Michael Terribilini

798 total citations
8 papers, 614 citations indexed

About

Michael Terribilini is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine and Biotechnology. According to data from OpenAlex, Michael Terribilini has authored 8 papers receiving a total of 614 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 1 paper in Pulmonary and Respiratory Medicine and 1 paper in Biotechnology. Recurrent topics in Michael Terribilini's work include RNA and protein synthesis mechanisms (6 papers), RNA Research and Splicing (4 papers) and Machine Learning in Bioinformatics (3 papers). Michael Terribilini is often cited by papers focused on RNA and protein synthesis mechanisms (6 papers), RNA Research and Splicing (4 papers) and Machine Learning in Bioinformatics (3 papers). Michael Terribilini collaborates with scholars based in United States and Egypt. Michael Terribilini's co-authors include Vasant Honavar, Drena Dobbs, Robert L. Jernigan, Jae‐Hyung Lee, Changhui Yan, Rasna R. Walia, Jeffry D. Sander, Feihong Wu, Charles Zheng and Jane F. Ferguson and has published in prestigious journals such as Nucleic Acids Research, BMC Bioinformatics and RNA.

In The Last Decade

Michael Terribilini

8 papers receiving 611 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michael Terribilini United States	7	577	44	44	30	27	8	614
Taras Makhnevych Canada	14	819 1.4×	25 0.6×	22 0.5×	39 1.3×	22 0.8×	18	842
Roger Olivella Spain	4	277 0.5×	34 0.8×	12 0.3×	25 0.8×	22 0.8×	5	350
Stefanie Kaufmann Germany	5	331 0.6×	22 0.5×	28 0.6×	28 0.9×	29 1.1×	6	370
Ryan R. Murray United States	5	374 0.6×	50 1.1×	9 0.2×	7 0.2×	16 0.6×	5	433
Tomasz Puton Poland	5	417 0.7×	12 0.3×	23 0.5×	41 1.4×	9 0.3×	5	447
Jakub Paś Poland	7	201 0.3×	47 1.1×	11 0.3×	31 1.0×	26 1.0×	11	293
Priyanka Dhingra India	8	158 0.3×	21 0.5×	41 0.9×	17 0.6×	11 0.4×	17	215
Manato Akiyama Japan	6	298 0.5×	34 0.8×	33 0.8×	37 1.2×	10 0.4×	11	362
Stephan Lagleder Germany	5	458 0.8×	69 1.6×	12 0.3×	94 3.1×	12 0.4×	5	495
Huaqun Zhang China	8	208 0.4×	16 0.4×	22 0.5×	19 0.6×	9 0.3×	17	256

Countries citing papers authored by Michael Terribilini

Since Specialization

Citations

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

Fields of papers citing papers by Michael Terribilini

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Terribilini

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

All Works

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8 of 8 papers shown

1.

Walia, Rasna R., Cornelia Caragea, Fadi Towfic, et al.. (2012). Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art. BMC Bioinformatics. 13(1). 89–89. 74 indexed citations

2.

Lewis, Barbara, Rasna R. Walia, Michael Terribilini, et al.. (2010). PRIDB: a protein-RNA interface database. Nucleic Acids Research. 39(Database). D277–D282. 107 indexed citations

3.

Langston, James, et al.. (2007). Substrate specificity of Streptomyces transglutaminases. Applied Biochemistry and Biotechnology. 136(3). 291–308. 12 indexed citations

4.

Terribilini, Michael, Jeffry D. Sander, Jae‐Hyung Lee, et al.. (2007). RNABindR: a server for analyzing and predicting RNA-binding sites in proteins. Nucleic Acids Research. 35(Web Server). W578–W584. 153 indexed citations

5.

Lee, Jae‐Hyung, Michael Hamilton, Colin Gleeson, et al.. (2007). STRIKING SIMILARITIES IN DIVERSE TELOMERASE PROTEINS REVEALED BY COMBINING STRUCTURE PREDICTION AND MACHINE LEARNING APPROACHES. PubMed. 501–12. 5 indexed citations

6.

Terribilini, Michael, Jae‐Hyung Lee, Changhui Yan, et al.. (2006). Prediction of RNA binding sites in proteins from amino acid sequence. RNA. 12(8). 1450–1462. 134 indexed citations

7.

Yan, Changhui, Michael Terribilini, Feihong Wu, et al.. (2006). Predicting DNA-binding sites of proteins from amino acid sequence. BMC Bioinformatics. 7(1). 262–262. 121 indexed citations

8.

Terribilini, Michael, Jae‐Hyung Lee, Changhui Yan, et al.. (2005). IDENTIFYING INTERACTION SITES IN “RECALCITRANT” PROTEINS: PREDICTED PROTEIN AND RNA BINDING SITES IN REV PROTEINS OF HIV-1 AND EIAV AGREE WITH EXPERIMENTAL DATA. PubMed. 415–426. 8 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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