Saurabh Sinha
Impact in
- Biophysics top 0.5%
- Aging top 2%
Papers in
-
- Genomics and Chromatin Dynamics 49
- RNA and protein synthesis mechanisms 22
- RNA Research and Splicing 20
- Genomics and Phylogenetic Studies 17
- Bioinformatics and Genomic Networks 12
- Gene expression and cancer classification 10
- Genetics 19
- Insect and Arachnid Ecology and Behavior 9
- Co-authors
- Gene E. Robinson (16 shared papers)Martin Tompa (3 shared papers)Eric D. Siggia (4 shared papers)Charles Blatti (15 shared papers)ChengXiang Zhai (2 shared papers)Faraz Faghri (1 shared paper)Miles Efron (1 shared paper)Roy H. Campbell (1 shared paper)
- Journals
- Nucleic Acids Research (10 papers)PLoS Computational Biology (9 papers)Bioinformatics (8 papers)Proceedings of the National Academy of Sciences (7 papers)Genome biology (5 papers)
- Partner nations
- United StatesCanadaGermany
In The Last Decade
Saurabh Sinha
115 papers receiving 5.4k citations
Saurabh Sinha's Hit Papers
Peers
Comparison fields: 5 of 171
- Biophysics 351
- Aging 93
- Molecular Biology 3.4k
- Genetics 1.2k
- Developmental Biology 83
Countries citing papers authored by Saurabh Sinha
This map shows the geographic impact of Saurabh Sinha'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 Saurabh Sinha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Saurabh Sinha more than expected).
Fields of papers citing papers by Saurabh Sinha
This network shows the impact of papers produced by Saurabh Sinha. 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 Saurabh Sinha. The network helps show where Saurabh Sinha may publish in the future.
Co-authors
The 25 scholars most cited alongside Saurabh Sinha, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 120 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Big Data: Astronomical or Genomical? Hit paper breakdown → | 2015 | 755 |
| 2 | 2007 | 374 | |
| 3 | 2009 | 217 | |
| 4 | 2018 | 200 | |
| 5 | 2010 | 165 | |
| 6 | 2003 | 165 | |
| 7 | 2019 | 145 | |
| 8 | 2004 | 139 | |
| 9 | A statistical method for finding transcription factor binding sites. | 2000 | 137 |
| 10 | 2008 | 133 | |
| 11 | 2010 | 133 | |
| 12 | 2014 | 124 | |
| 13 | 2007 | 113 | |
| 14 | 2009 | 94 | |
| 15 | 2014 | 79 | |
| 16 | 2004 | 77 | |
| 17 | 2012 | 70 | |
| 18 | 2020 | 68 | |
| 19 | 2013 | 66 | |
| 20 | 2019 | 66 |
About Saurabh Sinha
Saurabh Sinha is a scholar working on Molecular Biology, Genetics, Plant Science, Ecology, Evolution, Behavior and Systematics and Cancer Research, having authored 120 papers that have together received 5.6k indexed citations. Recurring topics across this work include Genomics and Chromatin Dynamics (49 papers), RNA and protein synthesis mechanisms (22 papers), RNA Research and Splicing (20 papers), Genomics and Phylogenetic Studies (17 papers), Bioinformatics and Genomic Networks (12 papers), Plant Molecular Biology Research (10 papers), Gene expression and cancer classification (10 papers) and Insect and Arachnid Ecology and Behavior (9 papers). The work is most often cited by research in Biophysics (351 citations), Aging (93 citations), Molecular Biology (3.4k citations), Genetics (1.2k citations) and Developmental Biology (83 citations). Saurabh Sinha has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include Gene E. Robinson, Martin Tompa, Eric D. Siggia, Charles Blatti, ChengXiang Zhai, Faraz Faghri, Miles Efron, Roy H. Campbell, Ravishankar K. Iyer and Michael C. Schatz. Their work appears in journals such as Nucleic Acids Research, PLoS Computational Biology, Bioinformatics, Proceedings of the National Academy of Sciences and Genome biology.
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.