull.
Her research in Skull intersects with topics in Neural crest and Premature Closure.
Joan T. Richtsmeier has included themes like Orientation, Class, Metric and Natural language processing in her Biological data study.
Her most cited work include:
- Precision and Error of Three-dimensional Phenotypic Measures Acquired from 3dMD Photogrammetric Images (277 citations)
- Precision and Error of Three-dimensional Phenotypic Measures Acquired from 3dMD Photogrammetric Images (277 citations)
- The promise of geometric morphometrics (271 citations)
What are the main themes of her work throughout her whole career to date?
Joan T. Richtsmeier mainly investigates Anatomy, Craniofacial, Skull, Craniosynostosis and Genetics.
Her research in the fields of Neurocranium and Cranial vault overlaps with other disciplines such as Brain morphometry.
Joan T. Richtsmeier combines subjects such as Crania, Orthodontics and Pathology with her study of Craniofacial.
Her Skull research includes elements of Endocrinology, Internal medicine and Apert syndrome.
Her studies in Craniosynostosis integrate themes in fields like Fibrous joint, Bioinformatics and Premature Closure.
Her research related to Chromosome 21, Aneuploidy, Down syndrome, Trisomy and Phenotype might be considered part of Genetics.
She most often published in these fields:
- Anatomy (44.39%)
- Craniofacial (43.90%)
- Skull (37.07%)
What were the highlights of her more recent work (between 2016-2021)?
- Evolutionary biology (12.68%)
- Anatomy (44.39%)
- Craniosynostosis (29.76%)
In recent papers she was focusing on the following fields of study:
Her main research concerns Evolutionary biology, Anatomy, Craniosynostosis, Craniofacial and Skull.
Her study in Evolutionary biology is interdisciplinary in nature, drawing from both Phenotype, Heterochrony, Ontogeny and Developmental biology.
Her study in the fields of Chondrocranium and Cortical bone under the domain of Anatomy overlaps with other disciplines such as Materials science.
The concepts of her Craniosynostosis study are interwoven with issues in Pediatrics, Intramembranous ossification and Dysgenesis.
She has included themes like Trisomy, Cranial vault, Sibling and Significant difference in her Craniofacial study.
Many of her research projects under Skull are closely connected to Referral with Referral, tying the diverse disciplines of science together.
Between 2016 and 2021, her most popular works were:
- Building bones: Bone formation and development in anthropology (18 citations)
- Ontogenetic and Genetic Influences on Bone's Responsiveness to Mechanical Signals (13 citations)
- Midface and upper airway dysgenesis in FGFR2-related craniosynostosis involves multiple tissue-specific and cell cycle effects. (12 citations)
In her most recent research, the most cited papers focused on:
- Gene
- Genetics
- Internal medicine
Joan T. Richtsmeier mostly deals with Craniofacial, Evolutionary biology, Skull, Apert syndrome and Anatomy.
Joan T. Richtsmeier combines subjects such as Trisomy, Sibling, Significant difference and Selection with her study of Craniofacial.
Her work deals with themes such as Zoology, Ontogeny, Genetic correlation and Allometry, which intersect with Evolutionary biology.
Her Skull research is multidisciplinary, incorporating elements of Facial skeleton and Primate.
Her Apert syndrome research is included under the broader classification of Craniosynostosis.
Her work on Chondrocranium is typically connected to Association and Fibroblast growth factor receptor as part of general Anatomy study, connecting several disciplines of science.
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.
