What is he best known for?
The fields of study he is best known for:
- Gene
- Artificial intelligence
- Cancer
Casey S. Greene spends much of his time researching Computational biology, Artificial intelligence, Bioinformatics, Unsupervised learning and Genomics.
His Computational biology research is multidisciplinary, incorporating perspectives in Genetics, Expression data, Transcriptome, Data integration and Gene regulatory network.
His work in Gene regulatory network addresses issues such as Multicellular organism, which are connected to fields such as Cell type, Gene expression profiling, Genome-wide association study and Disease.
His Artificial intelligence study integrates concerns from other disciplines, such as Field, Machine learning, Protein function prediction and Data science.
His biological study spans a wide range of topics, including In silico, Organ Specificity, Cellular differentiation and Kidney disease.
In his study, which falls under the umbrella issue of Genomics, Web server and Visualization is strongly linked to Compendium.
His most cited work include:
- Oncogenic Signaling Pathways in The Cancer Genome Atlas (792 citations)
- Opportunities and obstacles for deep learning in biology and medicine. (726 citations)
- Understanding multicellular function and disease with human tissue-specific networks (472 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computational biology, Machine learning, Gene and Data mining are his primary areas of study.
The Artificial intelligence study combines topics in areas such as Human genetics and Pattern recognition.
His Computational biology research is multidisciplinary, incorporating elements of Genome-wide association study, Bioinformatics, Transcriptome, Genomics and Disease.
As part of the same scientific family, he usually focuses on Genomics, concentrating on Data science and intersecting with Precision medicine and Biomedicine.
His studies deal with areas such as Epistasis, Ant colony optimization algorithms and Human genome as well as Machine learning.
His research on Gene concerns the broader Genetics.
He most often published in these fields:
- Artificial intelligence (26.24%)
- Computational biology (26.24%)
- Machine learning (17.49%)
What were the highlights of his more recent work (between 2019-2021)?
- Data science (12.55%)
- Artificial intelligence (26.24%)
- Computational biology (26.24%)
In recent papers he was focusing on the following fields of study:
Casey S. Greene mainly investigates Data science, Artificial intelligence, Computational biology, Pandemic and Severe acute respiratory syndrome coronavirus 2.
Casey S. Greene has included themes like Biomedicine, Genomics and Knowledge graph in his Data science study.
His work carried out in the field of Artificial intelligence brings together such families of science as Structure, Machine learning and Pattern recognition.
His work on Deep neural networks and Interpretability as part of general Machine learning study is frequently linked to Biological structure and Benchmarking, therefore connecting diverse disciplines of science.
While the research belongs to areas of Computational biology, he spends his time largely on the problem of Gene, intersecting his research to questions surrounding Effector.
The various areas that Casey S. Greene examines in his Deep learning study include Frame and Precision medicine.
Between 2019 and 2021, his most popular works were:
- Transparency and reproducibility in artificial intelligence. (23 citations)
- Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing. (23 citations)
- The importance of transparency and reproducibility in artificial intelligence research (22 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Artificial intelligence
- Cancer
His primary areas of study are Pandemic, Artificial intelligence, Transparency, Severe acute respiratory syndrome coronavirus 2 and Disease.
The study incorporates disciplines such as Biomedicine and Pattern recognition in addition to Artificial intelligence.
His research in Biomedicine intersects with topics in Frame, Deep learning and Knowledge graph.
Transparency is connected with Reproducibility, Big data, Documentation, Usability and Open data in his research.
His studies link Coronavirus with Severe acute respiratory syndrome coronavirus 2.
The concepts of his Disease study are interwoven with issues in Vitamin D and neurology, Nutraceutical and Intensive care medicine.
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