Matteo Comin

1.1k total citations
57 papers, 567 citations indexed

About

Matteo Comin is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Matteo Comin has authored 57 papers receiving a total of 567 indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Molecular Biology, 27 papers in Artificial Intelligence and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Matteo Comin's work include Genomics and Phylogenetic Studies (35 papers), Algorithms and Data Compression (26 papers) and Machine Learning in Bioinformatics (20 papers). Matteo Comin is often cited by papers focused on Genomics and Phylogenetic Studies (35 papers), Algorithms and Data Compression (26 papers) and Machine Learning in Bioinformatics (20 papers). Matteo Comin collaborates with scholars based in Italy, United States and France. Matteo Comin's co-authors include Cinzia Pizzi, Alberto Apostolico, Laxmi Parida, Concettina Guerra, Giuseppe Zanotti, Guillaume Bernard, Benjamin T. James, Susana Vinga, Michael S. Waterman and Cheong Xin Chan and has published in prestigious journals such as Bioinformatics, Genome biology and BMC Bioinformatics.

In The Last Decade

Matteo Comin

52 papers receiving 561 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matteo Comin Italy 13 488 217 66 54 49 57 567
Phillip E. C. Compeau United States 6 325 0.7× 96 0.4× 51 0.8× 93 1.7× 70 1.4× 7 445
Mikhail Roytberg Russia 14 552 1.1× 126 0.6× 24 0.4× 66 1.2× 81 1.7× 36 655
François Rechenmann France 11 454 0.9× 181 0.8× 28 0.4× 49 0.9× 72 1.5× 21 556
Sofie Van Landeghem Belgium 13 449 0.9× 231 1.1× 26 0.4× 162 3.0× 27 0.6× 21 616
Antoine Limasset France 8 294 0.6× 100 0.5× 61 0.9× 69 1.3× 29 0.6× 14 335
Adam Gudyś Poland 9 216 0.4× 66 0.3× 53 0.8× 54 1.0× 20 0.4× 18 336
Chaoyang Zhang United States 14 496 1.0× 68 0.3× 14 0.2× 46 0.9× 73 1.5× 43 651
Michal Ziv-Ukelson Israel 13 662 1.4× 168 0.8× 20 0.3× 19 0.4× 71 1.4× 44 793
Jiří Vohradský Czechia 14 606 1.2× 74 0.3× 82 1.2× 38 0.7× 165 3.4× 27 727
David Koslicki United States 15 419 0.9× 58 0.3× 140 2.1× 31 0.6× 36 0.7× 38 552

Countries citing papers authored by Matteo Comin

Since Specialization
Citations

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

Fields of papers citing papers by Matteo Comin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matteo Comin

This figure shows the co-authorship network connecting the top 25 collaborators of Matteo Comin. A scholar is included among the top collaborators of Matteo Comin 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 Matteo Comin. Matteo Comin 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.
Comin, Matteo, et al.. (2024). Enhanced Compression of k -Mer Sets with Counters via de Bruijn Graphs. Journal of Computational Biology. 31(6). 524–538.
2.
Pizzi, Cinzia, et al.. (2024). MISSH: Fast Hashing of Multiple Spaced Seeds. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 21(6). 2330–2339.
3.
Pizzi, Cinzia, et al.. (2021). MetaProb 2: Metagenomic Reads Binning Based on Assembly Using Minimizers and K-Mers Statistics. Journal of Computational Biology. 28(11). 1052–1062. 8 indexed citations
4.
Comin, Matteo, Barbara Di Camillo, Cinzia Pizzi, & Fabio Vandin. (2020). Comparison of microbiome samples: methods and computational challenges. Briefings in Bioinformatics. 22(1). 88–95. 17 indexed citations
5.
Noé, Laurent, et al.. (2019). Iterative Spaced Seed Hashing: Closing the Gap Between Spaced Seed Hashing and k -mer Hashing. Journal of Computational Biology. 27(2). 223–233. 9 indexed citations
6.
Zieleziński, Andrzej, Hani Z. Girgis, Guillaume Bernard, et al.. (2019). Benchmarking of alignment-free sequence comparison methods. Genome biology. 20(1). 144–144. 123 indexed citations
7.
Comin, Matteo, et al.. (2019). Better quality score compression through sequence-based quality smoothing. BMC Bioinformatics. 20(S9). 302–302. 9 indexed citations
8.
Comin, Matteo, et al.. (2018). FSH: fast spaced seed hashing exploiting adjacent hashes. Algorithms for Molecular Biology. 13(1). 8–8. 7 indexed citations
9.
Comin, Matteo, et al.. (2017). Higher recall in metagenomic sequence classification exploiting overlapping reads. BMC Genomics. 18(S10). 917–917. 8 indexed citations
10.
Comin, Matteo, et al.. (2017). Binning metagenomic reads with probabilistic sequence signatures based on spaced seeds. Padua Research Archive (University of Padova). 1–8. 2 indexed citations
11.
Comin, Matteo, et al.. (2016). Fast comparison of genomic and meta-genomic reads with alignment-free measures based on quality values. BMC Medical Genomics. 9(S1). 36–36. 5 indexed citations
12.
Comin, Matteo, et al.. (2015). Clustering of reads with alignment-free measures and quality values. Algorithms for Molecular Biology. 10(1). 4–4. 17 indexed citations
13.
Apostolico, Alberto, et al.. (2013). Ultrametric networks: a new tool for phylogenetic analysis. Algorithms for Molecular Biology. 8(1). 7–7. 4 indexed citations
14.
Comin, Matteo, et al.. (2011). The Irredundant Class Method for Remote Homology Detection of Protein Sequences. Journal of Computational Biology. 18(12). 1819–1829. 13 indexed citations
15.
Comin, Matteo, et al.. (2010). Classification of protein sequences by means of irredundant patterns. BMC Bioinformatics. 11(S1). S16–S16. 14 indexed citations
16.
Apostolico, Alberto, Matteo Comin, & Laxmi Parida. (2008). VARUN: Discovering Extensible Motifs under Saturation Constraints. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 7(4). 752–762. 16 indexed citations
17.
Comin, Matteo, Concettina Guerra, & Giuseppe Zanotti. (2008). MINING OVERREPRESENTED 3D PATTERNS OF SECONDARY STRUCTURES IN PROTEINS. Journal of Bioinformatics and Computational Biology. 6(6). 1067–1087. 1 indexed citations
18.
Comin, Matteo & Laxmi Parida. (2007). SUBTLE MOTIF DISCOVERY FOR DETECTION OF DNA REGULATORY SITES. Research Padua Archive (University of Padua). 27–36. 6 indexed citations
19.
Apostolico, Alberto, Matteo Comin, & Laxmi Parida. (2006). Mining, compressing and classifying with extensible motifs. Algorithms for Molecular Biology. 1(1). 4–4. 18 indexed citations
20.
Comin, Matteo, Concettina Guerra, & Giuseppe Zanotti. (2004). PROuST: a server based comparison method of three-dimensional structures of proteins using indexing techniques. Research Padua Archive (University of Padua). 11. 1061–1072. 6 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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