Daniel E. Newburger

3.2k total citations · 1 hit paper
15 papers, 2.4k citations indexed

About

Daniel E. Newburger is a scholar working on Molecular Biology, Genetics and Cancer Research. According to data from OpenAlex, Daniel E. Newburger has authored 15 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 5 papers in Genetics and 3 papers in Cancer Research. Recurrent topics in Daniel E. Newburger's work include RNA and protein synthesis mechanisms (6 papers), Genomics and Phylogenetic Studies (6 papers) and Genomics and Chromatin Dynamics (4 papers). Daniel E. Newburger is often cited by papers focused on RNA and protein synthesis mechanisms (6 papers), Genomics and Phylogenetic Studies (6 papers) and Genomics and Chromatin Dynamics (4 papers). Daniel E. Newburger collaborates with scholars based in United States, Canada and Australia. Daniel E. Newburger's co-authors include Martha L. Bulyk, Anthony Philippakis, Gwenaël Badis, Andrew R. Gehrke, Esther T. Chan, Timothy R. Hughes, Quaid Morris, Shaheynoor Talukder, Savina Jaeger and Federico De Masi and has published in prestigious journals such as Science, Cell and Nucleic Acids Research.

In The Last Decade

Daniel E. Newburger

15 papers receiving 2.3k citations

Hit Papers

Diversity and Complexity in DNA Recognition by Transcript... 2009 2026 2014 2020 2009 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Diversity and Complexity in DNA Recognition by Transcription Factors
    2009 761 citations Gwenaël Badis, Michael F. Berger et al. Science profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel E. Newburger United States 12 2.1k 365 231 192 116 15 2.4k
Jeffrey A. Hussmann United States 17 2.5k 1.2× 467 1.3× 157 0.7× 222 1.2× 86 0.7× 18 2.7k
Michael C. Bassik United States 2 1.8k 0.9× 286 0.8× 105 0.5× 193 1.0× 128 1.1× 2 1.9k
Henriette O’Geen United States 28 2.4k 1.2× 527 1.4× 275 1.2× 202 1.1× 53 0.5× 43 2.7k
Sean Thomas United States 19 2.4k 1.2× 497 1.4× 391 1.7× 309 1.6× 30 0.3× 23 2.8k
Liana F. Lareau United States 12 3.0k 1.5× 198 0.5× 178 0.8× 422 2.2× 32 0.3× 15 3.2k
Aaron C. Goldstrohm United States 24 2.1k 1.0× 122 0.3× 105 0.5× 161 0.8× 179 1.5× 40 2.4k
Gwenaël Badis France 14 2.7k 1.3× 291 0.8× 247 1.1× 270 1.4× 33 0.3× 18 3.0k
Ivan V. Kulakovskiy Russia 25 2.1k 1.0× 369 1.0× 224 1.0× 330 1.7× 39 0.3× 76 2.6k
Shaheynoor Talukder Canada 10 2.7k 1.3× 330 0.9× 212 0.9× 276 1.4× 41 0.4× 11 3.0k
Søren Lykke‐Andersen Denmark 21 3.2k 1.5× 187 0.5× 183 0.8× 542 2.8× 32 0.3× 31 3.4k

Countries citing papers authored by Daniel E. Newburger

Since Specialization
Citations

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

Fields of papers citing papers by Daniel E. Newburger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel E. Newburger. 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 Daniel E. Newburger. The network helps show where Daniel E. Newburger may publish in the future.

Co-authorship network of co-authors of Daniel E. Newburger

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel E. Newburger. A scholar is included among the top collaborators of Daniel E. Newburger 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 Daniel E. Newburger. Daniel E. Newburger is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
1.
Ovadia, Yaniv, Yoni Halpern, Dilip Krishnan, et al.. (2017). Learning to Count Mosquitoes for the Sterile Insect Technique. 1943–1949. 3 indexed citations
2.
Bishara, Alex, Yuling Liu, Ziming Weng, et al.. (2015). Read clouds uncover variation in complex regions of the human genome. Genome Research. 25(10). 1570–1580. 36 indexed citations
3.
Weng, Ziming, Noah Spies, Shirley Zhu, et al.. (2015). Cell-lineage heterogeneity and driver mutation recurrence in pre-invasive breast neoplasia. Genome Medicine. 7(1). 28–28. 13 indexed citations
4.
Newburger, Daniel E., Dorna Kashef-Haghighi, Ziming Weng, et al.. (2013). Genome evolution during progression to breast cancer. Genome Research. 23(7). 1097–1108. 80 indexed citations
5.
Salari, Raheleh, Dorna Kashef-Haghighi, Daniel E. Newburger, et al.. (2013). Inference of Tumor Phylogenies with Improved Somatic Mutation Discovery. Journal of Computational Biology. 20(11). 933–944. 14 indexed citations
6.
Natsoulis, Georges, John Bell, Hua Xu, et al.. (2011). A Flexible Approach for Highly Multiplexed Candidate Gene Targeted Resequencing. PLoS ONE. 6(6). e21088–e21088. 14 indexed citations
7.
Newburger, Daniel E., Georges Natsoulis, Susan M. Grimes, et al.. (2011). The Human OligoGenome Resource: a database of oligonucleotide capture probes for resequencing target regions across the human genome. Nucleic Acids Research. 40(D1). D1137–D1143. 2 indexed citations
8.
Natsoulis, Georges, John Bell, Daniel E. Newburger, et al.. (2011). A cross-sample statistical model for SNP detection in short-read sequencing data. Nucleic Acids Research. 40(1). e5–e5. 11 indexed citations
9.
Badis, Gwenaël, Michael F. Berger, Anthony Philippakis, et al.. (2009). Diversity and Complexity in DNA Recognition by Transcription Factors. Science. 324(5935). 1720–1723. 761 indexed citations breakdown →
10.
Grove, Christian A, Federico De Masi, M. Inmaculada Barrasa, et al.. (2009). A Multiparameter Network Reveals Extensive Divergence between C. elegans bHLH Transcription Factors. Cell. 138(2). 314–327. 205 indexed citations
11.
Zhu, Cong, Kelsey J.R.P. Byers, Rachel Patton McCord, et al.. (2009). High-resolution DNA-binding specificity analysis of yeast transcription factors. Genome Research. 19(4). 556–566. 322 indexed citations
12.
Newburger, Daniel E. & Martha L. Bulyk. (2008). UniPROBE: an online database of protein binding microarray data on protein-DNA interactions. Nucleic Acids Research. 37(Database). D77–D82. 284 indexed citations
13.
Berger, Michael F., Gwenaël Badis, Andrew R. Gehrke, et al.. (2008). Variation in Homeodomain DNA Binding Revealed by High-Resolution Analysis of Sequence Preferences. Cell. 133(7). 1266–1276. 487 indexed citations
14.
Rozowsky, Joel, Daniel E. Newburger, Jiaqian Wu, et al.. (2007). The DART classification of unannotated transcription within the ENCODE regions: Associating transcription with known and novel loci. Genome Research. 17(6). 732–745. 20 indexed citations
15.
Agrawal, Pankaj B., Corinne D. Strickland, Charles R. Midgett, et al.. (2004). Heterogeneity of nemaline myopathy cases with skeletal muscle α‐actin gene mutations. Annals of Neurology. 56(1). 86–96. 102 indexed citations

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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