Robert Langlois

1.4k total citations
24 papers, 938 citations indexed

About

Robert Langlois is a scholar working on Molecular Biology, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics. According to data from OpenAlex, Robert Langlois has authored 24 papers receiving a total of 938 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 3 papers in Atomic and Molecular Physics, and Optics and 3 papers in Computational Theory and Mathematics. Recurrent topics in Robert Langlois's work include RNA and protein synthesis mechanisms (14 papers), Machine Learning in Bioinformatics (12 papers) and Protein Structure and Dynamics (9 papers). Robert Langlois is often cited by papers focused on RNA and protein synthesis mechanisms (14 papers), Machine Learning in Bioinformatics (12 papers) and Protein Structure and Dynamics (9 papers). Robert Langlois collaborates with scholars based in United States, China and Russia. Robert Langlois's co-authors include Hui Lü, Joachim Frank, Hstau Y. Liao, Amédée des Georges, Robert A. Grassucci, Vidya Dhote, Yaser Hashem, Christopher U.T. Hellen, Tatyana V. Pestova and Jesper Pallesen and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Robert Langlois

24 papers receiving 921 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Robert Langlois United States 15 725 197 107 99 74 24 938
Hstau Y. Liao United States 13 663 0.9× 261 1.3× 136 1.3× 108 1.1× 89 1.2× 25 948
Rishi Matadeen United Kingdom 10 587 0.8× 312 1.6× 131 1.2× 63 0.6× 133 1.8× 11 957
Vahid Abrishami Spain 12 398 0.5× 321 1.6× 203 1.9× 32 0.3× 116 1.6× 20 778
Markus Stabrin Germany 8 764 1.1× 320 1.6× 146 1.4× 102 1.0× 105 1.4× 10 1.2k
Benjamin A. Barad United States 7 720 1.0× 176 0.9× 66 0.6× 44 0.4× 169 2.3× 12 1.0k
Rubén Sánchez-García Spain 11 760 1.0× 163 0.8× 88 0.8× 34 0.3× 133 1.8× 28 1.2k
Amir Apelbaum Germany 4 595 0.8× 190 1.0× 88 0.8× 34 0.3× 77 1.0× 5 948
Lars V. Bock Germany 16 943 1.3× 173 0.9× 59 0.6× 51 0.5× 121 1.6× 26 1.1k
Irene S. Gabashvili United States 14 908 1.3× 146 0.7× 56 0.5× 53 0.5× 114 1.5× 24 1.1k
Grigory Sharov United Kingdom 8 722 1.0× 261 1.3× 142 1.3× 58 0.6× 106 1.4× 11 1.1k

Countries citing papers authored by Robert Langlois

Since Specialization
Citations

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

Fields of papers citing papers by Robert Langlois

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Robert Langlois

This figure shows the co-authorship network connecting the top 25 collaborators of Robert Langlois. A scholar is included among the top collaborators of Robert Langlois 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 Langlois. Robert Langlois 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.
Wang, Wenchuan, et al.. (2019). Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid–Binding Proteins. Frontiers in Genetics. 10. 729–729. 1 indexed citations
2.
Langlois, Robert, et al.. (2014). Automated particle picking for low-contrast macromolecules in cryo-electron microscopy. Journal of Structural Biology. 186(1). 1–7. 42 indexed citations
3.
Hashem, Yaser, Amédée des Georges, Vidya Dhote, et al.. (2014). Structure of the Mammalian Ribosomal 43S Preinitiation Complex Bound to the Scanning Factor DHX29. Biophysical Journal. 106(2). 492a–492a. 2 indexed citations
4.
Hashem, Yaser, Amédée des Georges, Vidya Dhote, et al.. (2013). Structure of the Mammalian Ribosomal 43S Preinitiation Complex Bound to the Scanning Factor DHX29. Cell. 153(5). 1108–1119. 162 indexed citations
5.
Hashem, Yaser, Amédée des Georges, Vidya Dhote, et al.. (2013). Hepatitis-C-virus-like internal ribosome entry sites displace eIF3 to gain access to the 40S subunit. Nature. 503(7477). 539–543. 149 indexed citations
6.
Sharma, Gyanesh, Jesper Pallesen, Robert A. Grassucci, et al.. (2012). Affinity grid-based cryo-EM of PKC binding to RACK1 on the ribosome. Journal of Structural Biology. 181(2). 190–194. 29 indexed citations
7.
Langlois, Robert & Joachim Frank. (2011). A clarification of the terms used in comparing semi-automated particle selection algorithms in Cryo-EM. Journal of Structural Biology. 175(3). 348–352. 22 indexed citations
8.
Langlois, Robert, Jesper Pallesen, & Joachim Frank. (2011). Reference-free particle selection enhanced with semi-supervised machine learning for cryo-electron microscopy. Journal of Structural Biology. 175(3). 353–361. 17 indexed citations
9.
Langlois, Robert & Hui Lü. (2010). Boosting the prediction and understanding of DNA-binding domains from sequence. Nucleic Acids Research. 38(10). 3149–3158. 43 indexed citations
10.
Carson, Matthew B., Robert Langlois, & Hui Lü. (2010). NAPS: a residue-level nucleic acid-binding prediction server. Nucleic Acids Research. 38(suppl_2). W431–W435. 63 indexed citations
11.
Genchev, Georgi Z., Morten Källberg, Gamze Gürsoy, et al.. (2009). Mechanical Signaling on the Single Protein Level Studied Using Steered Molecular Dynamics. Cell Biochemistry and Biophysics. 55(3). 141–152. 29 indexed citations
12.
Langlois, Robert. (2008). Machine learning in bioinformatics: Algorithms, implementations and applications.. Figshare. 3 indexed citations
13.
Langlois, Robert & Hui Lü. (2008). Intelligible machine learning with malibu. PubMed. 4. 3795–3798. 8 indexed citations
14.
Carson, Matthew B., Robert Langlois, & Hui Lü. (2008). Mining knowledge for the methylation status of CpG islands using alternating decision trees. 3787–3790. 5 indexed citations
15.
Langlois, Robert, Matthew B. Carson, Nitin Bhardwaj, & Hui Lü. (2007). Learning to Translate Sequence and Structure to Function: Identifying DNA Binding and Membrane Binding Proteins. Annals of Biomedical Engineering. 35(6). 1043–1052. 15 indexed citations
16.
Bhardwaj, Nitin, Robert V. Stahelin, Robert Langlois, Wonhwa Cho, & Hui Lü. (2006). Structural Bioinformatics Prediction of Membrane-binding Proteins. Journal of Molecular Biology. 359(2). 486–495. 48 indexed citations
17.
Bhardwaj, Nina, Robert Langlois, Guijun Zhao, & Hui Lü. (2005). Structure Based Prediction of Binding Residues on DNA-binding Proteins. PubMed. 2005. 2611–2614. 18 indexed citations
18.
Langlois, Robert, et al.. (2005). Kernel based approach for protein fold prediction from sequence. PubMed. 4. 2885–2888. 3 indexed citations
19.
Langlois, Robert, et al.. (2005). Improved protein fold assignment using support vector machines. International Journal of Bioinformatics Research and Applications. 1(3). 319–319. 6 indexed citations
20.
Langlois, Robert, et al.. (2005). Improved protein fold assignment using support vector machines. International Journal of Bioinformatics Research and Applications. 1(3). 319–319. 10 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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