David Noya
Impact in
- Genetics top 10%
- Neurogenetic and Muscular Disorders Research
- Physiology top 10%
- Adipose Tissue and Metabolism
Papers in
-
- Molecular Biology Techniques and Applications 2
- Advanced biosensing and bioanalysis techniques 1
- Genomics and Phylogenetic Studies 1
- RNA modifications and cancer 1
- Peroxisome Proliferator-Activated Receptors 1
- Genetics 3
- Genetic Mapping and Diversity in Plants and Animals 1
- Co-authors
- Julie R. Korenberg (6 shared papers)J K Reddy (1 shared paper)Rao Ms (1 shared paper)Yi Zhu (1 shared paper)Chao Qi (1 shared paper)Xiao–Ning Chen (2 shared papers)Christine J. DiDonato (1 shared paper)Louise R. Simard (1 shared paper)
- Journals
- Genome Research (2 papers)Genomics (2 papers)Mammalian Genome (1 paper)Proceedings of the National Academy of Sciences (1 paper)
- Partner nations
- United StatesCanada
In The Last Decade
David Noya
6 papers receiving 807 citations
David Noya's Hit Papers
Peers
Comparison fields: 5 of 69
- Genetics 120
- Physiology 284
- Molecular Biology 712
- Biochemistry 50
- Epidemiology 158
Countries citing papers authored by David Noya
This map shows the geographic impact of David Noya's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Noya with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Noya more than expected).
Fields of papers citing papers by David Noya
This network shows the impact of papers produced by David Noya. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Noya. The network helps show where David Noya may publish in the future.
Co-authors
The 25 scholars most cited alongside David Noya, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Structural organization of mouse peroxisome proliferator-activated receptor gamma (mPPAR gamma) gene: alternative promoter use and different splicing yield two mPPAR gamma isoforms. Hit paper breakdown → | 1995 | 576 |
| 2 | 1997 | 104 | |
| 3 | 1996 | 66 | |
| 4 | 1999 | 35 | |
| 5 | 1997 | 33 | |
| 6 | 1994 | 19 |
About David Noya
David Noya is a scholar working on Molecular Biology, Genetics, Plant Science, Genetics and Cellular and Molecular Neuroscience, having authored 6 papers that have together received 833 indexed citations. Recurring topics across this work include Molecular Biology Techniques and Applications (2 papers), Chromosomal and Genetic Variations (2 papers), Advanced biosensing and bioanalysis techniques (1 paper), Genetic Mapping and Diversity in Plants and Animals (1 paper), Genomics and Phylogenetic Studies (1 paper), RNA modifications and cancer (1 paper), Peroxisome Proliferator-Activated Receptors (1 paper) and Down syndrome and intellectual disability research (1 paper). The work is most often cited by research in Genetics (120 citations), Physiology (284 citations), Molecular Biology (712 citations), Biochemistry (50 citations) and Epidemiology (158 citations). David Noya has collaborated with scholars based in United States and Canada. Frequent co-authors include Julie R. Korenberg, J K Reddy, Rao Ms, Yi Zhu, Chao Qi, Xiao–Ning Chen, Christine J. DiDonato, Louise R. Simard, Joseph H. Nadeau and Joyce Miller. Their work appears in journals such as Genome Research, Genomics, Mammalian Genome and Proceedings of the National Academy of Sciences.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.