Primo Baybayan

10.1k total citations · 2 hit papers
27 papers, 1.6k citations indexed

About

Primo Baybayan is a scholar working on Molecular Biology, Genetics and Plant Science. According to data from OpenAlex, Primo Baybayan has authored 27 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 10 papers in Genetics and 5 papers in Plant Science. Recurrent topics in Primo Baybayan's work include Genomics and Phylogenetic Studies (9 papers), Genomics and Rare Diseases (5 papers) and Chromosomal and Genetic Variations (4 papers). Primo Baybayan is often cited by papers focused on Genomics and Phylogenetic Studies (9 papers), Genomics and Rare Diseases (5 papers) and Chromosomal and Genetic Variations (4 papers). Primo Baybayan collaborates with scholars based in United States, Australia and Malaysia. Primo Baybayan's co-authors include Ellson Y. Chen, David Schlessinger, Alex MacKenzie, Giuseppe Pilia, Giovanni Neri, Reid Huber, Antonino Forabosco, Antonio Cao, Ulpu Saarialho‐Kere and Anand Srivastava and has published in prestigious journals such as Nature Genetics, PLoS ONE and Cancer Research.

In The Last Decade

Primo Baybayan

25 papers receiving 1.6k citations

Hit Papers

Mutations in GPC3, a glypican gene, cause the Simpson-Gol... 1996 2026 2006 2016 1996 1996 100 200 300 400 500

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Primo Baybayan United States 15 1.2k 458 283 183 131 27 1.6k
Renée Laufer Amorim Brazil 26 545 0.5× 307 0.7× 60 0.2× 51 0.3× 135 1.0× 176 1.9k
E. van Garderen Netherlands 23 732 0.6× 330 0.7× 98 0.3× 41 0.2× 210 1.6× 46 2.3k
Miao Yu China 21 748 0.6× 157 0.3× 69 0.2× 196 1.1× 170 1.3× 69 1.2k
J. Merregaert Belgium 24 1.2k 1.0× 386 0.8× 126 0.4× 12 0.1× 194 1.5× 55 1.9k
Jérôme Abadie France 19 330 0.3× 294 0.6× 29 0.1× 70 0.4× 153 1.2× 65 1.4k
Clayton K. Collings United States 21 1.1k 0.9× 119 0.3× 26 0.1× 164 0.9× 95 0.7× 40 1.5k
Ke Zheng China 21 1.0k 0.9× 249 0.5× 37 0.1× 28 0.2× 229 1.7× 59 1.6k
Takao Kotani Japan 20 393 0.3× 135 0.3× 65 0.2× 39 0.2× 34 0.3× 106 1.4k
Leny Toma Brazil 22 698 0.6× 381 0.8× 419 1.5× 21 0.1× 163 1.2× 46 1.3k
Luca Cozzuto Spain 22 1.4k 1.2× 190 0.4× 82 0.3× 10 0.1× 211 1.6× 43 1.9k

Countries citing papers authored by Primo Baybayan

Since Specialization
Citations

This map shows the geographic impact of Primo Baybayan'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 Primo Baybayan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Primo Baybayan more than expected).

Fields of papers citing papers by Primo Baybayan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Primo Baybayan. 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 Primo Baybayan. The network helps show where Primo Baybayan may publish in the future.

Co-authorship network of co-authors of Primo Baybayan

This figure shows the co-authorship network connecting the top 25 collaborators of Primo Baybayan. A scholar is included among the top collaborators of Primo Baybayan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Primo Baybayan. Primo Baybayan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kwint, Michael, Ronny Derks, Aaron M. Wenger, et al.. (2023). Comprehensive de novo mutation discovery with HiFi long-read sequencing. Genome Medicine. 15(1). 34–34. 18 indexed citations
2.
Volden, Roger, Zev Kronenberg, Michael J. Monument, et al.. (2023). Abstract LB078: pbfusion: Detecting gene-fusion and other transcriptional abnormalities using PacBio HiFi data. Cancer Research. 83(8_Supplement). LB078–LB078. 4 indexed citations
3.
Kobayashi, Erica Sanford, Serge Batalov, Aaron M. Wenger, et al.. (2022). Approaches to long-read sequencing in a clinical setting to improve diagnostic rate. Scientific Reports. 12(1). 16945–16945. 28 indexed citations
4.
Benton, Michael G., Wallace Akerley, George F. Mayhew, et al.. (2020). Structural variation and its potential impact on genome instability: Novel discoveries in the EGFR landscape by long-read sequencing. PLoS ONE. 15(1). e0226340–e0226340. 23 indexed citations
5.
Wang, Bo, Elizabeth Tseng, Primo Baybayan, et al.. (2020). Variant phasing and haplotypic expression from long-read sequencing in maize. Communications Biology. 3(1). 78–78. 19 indexed citations
6.
Pauper, Marc, Aaron M. Wenger, Shreyasee Chakraborty, et al.. (2020). Long-read trio sequencing of individuals with unsolved intellectual disability. European Journal of Human Genetics. 29(4). 637–648. 24 indexed citations
7.
Kingan, Sarah B., Julie Urban, Christine Lambert, et al.. (2019). A high-quality genome assembly from a single, field-collected spotted lanternfly ( Lycorma delicatula ) using the PacBio Sequel II system. GigaScience. 8(10). 32 indexed citations
8.
Kingan, Sarah B., Haynes Heaton, Juliana Cudini, et al.. (2019). A High-Quality De novo Genome Assembly from a Single Mosquito Using PacBio Sequencing. Genes. 10(1). 62–62. 89 indexed citations
10.
Sutton, Jolene T., Martin Helmkampf, Cynthia Steiner, et al.. (2018). A High-Quality, Long-Read De Novo Genome Assembly to Aid Conservation of Hawaiiʻs Last Remaining Crow Species. Genes. 9(8). 393–393. 18 indexed citations
12.
Baybayan, Primo & Laura K. Nolden. (2017). Abstract 5366: Detection of low-frequency somatic variants using single-molecule, real-time sequencing. Cancer Research. 77(13_Supplement). 5366–5366.
13.
Vembar, Shruthi Sridhar, Matthew G. Seetin, Christine Lambert, et al.. (2016). Complete telomere-to-telomerede novoassembly of thePlasmodium falciparumgenome through long-read (>11 kb), single molecule, real-time sequencing. DNA Research. 23(4). 339–351. 36 indexed citations
14.
Taylor, Todd D., Shinji Kondo, Nazalan Najimudin, et al.. (2015). Complete genome sequence of Streptomyces sp. strain CFMR 7, a natural rubber degrading actinomycete isolated from Penang, Malaysia. Journal of Biotechnology. 214. 47–48. 10 indexed citations
15.
Anton, Brian P., Susana Wang, Primo Baybayan, et al.. (2015). The complete methylome of Helicobacter pylori UM032. BMC Genomics. 16(1). 424–424. 39 indexed citations
16.
Peluso, Paul, David R. Rank, Kyung‐Tae Kim, et al.. (2014). SMRT® Sequencing Solutions for Large Genomes and Transcriptomes. Journal of Biomolecular Techniques JBT. 25. 2 indexed citations
17.
Khosravi, Yalda, Wei Yee Wee, Susana Wang, et al.. (2013). Comparing the genomes of Helicobacter pylori clinical strain UM032 and Mice-adapted derivatives. Gut Pathogens. 5(1). 25–25. 11 indexed citations
18.
Wang, Susana, et al.. (2012). Sample Quality - Effects of Contaminants on SMRTbell TM Library Preparation and Sequencing.
19.
Pilia, Giuseppe, Alex MacKenzie, Primo Baybayan, et al.. (1996). Mutations in GPC3, a glypican gene, cause the Simpson-Golabi-Behmel overgrowth syndrome. Nature Genetics. 12(3). 241–247. 593 indexed citations breakdown →
20.
Kere, Juha, Anand Srivastava, Jonathan Zonana, et al.. (1996). X–linked anhidrotic (hypohidrotic) ectodermal dysplasia is caused by mutation in a novel transmembrane protein. Nature Genetics. 13(4). 409–416. 557 indexed citations breakdown →

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.

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