Filippo Utro

3.1k total citations
48 papers, 490 citations indexed

About

Filippo Utro is a scholar working on Molecular Biology, Genetics and Artificial Intelligence. According to data from OpenAlex, Filippo Utro has authored 48 papers receiving a total of 490 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 11 papers in Genetics and 10 papers in Artificial Intelligence. Recurrent topics in Filippo Utro's work include Genomics and Phylogenetic Studies (12 papers), Bioinformatics and Genomic Networks (11 papers) and Gene expression and cancer classification (9 papers). Filippo Utro is often cited by papers focused on Genomics and Phylogenetic Studies (12 papers), Bioinformatics and Genomic Networks (11 papers) and Gene expression and cancer classification (9 papers). Filippo Utro collaborates with scholars based in United States, Italy and Ireland. Filippo Utro's co-authors include Raffaele Giancarlo, Simona E. Rombo, Niina Haiminen, Laxmi Parida, Erhan Bilal, Aristotelis Tsirigos, Dalila Scaturro, Boaz Carmeli, Zeev Waks and Omer Weissbrod and has published in prestigious journals such as Journal of Clinical Oncology, Blood and Bioinformatics.

In The Last Decade

Filippo Utro

44 papers receiving 479 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Filippo Utro United States 11 348 141 58 46 39 48 490
Francis D. Gibbons United States 10 533 1.5× 91 0.6× 40 0.7× 37 0.8× 18 0.5× 19 685
German Tischler United Kingdom 11 271 0.8× 79 0.6× 96 1.7× 84 1.8× 48 1.2× 21 486
Brad Solomon United States 7 239 0.7× 70 0.5× 52 0.9× 21 0.5× 32 0.8× 11 331
Aleksi Kallio Finland 12 254 0.7× 64 0.5× 40 0.7× 17 0.4× 65 1.7× 25 416
Chaoyang Zhang United States 14 496 1.4× 68 0.5× 73 1.3× 46 1.0× 48 1.2× 43 651
Raluca Uricaru France 6 217 0.6× 59 0.4× 37 0.6× 30 0.7× 35 0.9× 13 281
Brandon Malone United States 13 291 0.8× 168 1.2× 39 0.7× 94 2.0× 55 1.4× 29 605
Pietro Pinoli Italy 15 359 1.0× 124 0.9× 29 0.5× 9 0.2× 52 1.3× 60 555
Markus Hsi-Yang Fritz Germany 4 253 0.7× 127 0.9× 67 1.2× 44 1.0× 50 1.3× 5 365
Yizhou Li China 15 562 1.6× 62 0.4× 41 0.7× 20 0.4× 63 1.6× 62 746

Countries citing papers authored by Filippo Utro

Since Specialization
Citations

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

Fields of papers citing papers by Filippo Utro

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Filippo Utro

This figure shows the co-authorship network connecting the top 25 collaborators of Filippo Utro. A scholar is included among the top collaborators of Filippo Utro 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 Filippo Utro. Filippo Utro 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.
Utro, Filippo, et al.. (2025). Probing omics data via harmonic persistent homology. Scientific Reports. 15(1). 38836–38836.
2.
Platt, Daniel E., et al.. (2024). AI-enabled evaluation of genome-wide association relevance and polygenic risk score prediction in Alzheimer's disease. iScience. 27(3). 109209–109209. 3 indexed citations
3.
Gardiner, Laura‐Jayne, Niina Haiminen, Jennifer Kelly, et al.. (2024). AutoXAI4Omics: an automated explainable AI tool for omics and tabular data. Briefings in Bioinformatics. 26(1). 6 indexed citations
4.
Rhrissorrakrai, Kahn, Filippo Utro, Chaya Levovitz, & Laxmi Parida. (2023). Lesion Shedding Model: unraveling site-specific contributions to ctDNA. Briefings in Bioinformatics. 24(2). 7 indexed citations
5.
Chen, Zigui, Filippo Utro, Daniel E. Platt, et al.. (2021). K-Mer Analyses Reveal Different Evolutionary Histories of Alpha, Beta, and Gamma Papillomaviruses. International Journal of Molecular Sciences. 22(17). 9657–9657. 8 indexed citations
6.
Gardiner, Laura‐Jayne, Niina Haiminen, Filippo Utro, et al.. (2021). Re-purposing software for functional characterization of the microbiome. Microbiome. 9(1). 4–4. 8 indexed citations
7.
Utro, Filippo, Chaya Levovitz, Kahn Rhrissorrakrai, & Laxmi Parida. (2021). A common methodological phylogenomics framework for intra-patient heteroplasmies to infer SARS-CoV-2 sublineages and tumor clones. BMC Genomics. 22(S5). 518–518. 2 indexed citations
8.
Haiminen, Niina, et al.. (2021). Functional profiling of COVID-19 respiratory tract microbiomes. Scientific Reports. 11(1). 6433–6433. 14 indexed citations
9.
Utro, Filippo, Niina Haiminen, Enrico Siragusa, et al.. (2020). Hierarchically Labeled Database Indexing Allows Scalable Characterization of Microbiomes. iScience. 23(4). 100988–100988. 2 indexed citations
10.
Giancarlo, Raffaele, Simona E. Rombo, & Filippo Utro. (2018). DNA combinatorial messages and Epigenomics: The case of chromatin organization and nucleosome occupancy in eukaryotic genomes. Theoretical Computer Science. 792. 117–130. 4 indexed citations
11.
Waks, Zeev, Omer Weissbrod, Boaz Carmeli, et al.. (2016). Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins. Scientific Reports. 6(1). 38988–38988. 21 indexed citations
12.
Giancarlo, Raffaele, Dalila Scaturro, & Filippo Utro. (2015). ValWorkBench: An open source Java library for cluster validation, with applications to microarray data analysis. Computer Methods and Programs in Biomedicine. 118(2). 207–217. 4 indexed citations
13.
Giancarlo, Raffaele, Giosuè Lo Bosco, & Filippo Utro. (2014). Bayesian versus data driven model selection for microarray data. Natural Computing. 14(3). 393–402. 3 indexed citations
14.
Utro, Filippo, Niina Haiminen, Donald Livingstone, et al.. (2013). iXora: exact haplotype inferencing and trait association. BMC Genetics. 14(1). 48–48. 8 indexed citations
15.
Utro, Filippo, Marc Pybus, & Laxmi Parida. (2013). Sum of parts is greater than the whole: inference of common genetic history of populations. BMC Genomics. 14(S1). S10–S10. 2 indexed citations
16.
Giancarlo, Raffaele, Simona E. Rombo, & Filippo Utro. (2013). Compressive biological sequence analysis and archival in the era of high-throughput sequencing technologies. Briefings in Bioinformatics. 15(3). 390–406. 40 indexed citations
17.
Giancarlo, Raffaele & Filippo Utro. (2012). Algorithmic paradigms for stability-based cluster validity and model selection statistical methods, with applications to microarray data analysis. Theoretical Computer Science. 428. 58–79. 12 indexed citations
18.
Giancarlo, Raffaele, Dalila Scaturro, & Filippo Utro. (2012). Textual data compression in computational biology: Algorithmic techniques. Computer Science Review. 6(1). 1–25. 17 indexed citations
19.
20.
Giancarlo, Raffaele, et al.. (2007). A basic analysis toolkit for biological sequences. Algorithms for Molecular Biology. 2(1). 10–10. 3 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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