Filippo Geraci

1.5k total citations
35 papers, 864 citations indexed

About

Filippo Geraci is a scholar working on Molecular Biology, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Filippo Geraci has authored 35 papers receiving a total of 864 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 8 papers in Information Systems and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Filippo Geraci's work include Web Data Mining and Analysis (8 papers), Complex Network Analysis Techniques (6 papers) and Genomics and Phylogenetic Studies (6 papers). Filippo Geraci is often cited by papers focused on Web Data Mining and Analysis (8 papers), Complex Network Analysis Techniques (6 papers) and Genomics and Phylogenetic Studies (6 papers). Filippo Geraci collaborates with scholars based in Italy, United States and United Kingdom. Filippo Geraci's co-authors include Marco Pellegrini, Manuela Montangero, Yon Dourisboure, Marco Furini, Miriam Baglioni, Ginestra Bianconi, Fabrizio Sebastiani, Antonella Galizia, Emanuele Salerno and Veronica Morea and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and PLoS ONE.

In The Last Decade

Filippo Geraci

33 papers receiving 826 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 Geraci Italy 15 266 263 193 178 167 35 864
Da Kuang United States 12 226 0.8× 398 1.5× 47 0.2× 157 0.9× 277 1.7× 25 1.0k
Hans‐Jörg Schulz Germany 18 990 3.7× 134 0.5× 217 1.1× 184 1.0× 362 2.2× 57 1.3k
Dandan Song China 18 166 0.6× 216 0.8× 113 0.6× 32 0.2× 503 3.0× 82 1.0k
Xiaohua Hu United States 16 119 0.4× 199 0.8× 67 0.3× 95 0.5× 677 4.1× 94 1.0k
David Auber France 15 634 2.4× 141 0.5× 172 0.9× 265 1.5× 166 1.0× 42 857
Hongjie Bai China 5 269 1.0× 81 0.3× 74 0.4× 119 0.7× 325 1.9× 9 659
Bilal Alsallakh Austria 11 391 1.5× 155 0.6× 105 0.5× 53 0.3× 244 1.5× 24 695
Saeed Salem United States 15 61 0.2× 247 0.9× 124 0.6× 463 2.6× 534 3.2× 52 1.1k
J. Y. Shi Hong Kong 14 87 0.3× 91 0.3× 80 0.4× 114 0.6× 217 1.3× 40 491
Mingdong Ou China 7 278 1.0× 192 0.7× 32 0.2× 504 2.8× 680 4.1× 9 1.0k

Countries citing papers authored by Filippo Geraci

Since Specialization
Citations

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

Fields of papers citing papers by Filippo Geraci

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Filippo Geraci

This figure shows the co-authorship network connecting the top 25 collaborators of Filippo Geraci. A scholar is included among the top collaborators of Filippo Geraci 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 Geraci. Filippo Geraci 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.
Geraci, Filippo, et al.. (2025). Biomarker Profile in Peripheral Blood Cells Related to Alzheimer’s Disease. Molecular Neurobiology. 62(7). 8949–8964.
2.
Zanfardino, Mario, Monica Franzese, & Filippo Geraci. (2024). DeClUt: Decluttering differentially expressed genes through clustering of their expression profiles. Computer Methods and Programs in Biomedicine. 254. 108258–108258. 1 indexed citations
3.
Geraci, Filippo, et al.. (2022). Weighted simplicial complexes and their representation power of higher-order network data and topology. Physical review. E. 106(3). 34319–34319. 49 indexed citations
4.
Andreini, Paolo, Simone Bonechi, Monica Bianchini, & Filippo Geraci. (2022). MicroRNA signature for interpretable breast cancer classification with subtype clue. Use Siena air (University of Siena). 3. 100042–100042. 6 indexed citations
5.
Schiano, Concetta, Monica Franzese, Filippo Geraci, et al.. (2021). Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery. Genes. 12(12). 1946–1946. 10 indexed citations
6.
Geraci, Filippo & Giovanni Manzini. (2021). EZcount: An all-in-one software for microRNA expression quantification from NGS sequencing data. Computers in Biology and Medicine. 133. 104352–104352. 3 indexed citations
7.
Galizia, Antonella, Filippo Geraci, Loredana Le Pera, et al.. (2021). AI applications in functional genomics. Computational and Structural Biotechnology Journal. 19. 5762–5790. 65 indexed citations
8.
Pellegrini, Marco, et al.. (2018). Dot2dot : accurate whole-genome tandem repeats discovery. Bioinformatics. 35(6). 914–922. 16 indexed citations
9.
Saha, Indrajit, et al.. (2018). Genome-wide analysis of NGS data to compile cancer-specific panels of miRNA biomarkers. PLoS ONE. 13(7). e0200353–e0200353. 12 indexed citations
10.
Geraci, Filippo, Lucia Corrado, Eleonora Mangano, et al.. (2018). A Census of Tandemly Repeated Polymorphic Loci in Genic Regions Through the Comparative Integration of Human Genome Assemblies. Frontiers in Genetics. 9. 155–155. 9 indexed citations
11.
Russo, Francesco, Sebastiano Di Bella, Federica Vannini, et al.. (2017). miRandola 2017: a curated knowledge base of non-invasive biomarkers. Nucleic Acids Research. 46(D1). D354–D359. 64 indexed citations
12.
Pellegrini, Marco, Miriam Baglioni, & Filippo Geraci. (2016). Protein complex prediction for large protein protein interaction networks with the Core&Peel method. BMC Bioinformatics. 17(S12). 372–372. 36 indexed citations
13.
Baglioni, Miriam, Francesco Russo, Filippo Geraci, et al.. (2015). A New Method for Discovering Disease-Specific MiRNA-Target Regulatory Networks. PLoS ONE. 10(4). e0122473–e0122473. 6 indexed citations
14.
Geraci, Filippo, et al.. (2013). A Framework to Evaluate Information Quality in Public Administration Website. Journal of the Association for Information Systems. 25–42.
15.
Baglioni, Miriam, et al.. (2012). Fast Exact Computation of betweenness Centrality in Social Networks. ISTI Open Portal. 450–456. 28 indexed citations
16.
Geraci, Filippo, Marco Pellegrini, & M. Elena Renda. (2008). AMIC@: All MIcroarray Clusterings @ once. Nucleic Acids Research. 36(Web Server). W315–W319. 7 indexed citations
17.
Geraci, Filippo, et al.. (2008). SpeedHap: An Accurate Heuristic for the Single Individual SNP Haplotyping Problem with Many Gaps, High Reading Error Rate and Low Coverage. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 5(4). 492–502. 23 indexed citations
18.
Geraci, Filippo & Marco Pellegrini. (2008). Dynamic User-Defined Similarity Searching in Semi-Structured Text Retrieval. 1 indexed citations
19.
Geraci, Filippo, Marco Pellegrini, Marco Maggini, & Fabrizio Sebastiani. (2006). Cluster Generation and Cluster Labelling for Web Snippets. 25–36. 8 indexed citations
20.
Geraci, Filippo, et al.. (2005). Packet classification via improved space decomposition techniques. CINECA IRIS Institutial research information system (University of Pisa). 304–312 vol. 1. 17 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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