What is she best known for?
The fields of study she is best known for:
Computational biology, Genetics, Gene, Gene expression profiling and Gene regulatory network are her primary areas of study.
The various areas that she examines in her Computational biology study include Genome-wide association study, Functional genomics, Genomics, Genomic data and Human genetics.
Her Genetics research integrates issues from Bayes' theorem and Cell biology.
Her study in the field of Synthetic genetic array, Genetic Fitness and Saccharomyces cerevisiae also crosses realms of Basis.
Her study in Gene expression profiling is interdisciplinary in nature, drawing from both Microarray, Pearson product-moment correlation coefficient and Cell type.
Her Gene regulatory network study integrates concerns from other disciplines, such as Transcription factor, Cell fate determination, Chromatin, Homeobox protein NANOG and Epigenome.
Her most cited work include:
- Missing value estimation methods for DNA microarrays. (2632 citations)
- The genetic landscape of a cell. (1828 citations)
- Diversity of gene expression in adenocarcinoma of the lung (1177 citations)
What are the main themes of her work throughout her whole career to date?
Olga G. Troyanskaya mostly deals with Computational biology, Genetics, Gene, Genome and Functional genomics.
Olga G. Troyanskaya has researched Computational biology in several fields, including Genome-wide association study, Gene expression profiling, Chromatin, Disease and Gene regulatory network.
Her biological study spans a wide range of topics, including Microarray analysis techniques and Cancer research.
In general Genetics, her work in Genomics, DNA microarray and Human genome is often linked to Compendium linking many areas of study.
Olga G. Troyanskaya has included themes like Proband, Model organism and Autism spectrum disorder in her Genome study.
Her Functional genomics research incorporates elements of Data mining and Biological data.
She most often published in these fields:
- Computational biology (37.31%)
- Genetics (25.37%)
- Gene (19.90%)
What were the highlights of her more recent work (between 2018-2021)?
- Computational biology (37.31%)
- Genome (17.41%)
- Artificial intelligence (12.44%)
In recent papers she was focusing on the following fields of study:
Her primary areas of investigation include Computational biology, Genome, Artificial intelligence, Gene and Kidney.
Her studies in Computational biology integrate themes in fields like Chromatin, Transcription factor, Disease and Epigenetics.
Her Genome study incorporates themes from RNA, Schizophrenia, Psychiatry and Small molecule.
Her study looks at the intersection of Artificial intelligence and topics like Machine learning with Genomics.
Her Gene study is associated with Genetics.
Her research in Genetics tackles topics such as Enzyme which are related to areas like Phenotype.
Between 2018 and 2021, her most popular works were:
- New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries (105 citations)
- Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. (98 citations)
- Organoid single cell profiling identifies a transcriptional signature of glomerular disease (28 citations)
In her most recent research, the most cited papers focused on:
- Gene
- Artificial intelligence
- DNA
The scientist’s investigation covers issues in Genome, Gene, RNA, Severe acute respiratory syndrome coronavirus 2 and Kidney.
Her study with Gene involves better knowledge in Genetics.
Her Genetics research is multidisciplinary, incorporating elements of Odds ratio, Heart disease and Confidence interval.
Her work deals with themes such as Exacerbation, Rheumatoid arthritis, Immunology, Arthritis and Small molecule, which intersect with RNA.
Her Kidney research also works with subjects such as
- In situ hybridization that connect with fields like Acute kidney injury, Diabetes mellitus and Diabetic nephropathy,
- Pathology which intersects with area such as Cell type, Renal function and Cell,
- Kidney disease which connect with Genotype phenotype, Biological data, Omics and Proteomics.
Her Disease study combines topics from a wide range of disciplines, such as Annotation and Computational biology.
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