What is he best known for?
The fields of study he is best known for:
- Gene
- Artificial intelligence
- Statistics
His primary areas of study are Genetics, Epistasis, Multifactor dimensionality reduction, Computational biology and Genetic association.
His research on Epistasis also deals with topics like
- Genetic architecture which connect with Evolutionary biology and Human genetics,
- Systems biology which is related to area like Gene regulatory network.
The concepts of his Multifactor dimensionality reduction study are interwoven with issues in Machine learning, Gene interaction and Data mining, Identification.
His Machine learning research focuses on subjects like Data science, which are linked to Artificial intelligence.
His work deals with themes such as Regulation of gene expression and Disease, which intersect with Computational biology.
His Genetic association study combines topics from a wide range of disciplines, such as Genome-wide association study, Genetic variation and Genetic testing.
His most cited work include:
- Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer (1597 citations)
- Missing heritability and strategies for finding the underlying causes of complex disease (1316 citations)
- The Genetic Structure and History of Africans and African Americans (1103 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Machine learning, Genetics, Computational biology and Epistasis are his primary areas of study.
His Artificial intelligence research integrates issues from Data mining and Human genetics.
Single-nucleotide polymorphism, Gene, Genome-wide association study, Allele and Genetic variation are among the areas of Genetics where he concentrates his study.
The study of Genome-wide association study is intertwined with the study of Genetic association in a number of ways.
Jason H. Moore combines subjects such as Bioinformatics, Robustness, Phenotype, Genome and Disease with his study of Computational biology.
His research integrates issues of Multifactor dimensionality reduction and Genetic architecture in his study of Epistasis.
He most often published in these fields:
- Artificial intelligence (25.16%)
- Machine learning (20.23%)
- Genetics (20.10%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (25.16%)
- Machine learning (20.23%)
- Genetic programming (11.28%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Machine learning, Genetic programming, Data science and Computational biology.
His Artificial intelligence study integrates concerns from other disciplines, such as Pattern recognition and Big data.
The various areas that Jason H. Moore examines in his Machine learning study include Tree, Context and Pipeline.
The Genetic programming study combines topics in areas such as Evolutionary computation, Set, Crossover and Benchmark.
His Software research extends to Data science, which is thematically connected.
His Computational biology research is multidisciplinary, incorporating elements of Phenotype, Gene, Genome-wide association study and Disease.
Between 2017 and 2021, his most popular works were:
- Relief-based feature selection: Introduction and review. (224 citations)
- TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning (167 citations)
- Data-driven advice for applying machine learning to bioinformatics problems. (70 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Artificial intelligence
- Statistics
His scientific interests lie mostly in Artificial intelligence, Machine learning, Genetic programming, Data mining and Data science.
His Artificial intelligence research incorporates elements of Generalization, Health records and Pipeline.
His biological study spans a wide range of topics, including Tree, Stochastic optimization and Variable.
The concepts of his Data mining study are interwoven with issues in Bioconductor, Sample size determination and Missing data.
His Single-nucleotide polymorphism study focuses on Gene and Genetics.
Epistasis is closely connected to Computational biology in his research, which is encompassed under the umbrella topic of Enhancer.
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.
