Robert H. Newman

1.9k total citations
57 papers, 1.2k citations indexed

About

Robert H. Newman is a scholar working on Molecular Biology, Spectroscopy and Oncology. According to data from OpenAlex, Robert H. Newman has authored 57 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Molecular Biology, 9 papers in Spectroscopy and 6 papers in Oncology. Recurrent topics in Robert H. Newman's work include Machine Learning in Bioinformatics (12 papers), Advanced Proteomics Techniques and Applications (9 papers) and Advanced Biosensing Techniques and Applications (8 papers). Robert H. Newman is often cited by papers focused on Machine Learning in Bioinformatics (12 papers), Advanced Proteomics Techniques and Applications (9 papers) and Advanced Biosensing Techniques and Applications (8 papers). Robert H. Newman collaborates with scholars based in United States, United Kingdom and Japan. Robert H. Newman's co-authors include Jin Zhang, Matthew Fosbrink, Dukka B. KC, Vedangi Sample, Heng Zhu, Hiroto Saigo, Hamid D. Ismail, Jiang Qian, Jianfei Hu and Niraj Thapa and has published in prestigious journals such as Chemical Reviews, Chemical Society Reviews and Journal of Clinical Oncology.

In The Last Decade

Robert H. Newman

54 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Robert H. Newman United States	18	839	194	135	126	108	57	1.2k
Ryan Barnes United States	18	827 1.0×	107 0.6×	128 0.9×	85 0.7×	56 0.5×	31	1.5k
Mioara Larion United States	22	980 1.2×	100 0.5×	79 0.6×	171 1.4×	41 0.4×	47	1.7k
Arjen N. Bader Netherlands	20	687 0.8×	460 2.4×	72 0.5×	110 0.9×	95 0.9×	41	1.4k
Uthpala Seneviratne United States	13	482 0.6×	263 1.4×	50 0.4×	67 0.5×	69 0.6×	22	965
Stanley R. Krystek United States	25	1.2k 1.4×	63 0.3×	182 1.3×	79 0.6×	145 1.3×	51	1.8k
Sina Reckel Germany	20	1.1k 1.3×	80 0.4×	296 2.2×	54 0.4×	169 1.6×	26	1.5k
Deborah A. Roess United States	22	662 0.8×	92 0.5×	39 0.3×	85 0.7×	141 1.3×	74	1.4k
Laurie L. Parker United States	20	731 0.9×	38 0.2×	95 0.7×	198 1.6×	77 0.7×	56	1.3k
Jianfang Chen China	19	627 0.7×	51 0.3×	50 0.4×	125 1.0×	48 0.4×	58	1.1k
Natalie de Souza Switzerland	20	784 0.9×	80 0.4×	208 1.5×	371 2.9×	46 0.4×	63	1.5k

Countries citing papers authored by Robert H. Newman

Since Specialization

Citations

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

Fields of papers citing papers by Robert H. Newman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert H. Newman. 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 Robert H. Newman. The network helps show where Robert H. Newman may publish in the future.

Co-authorship network of co-authors of Robert H. Newman

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

All Works

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20 of 20 papers shown

Hudson, M’Liss A., Robert H. Newman, Checo J. Rorie, et al.. (2025). Promoting the therapeutic potential of interleukin-7 (IL-7) by expression in viral vectors. Cancer Gene Therapy. 32(11). 1166–1176. 2 indexed citations

Dong, Ming, et al.. (2023). Redox Modification of PKA-Cα Differentially Affects Its Substrate Selection. Life. 13(9). 1811–1811. 1 indexed citations

Newman, Robert H., et al.. (2023). Development of a Genetically Encodable Fluorescent Biosensor to Examine the Spatiotemporal Regulation of NEK Kinases in Living Cells. Journal of Pharmacology and Experimental Therapeutics. 385. 548–548.

Ismail, Hamid D., et al.. (2022). FEPS: A Tool for Feature Extraction from Protein Sequence. Methods in molecular biology. 2499. 65–104. 6 indexed citations

Heinzinger, Michael, et al.. (2022). Improving protein succinylation sites prediction using embeddings from protein language model. Scientific Reports. 12(1). 16933–16933. 43 indexed citations

Thapa, Niraj, Hamid D. Ismail, Doina Caragea, et al.. (2021). DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites. Frontiers in Cell and Developmental Biology. 9. 662983–662983. 14 indexed citations

Ahmed, Maryam, et al.. (2021). SARS-COV-2, infection, transmission, transcription, translation, proteins, and treatment: A review. International Journal of Biological Macromolecules. 193(Pt B). 1249–1273. 31 indexed citations

Thapa, Niraj, et al.. (2020). DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins. Molecular Omics. 16(5). 448–454. 23 indexed citations

Thapa, Niraj, et al.. (2020). DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction. BMC Bioinformatics. 21(S3). 63–63. 55 indexed citations

10.

Ahmed, Maryam, et al.. (2020). Introducing Chemistry Students to Emerging Technologies in Gene Editing, Their Applications, and Ethical Considerations. Journal of Chemical Education. 97(7). 1931–1943. 5 indexed citations

11.

Saigo, Hiroto, et al.. (2019). RF-GlutarySite: a random forest based predictor for glutarylation sites. Molecular Omics. 15(3). 189–204. 34 indexed citations

12.

McConnell, Evan W., et al.. (2018). SVM-SulfoSite: A support vector machine based predictor for sulfenylation sites. Scientific Reports. 8(1). 11288–11288. 15 indexed citations

13.

Newman, Robert H. & Jin Zhang. (2017). Integrated Strategies to Gain a Systems-Level View of Dynamic Signaling Networks. Methods in enzymology on CD-ROM/Methods in enzymology. 589. 133–170. 3 indexed citations

14.

Ismail, Hamid D., Robert H. Newman, & Dukka B. KC. (2016). RF-Hydroxysite: a random forest based predictor for hydroxylation sites. Molecular BioSystems. 12(8). 2427–2435. 24 indexed citations

15.

Woodard, Crystal, C. Rory Goodwin, Jun Wan, et al.. (2013). Profiling the Dynamics of a Human Phosphorylome Reveals New Components in HGF/c-Met Signaling. PLoS ONE. 8(9). e72671–e72671. 18 indexed citations

16.

Newman, Robert H. & Jin Zhang. (2013). The Design and Application of Genetically Encodable Biosensors Based on Fluorescent Proteins. Methods in molecular biology. 1071. 1–16. 14 indexed citations

17.

Woodard, Crystal, Meir Shamay, Gangling Liao, et al.. (2012). Phosphorylation of the Chromatin Binding Domain of KSHV LANA. PLoS Pathogens. 8(10). e1002972–e1002972. 27 indexed citations

18.

Newman, Robert H. & Jin Zhang. (2008). Visualization of phosphatase activity in living cells with a FRET-based calcineurin activity sensor. Molecular BioSystems. 4(6). 496–501. 55 indexed citations

19.

Newman, Robert H. & Jin Zhang. (2008). Small molecules and chemical tools at the interface. Nature Chemical Biology. 4(7). 382–386. 5 indexed citations

20.

Newman, Robert H. & Jin Zhang. (2008). Fucci: Street Lights on the Road to Mitosis. Chemistry & Biology. 15(2). 97–98. 12 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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