What is he best known for?
The fields of study he is best known for:
Genetics, Annotation, Protein function prediction, Candidate gene and Gene are his primary areas of study.
RNA splicing, Exonic splicing enhancer, Gene mutation, Mutation and Phenotype are among the areas of Genetics where Sean D. Mooney concentrates his study.
The various areas that Sean D. Mooney examines in his Annotation study include Machine learning, Computational biology and Molecular Sequence Annotation.
His research investigates the link between Computational biology and topics such as Ontology that cross with problems in Support vector machine.
His Candidate gene research includes elements of Trisomy, Heart septal defect and Case-control study.
His work on Human genetics and RNA interference is typically connected to Critical assessment and Experimental data as part of general Gene study, connecting several disciplines of science.
His most cited work include:
- Comprehensive molecular portraits of human breast tumours (7253 citations)
- A large-scale evaluation of computational protein function prediction (624 citations)
- Automated inference of molecular mechanisms of disease from amino acid substitutions (586 citations)
What are the main themes of his work throughout his whole career to date?
Sean D. Mooney mainly focuses on Genetics, Computational biology, Genome, Gene and Data science.
His Genetics study focuses mostly on Mutation, Phenotype, Allele, Missense mutation and In silico.
His Computational biology study also includes fields such as
- Protein structure which is related to area like Amino acid,
- Single-nucleotide polymorphism that connect with fields like Candidate gene.
Many of his studies on Genome apply to Exome as well.
With his scientific publications, his incorporates both Gene and Critical assessment.
His biological study spans a wide range of topics, including Genetic variation, Bioinformatics and Genomics.
He most often published in these fields:
- Genetics (32.32%)
- Computational biology (25.25%)
- Genome (12.63%)
What were the highlights of his more recent work (between 2018-2021)?
- Computational biology (25.25%)
- Genome (12.63%)
- Disease (7.58%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Computational biology, Genome, Disease, Data science and Generalizability theory.
His Computational biology study combines topics from a wide range of disciplines, such as Phenotype, In silico, Gene and Uncertain significance.
His Genome study introduces a deeper knowledge of Genetics.
His studies in Disease integrate themes in fields like Precision medicine and Internet privacy.
He has included themes like Translational research and Dream in his Data science study.
In his study, Ontology is inextricably linked to Annotation, which falls within the broad field of Human genetics.
Between 2018 and 2021, his most popular works were:
- The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens (82 citations)
- SILAC Analysis Reveals Increased Secretion of Hemostasis-Related Factors by Senescent Cells. (34 citations)
- Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. (32 citations)
In his most recent research, the most cited papers focused on:
Sean D. Mooney spends much of his time researching Computational biology, Gene, Genome, Critical assessment and Generalizability theory.
His Computational biology study combines topics in areas such as Amino acid, Phenotype, Uncertain significance and Clinical significance.
His work on Protein function prediction and Novel gene as part of general Gene research is frequently linked to Long-term memory, thereby connecting diverse disciplines of science.
He interconnects Biological process and Molecular function in the investigation of issues within Protein function prediction.
His research integrates issues of Mutation, De novo mutations and Disease in his study of Genome.
The study incorporates disciplines such as Annotation, Cellular component, Mutation screening and Human genetics in addition to Function.
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