Michael C. O’Neill

1.4k total citations
31 papers, 1.2k citations indexed

About

Michael C. O’Neill is a scholar working on Molecular Biology, Genetics and Ecology. According to data from OpenAlex, Michael C. O’Neill has authored 31 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 11 papers in Genetics and 4 papers in Ecology. Recurrent topics in Michael C. O’Neill's work include Bacterial Genetics and Biotechnology (8 papers), RNA and protein synthesis mechanisms (8 papers) and Muscle Physiology and Disorders (5 papers). Michael C. O’Neill is often cited by papers focused on Bacterial Genetics and Biotechnology (8 papers), RNA and protein synthesis mechanisms (8 papers) and Muscle Physiology and Disorders (5 papers). Michael C. O’Neill collaborates with scholars based in United States, Finland and Japan. Michael C. O’Neill's co-authors include Frank E. Stockdale, Song Li, Ivan Erill, Benoît De Crombrugghe, Richard C. Strohman, Kevin L. Griffith, Richard E. Wolf, Ishita M. Shah, Todd Myers and Alan P. Kendal and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Michael C. O’Neill

31 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael C. O’Neill United States 19 945 320 112 104 68 31 1.2k
G. Thomas Hayman United States 23 984 1.0× 293 0.9× 65 0.6× 74 0.7× 32 0.5× 53 1.4k
Suresh Subramani United States 12 1.2k 1.3× 473 1.5× 37 0.3× 71 0.7× 59 0.9× 18 1.7k
Brad Marshall United States 4 954 1.0× 209 0.7× 64 0.6× 73 0.7× 23 0.3× 4 1.4k
Robert Ivarie United States 27 1.7k 1.8× 874 2.7× 123 1.1× 81 0.8× 32 0.5× 55 2.1k
Jorge Amigo Spain 21 797 0.8× 662 2.1× 108 1.0× 45 0.4× 95 1.4× 51 1.4k
Eugene Kulesha United Kingdom 13 1.4k 1.4× 432 1.4× 68 0.6× 54 0.5× 27 0.4× 13 1.8k
Keisuke Yamada Japan 17 873 0.9× 193 0.6× 152 1.4× 102 1.0× 73 1.1× 32 1.4k
Pär G. Engström Sweden 18 1.9k 2.0× 412 1.3× 53 0.5× 100 1.0× 42 0.6× 25 2.4k
Tatyana Goldberg Germany 15 1.1k 1.2× 186 0.6× 93 0.8× 84 0.8× 37 0.5× 22 1.7k
Alexander Sturn Austria 7 1.1k 1.2× 154 0.5× 70 0.6× 82 0.8× 27 0.4× 8 1.8k

Countries citing papers authored by Michael C. O’Neill

Since Specialization
Citations

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

Fields of papers citing papers by Michael C. O’Neill

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael C. O’Neill. 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 Michael C. O’Neill. The network helps show where Michael C. O’Neill may publish in the future.

Co-authorship network of co-authors of Michael C. O’Neill

This figure shows the co-authorship network connecting the top 25 collaborators of Michael C. O’Neill. A scholar is included among the top collaborators of Michael C. O’Neill 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 Michael C. O’Neill. Michael C. O’Neill 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.
Chornoguz, Olesya, Alexei Gapeev, Michael C. O’Neill, & Suzanne Ostrand‐Rosenberg. (2012). Major Histocompatibility Complex Class II+ Invariant Chain Negative Breast Cancer Cells Present Unique Peptides that Activate Tumor-specific T Cells from Breast Cancer Patients. Molecular & Cellular Proteomics. 11(11). 1457–1467. 16 indexed citations
2.
Erill, Ivan & Michael C. O’Neill. (2009). A reexamination of information theory-based methods for DNA-binding site identification. BMC Bioinformatics. 10(1). 57–57. 31 indexed citations
3.
Bradley, Brian P., et al.. (2009). Protein Expression Profiling. Methods in molecular biology. 519. 455–468. 6 indexed citations
4.
Choi, Young-Lim, Michael C. O’Neill, Yohei Yamada, et al.. (2006). A genomic analysis of adult T-cell leukemia. Oncogene. 26(8). 1245–1255. 65 indexed citations
5.
O’Neill, Michael C. & Song Li. (2003). Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect. BMC Bioinformatics. 4(1). 13–13. 75 indexed citations
6.
Griffith, Kevin L., Ishita M. Shah, Todd Myers, Michael C. O’Neill, & Richard E. Wolf. (2002). Evidence for “Pre-recruitment” as a New Mechanism of Transcription Activation in Escherichia coli: The Large Excess of SoxS Binding Sites per Cell Relative to the Number of SoxS Molecules per Cell. Biochemical and Biophysical Research Communications. 291(4). 979–986. 56 indexed citations
7.
Robinson, Phyllis R., Kevin L. Griffith, Jeffrey M. Gross, & Michael C. O’Neill. (1999). A back-propagation neural network predicts absorption maxima of chimeric human red/green visual pigments. Vision Research. 39(9). 1707–1712. 2 indexed citations
8.
Grahn, Ammi, Jaana K. H. Bamford, Michael C. O’Neill, & Dennis H. Bamford. (1994). Functional organization of the bacteriophage PRD1 genome. Journal of Bacteriology. 176(10). 3062–3068. 23 indexed citations
9.
O’Neill, Michael C.. (1992). Escherichia colipromoters: neural networks develop distinct descriptions in learning to search for promoters of different spacing classes. Nucleic Acids Research. 20(13). 3471–3477. 38 indexed citations
10.
O’Neill, Michael C.. (1991). A general method for modeling cell populations undergoing G1 → G0 transitions during development. Journal of Theoretical Biology. 153(4). 499–518. 2 indexed citations
11.
O’Neill, Michael C.. (1991). Training back-propagation neural networks to define and detect DNA-binding sites. Nucleic Acids Research. 19(2). 313–318. 69 indexed citations
12.
O’Neill, Michael C.. (1989). Consensus methods for finding and ranking DNA binding sites. Journal of Molecular Biology. 207(2). 301–310. 46 indexed citations
13.
O’Neill, Michael C., et al.. (1989). Escherichia coli Promoters. Journal of Biological Chemistry. 264(10). 5531–5534. 43 indexed citations
14.
O’Neill, Michael C.. (1989). Escherichia coli Promoters. Journal of Biological Chemistry. 264(10). 5522–5530. 103 indexed citations
15.
O’Neill, Michael C.. (1987). Growth and differentiation during myogenesis in the chick embryo. Developmental Biology. 120(2). 465–480. 20 indexed citations
16.
O’Neill, Michael C.. (1976). Similarities in the helical sequences of the represser-binding sites in the lac and λ operators. Nature. 260(5551). 550–554. 3 indexed citations
17.
O’Neill, Michael C. & Alan P. Kendal. (1975). Infection of differentiating muscle cells with influenza and Newcastle disease viruses. Nature. 253(5488). 195–198. 14 indexed citations
18.
Stockdale, Frank E. & Michael C. O’Neill. (1972). REPAIR DNA SYNTHESIS IN DIFFERENTIATED EMBRYONIC MUSCLE CELLS. The Journal of Cell Biology. 52(3). 589–597. 32 indexed citations
19.
O’Neill, Michael C. & Frank E. Stockdale. (1972). A KINETIC ANALYSIS OF MYOGENESIS IN VITRO. The Journal of Cell Biology. 52(1). 52–65. 230 indexed citations
20.
O’Neill, Michael C. & Richard C. Strohman. (1970). Studies of the decline of deoxyribonucleic acid polymerase activity during embryonic muscle cell fusion in vitro. Biochemistry. 9(14). 2832–2839. 28 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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