Alpan Raval

2.9k total citations
39 papers, 1.6k citations indexed

About

Alpan Raval is a scholar working on Molecular Biology, Astronomy and Astrophysics and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Alpan Raval has authored 39 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 9 papers in Astronomy and Astrophysics and 5 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Alpan Raval's work include Cosmology and Gravitation Theories (9 papers), Bioinformatics and Genomic Networks (8 papers) and Protein Structure and Dynamics (6 papers). Alpan Raval is often cited by papers focused on Cosmology and Gravitation Theories (9 papers), Bioinformatics and Genomic Networks (8 papers) and Protein Structure and Dynamics (6 papers). Alpan Raval collaborates with scholars based in United States, Australia and India. Alpan Raval's co-authors include Claus O. Wilke, Leonard Parker, D. Allan Drummond, Jesse D. Bloom, Animesh Ray, Stefano Piana, David E. Shaw, Michael P. Eastwood, B. L. Hu and Ron O. Dror and has published in prestigious journals such as Physical Review Letters, Nucleic Acids Research and Bioinformatics.

In The Last Decade

Alpan Raval

39 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alpan Raval United States 20 1.0k 327 307 205 155 39 1.6k
Yoav Peleg Israel 32 2.2k 2.1× 254 0.8× 378 1.2× 194 0.9× 161 1.0× 105 3.5k
Takashi Hara Japan 21 585 0.6× 203 0.6× 274 0.9× 185 0.9× 119 0.8× 47 2.0k
Sanjay Jain India 21 401 0.4× 355 1.1× 155 0.5× 362 1.8× 80 0.5× 65 1.6k
Michele Caselle Italy 29 1.0k 1.0× 130 0.4× 156 0.5× 730 3.6× 344 2.2× 160 2.5k
Andrew Ilin United States 12 600 0.6× 129 0.4× 44 0.1× 167 0.8× 244 1.6× 52 1.3k
Yaroslav Ispolatov United States 19 482 0.5× 73 0.2× 257 0.8× 29 0.1× 108 0.7× 63 1.4k
Mark R. Dowling Australia 29 795 0.8× 160 0.5× 84 0.3× 202 1.0× 527 3.4× 72 2.7k
Conrad J. Burden Australia 20 488 0.5× 167 0.5× 90 0.3× 794 3.9× 174 1.1× 75 1.6k
Saumen Datta India 23 639 0.6× 186 0.6× 234 0.8× 1.8k 9.0× 95 0.6× 103 2.7k
Michael Pierce United States 39 2.9k 2.9× 1.3k 4.0× 131 0.4× 164 0.8× 43 0.3× 103 4.7k

Countries citing papers authored by Alpan Raval

Since Specialization
Citations

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

Fields of papers citing papers by Alpan Raval

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alpan Raval

This figure shows the co-authorship network connecting the top 25 collaborators of Alpan Raval. A scholar is included among the top collaborators of Alpan Raval 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 Alpan Raval. Alpan Raval 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.
Sinha, Arunesh, et al.. (2020). Who and When to Screen: Multi-Round Active Screening for Network Recurrent Infectious Diseases Under Uncertainty. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 992–1000. 1 indexed citations
2.
Bhardwaj, Anvita, et al.. (2020). Robust lock-down optimization for COVID-19 policy guidance. National Conference on Artificial Intelligence. 2884. 2 indexed citations
3.
Raval, Alpan, Stefano Piana, Michael P. Eastwood, Ron O. Dror, & David E. Shaw. (2012). Refinement of protein structure homology models via long, all‐atom molecular dynamics simulations. Proteins Structure Function and Bioinformatics. 80(8). 2071–2079. 194 indexed citations
4.
Marshall, Adrienne, et al.. (2012). Differential hepatic protein tyrosine nitration of mouse due to aging – Effect on mitochondrial energy metabolism, quality control machinery of the endoplasmic reticulum and metabolism of drugs. Biochemical and Biophysical Research Communications. 430(1). 231–235. 8 indexed citations
5.
Vallabhajosyula, Ravishankar R. & Alpan Raval. (2010). Computational Modeling in Systems Biology. Methods in molecular biology. 662. 97–120. 6 indexed citations
6.
Arvey, Aaron, Rajeev K. Azad, Alpan Raval, & Jeffrey G. Lawrence. (2009). Detection of genomic islands via segmental genome heterogeneity. Nucleic Acids Research. 37(16). 5255–5266. 33 indexed citations
7.
8.
Vallabhajosyula, Ravishankar R., et al.. (2009). Identifying Hubs in Protein Interaction Networks. PLoS ONE. 4(4). e5344–e5344. 140 indexed citations
9.
Chakravarti, Bulbul, et al.. (2008). Proteomic profiling of aging in the mouse heart: Altered expression of mitochondrial proteins. Archives of Biochemistry and Biophysics. 474(1). 22–31. 30 indexed citations
10.
Chakravarti, Bulbul, et al.. (2008). A highly uniform UV transillumination imaging system for quantitative analysis of nucleic acids and proteins. PROTEOMICS. 8(9). 1789–1797. 9 indexed citations
11.
Zhao, Shan, et al.. (2008). Mining protein networks for synthetic genetic interactions. BMC Bioinformatics. 9(1). 426–426. 46 indexed citations
12.
Lewis, Steven, Alpan Raval, & John E. Angus. (2007). Bayesian Monte Carlo estimation for profile hidden Markov models. Mathematical and Computer Modelling. 47(11-12). 1198–1216. 4 indexed citations
13.
Bloom, Jesse D., Zhongyi Lu, David Chen, et al.. (2007). Evolution favors protein mutational robustness in sufficiently large populations. BMC Biology. 5(1). 29–29. 80 indexed citations
14.
Baitaluk, Michael, et al.. (2006). PathSys: integrating molecular interaction graphs for systems biology. BMC Bioinformatics. 7(1). 55–55. 38 indexed citations
15.
Drummond, D. Allan, Alpan Raval, & Claus O. Wilke. (2005). A Single Determinant Dominates the Rate of Yeast Protein Evolution. Molecular Biology and Evolution. 23(2). 327–337. 323 indexed citations
16.
Wilke, Claus O., Jesse D. Bloom, D. Allan Drummond, & Alpan Raval. (2005). Predicting the Tolerance of Proteins to Random Amino Acid Substitution. Biophysical Journal. 89(6). 3714–3720. 31 indexed citations
17.
Parker, Leonard & Alpan Raval. (2001). A New Look at the Accelerating Universe. Physical Review Letters. 86(5). 749–752. 36 indexed citations
18.
Hu, B. L. & Alpan Raval. (2000). Is there emitted radiation in Unruh effect?. ArXiv.org. 414–423. 2 indexed citations
19.
Parker, Leonard & Alpan Raval. (1999). Vacuum effects of an ultralow mass particle account for the recent acceleration of the universe. Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields. 60(12). 33 indexed citations
20.
Koks, Don, B. L. Hu, Andrew Matacz, & Alpan Raval. (1997). Thermal particle creation in cosmological spacetimes: A stochastic approach. Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields. 56(8). 4905–4915. 11 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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