Viktor Granholm

658 total citations
10 papers, 465 citations indexed

About

Viktor Granholm is a scholar working on Molecular Biology, Spectroscopy and Microbiology. According to data from OpenAlex, Viktor Granholm has authored 10 papers receiving a total of 465 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 9 papers in Spectroscopy and 1 paper in Microbiology. Recurrent topics in Viktor Granholm's work include Advanced Proteomics Techniques and Applications (9 papers), Mass Spectrometry Techniques and Applications (6 papers) and Machine Learning in Bioinformatics (5 papers). Viktor Granholm is often cited by papers focused on Advanced Proteomics Techniques and Applications (9 papers), Mass Spectrometry Techniques and Applications (6 papers) and Machine Learning in Bioinformatics (5 papers). Viktor Granholm collaborates with scholars based in Sweden, United States and Spain. Viktor Granholm's co-authors include Lukas Käll, William Stafford Noble, José Fernández Navarro, Henrik J. Johansson, Rui M. Branca, Janne Lehtiö, Åsa Pérez-Bercoff, Jenny Forshed, Mikael Huss and Lukas M. Orre and has published in prestigious journals such as Nature Methods, BMC Bioinformatics and Biopolymers.

In The Last Decade

Viktor Granholm

10 papers receiving 460 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Viktor Granholm Sweden	8	384	280	33	29	16	10	465
Annie Ha Canada	4	352 0.9×	251 0.9×	35 1.1×	28 1.0×	35 2.2×	9	505
Klemens Fröhlich Germany	8	218 0.6×	167 0.6×	27 0.8×	16 0.6×	11 0.7×	17	300
Sally J. Deeb Germany	3	216 0.6×	124 0.4×	31 0.9×	28 1.0×	26 1.6×	4	292
Melanie Christine Föll Germany	11	168 0.4×	133 0.5×	25 0.8×	22 0.8×	9 0.6×	19	278
Min‐Seok Kwon South Korea	7	169 0.4×	119 0.4×	27 0.8×	29 1.0×	10 0.6×	13	277
René B. H. Braakman Netherlands	10	233 0.6×	178 0.6×	47 1.4×	63 2.2×	8 0.5×	15	332
Simonas Savickas Denmark	8	257 0.7×	138 0.5×	42 1.3×	39 1.3×	25 1.6×	13	362
Debbie L. Cunningham United Kingdom	12	328 0.9×	207 0.7×	47 1.4×	25 0.9×	29 1.8×	17	480
Amir Rahbar United States	8	206 0.5×	133 0.5×	25 0.8×	18 0.6×	13 0.8×	12	308
Uli Ohmayer Germany	12	577 1.5×	151 0.5×	94 2.8×	20 0.7×	32 2.0×	17	651

Countries citing papers authored by Viktor Granholm

Since Specialization

Citations

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

Fields of papers citing papers by Viktor Granholm

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Viktor Granholm

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

All Works

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10 of 10 papers shown

1.

Moruz, Luminita, et al.. (2013). Mass Fingerprinting of Complex Mixtures: Protein Inference from High-Resolution Peptide Masses and Predicted Retention Times. Journal of Proteome Research. 12(12). 5730–5741. 14 indexed citations

2.

Branca, Rui M., Lukas M. Orre, Henrik J. Johansson, et al.. (2013). HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics. Nature Methods. 11(1). 59–62. 195 indexed citations

3.

Skwark, Marcin J., et al.. (2013). Membrane protein shaving with thermolysin can be used to evaluate topology predictors. PROTEOMICS. 13(9). 1467–1480. 8 indexed citations

4.

Granholm, Viktor, Sangtae Kim, José Fernández Navarro, et al.. (2013). Fast and Accurate Database Searches with MS-GF+Percolator. Journal of Proteome Research. 13(2). 890–897. 75 indexed citations

5.

Granholm, Viktor, William Stafford Noble, & Lukas Käll. (2012). A cross-validation scheme for machine learning algorithms in shotgun proteomics. BMC Bioinformatics. 13(S16). S3–S3. 41 indexed citations

6.

Granholm, Viktor, José Fernández Navarro, William Stafford Noble, & Lukas Käll. (2012). Determining the calibration of confidence estimation procedures for unique peptides in shotgun proteomics. Journal of Proteomics. 80. 123–131. 42 indexed citations

7.

Granholm, Viktor & Lukas Käll. (2011). Quality assessments of peptide–spectrum matches in shotgun proteomics. PROTEOMICS. 11(6). 1086–1093. 36 indexed citations

8.

Granholm, Viktor, William Stafford Noble, & Lukas Käll. (2011). On Using Samples of Known Protein Content to Assess the Statistical Calibration of Scores Assigned to Peptide-Spectrum Matches in Shotgun Proteomics. Journal of Proteome Research. 10(5). 2671–2678. 46 indexed citations

9.

Granholm, Viktor, William Stafford Noble, & Lukas Käll. (2011). On Using Samples of Known Protein Content to Assess the Statistical Calibration of Scores Assigned to Peptide-Spectrum Matches in Shotgun Proteomics. Journal of Proteome Research. 10(8). 3844–3844. 4 indexed citations

10.

Takahashi, Tsuyoshi, et al.. (2010). Design and conformational analysis of natively folded β‐hairpin peptides stabilized by nucleobase interactions. Biopolymers. 94(6). 830–842. 4 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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