James R. Apgar

819 total citations
11 papers, 426 citations indexed

About

James R. Apgar is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering. According to data from OpenAlex, James R. Apgar has authored 11 papers receiving a total of 426 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 5 papers in Radiology, Nuclear Medicine and Imaging and 3 papers in Biomedical Engineering. Recurrent topics in James R. Apgar's work include Monoclonal and Polyclonal Antibodies Research (5 papers), Protein Structure and Dynamics (4 papers) and Glycosylation and Glycoproteins Research (3 papers). James R. Apgar is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (5 papers), Protein Structure and Dynamics (4 papers) and Glycosylation and Glycoproteins Research (3 papers). James R. Apgar collaborates with scholars based in United States. James R. Apgar's co-authors include Amy E. Keating, Eric M. Bennett, K. Brajesh, R. Michael Raab, G. Lázár, Binzhang Shen, Benjamin N. Gray, Jeremy C. S. Johnson, Dongcheng Zhang and Nathan A. Ekborg and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Biotechnology and PLoS ONE.

In The Last Decade

James R. Apgar

11 papers receiving 409 citations

Peers

Countries citing papers authored by James R. Apgar

Since Specialization

Citations

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

Fields of papers citing papers by James R. Apgar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James R. Apgar. 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 James R. Apgar. The network helps show where James R. Apgar may publish in the future.

Co-authorship network of co-authors of James R. Apgar

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

All Works

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11 of 11 papers shown

#	Work	Indexed citations
1	Low-data interpretable deep learning prediction of antibody viscosity using a biophysically meaningful representation 2023 ·Scientific Reports ·K. Brajesh, James R. Apgar, Eric M. Bennett	17
2	Combining random mutagenesis, structure-guided design and next-generation sequencing to mitigate polyreactivity of an anti-IL-21R antibody 2021 ·mAbs ·(unknown), (unknown), James R. Apgar, (unknown), (unknown), (unknown), Eric M. Bennett, Matthew Lambert, Orla Cunningham	4
3	Modeling and mitigation of high-concentration antibody viscosity through structure-based computer-aided protein design 2020 ·PLoS ONE ·James R. Apgar, Amy Tam, (unknown), (unknown), Amy King, Han Kwang Yang, Kerry Kelleher, Denise Murphy, Aaron M. D’Antona, (unknown), Xiaotian Zhong, (unknown), Weijun Ma, (unknown), Gregory J. Carven, Eric M. Bennett, Laura Lin	37
4	Beyond CDR-grafting: Structure-guided humanization of framework and CDR regions of an anti-myostatin antibody 2016 ·mAbs ·James R. Apgar, (unknown), (unknown), Susan Benard, Peter Bialek, Mark Johnson, Yijie Gao, Mark R.H. Krebs, Jane Owens, Kevin Parris, (unknown), (unknown), Carl Morris, Lioudmila Tchistiakova	34
5	AB ‐Bind: Antibody binding mutational database for computational affinity predictions 2015 ·Protein Science ·(unknown), James R. Apgar, Eric M. Bennett, Amy E. Keating	123
6	Augmented Binary Substitution: Single-pass CDR germ-lining and stabilization of therapeutic antibodies 2015 ·Proceedings of the National Academy of Sciences ·(unknown), Brian J. Fennell, James R. Apgar, Matthew Lambert, Barry J. McDonnell, (unknown), (unknown), Edward Franklin, (unknown), (unknown), (unknown), (unknown), Amy King, Janet Paulsen, Timothy P. Hickling, Lioudmila Tchistiakova, Orla Cunningham, William J.J. Finlay	19
7	Engineering a thermoregulated intein-modified xylanase into maize for consolidated lignocellulosic biomass processing 2012 ·Nature Biotechnology ·Binzhang Shen, (unknown), (unknown), (unknown), James R. Apgar, (unknown), (unknown), (unknown), Matthew H. Parker, (unknown), (unknown), Benjamin N. Gray, Nathan A. Ekborg, Dongcheng Zhang, Jeremy C. S. Johnson, G. Lázár, R. Michael Raab	65
8	A Predictive Model of Intein Insertion Site for Use in the Engineering of Molecular Switches 2012 ·PLoS ONE ·James R. Apgar, (unknown), (unknown), (unknown), (unknown), Binzhang Shen, Humberto de la Vega, Philip A. Lessard, G. Lázár, R. Michael Raab	15
9	Cluster expansion models for flexible‐backbone protein energetics 2009 ·Journal of Computational Chemistry ·James R. Apgar, Seungsoo Hahn, Gevorg Grigoryan, Amy E. Keating	16
10	Predicting helix orientation for coiled‐coil dimers 2008 ·Proteins Structure Function and Bioinformatics ·James R. Apgar, (unknown), Amy E. Keating	32
11	Modeling Backbone Flexibility to Achieve Sequence Diversity: The Design of Novel α-Helical Ligands for Bcl-xL 2007 ·Journal of Molecular Biology ·(unknown), James R. Apgar, Amy E. Keating	64

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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