Ferrán Sanz

14.6k total citations · 4 hit papers
187 papers, 8.8k citations indexed

About

Ferrán Sanz is a scholar working on Molecular Biology, Computational Theory and Mathematics and Organic Chemistry. According to data from OpenAlex, Ferrán Sanz has authored 187 papers receiving a total of 8.8k indexed citations (citations by other indexed papers that have themselves been cited), including 88 papers in Molecular Biology, 42 papers in Computational Theory and Mathematics and 24 papers in Organic Chemistry. Recurrent topics in Ferrán Sanz's work include Computational Drug Discovery Methods (42 papers), Receptor Mechanisms and Signaling (23 papers) and Bioinformatics and Genomic Networks (23 papers). Ferrán Sanz is often cited by papers focused on Computational Drug Discovery Methods (42 papers), Receptor Mechanisms and Signaling (23 papers) and Bioinformatics and Genomic Networks (23 papers). Ferrán Sanz collaborates with scholars based in Spain, Germany and Netherlands. Ferrán Sanz's co-authors include Laura I. Furlong, Janet Piñero, Emilio Centeno, Álex Bravo, Núria Queralt-Rosiñach, Jordi Deu-Pons, Francesco Ronzano, Juan Manuel Ramírez‐Anguita, Alba Gutiérrez‐Sacristán and Anna Bauer‐Mehren and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Bioinformatics.

In The Last Decade

Ferrán Sanz

182 papers receiving 8.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

2016 1.8k citations Janet Piñero, Álex Bravo et al. Nucleic Acids Research profile →
The DisGeNET knowledge platform for disease genomics: 2019 update

2019 1.8k citations Janet Piñero, Juan Manuel Ramírez‐Anguita et al. Nucleic Acids Research profile →
DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes

2015 785 citations Janet Piñero, Núria Queralt-Rosiñach et al. profile →
The DisGeNET cytoscape app: Exploring and visualizing disease genomics data

2021 338 citations Janet Piñero, Ferrán Sanz et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ferrán Sanz Spain	35	5.1k	1.6k	832	767	683	187	8.8k
Christopher P. Austin United States	59	5.7k 1.1×	2.5k 1.5×	693 0.8×	881 1.1×	884 1.3×	172	11.9k
Ruili Huang United States	48	3.3k 0.6×	2.6k 1.6×	793 1.0×	602 0.8×	644 0.9×	219	8.0k
Douglas S. Auld United States	42	4.8k 0.9×	1.1k 0.7×	328 0.4×	365 0.5×	1.0k 1.5×	120	7.1k
Ismail Kola Australia	43	4.0k 0.8×	831 0.5×	523 0.6×	760 1.0×	552 0.8×	94	8.1k
Anton Simeonov United States	65	8.4k 1.7×	2.4k 1.4×	484 0.6×	828 1.1×	835 1.2×	298	14.1k
Ajit Jadhav United States	50	5.1k 1.0×	1.3k 0.8×	304 0.4×	498 0.6×	543 0.8×	150	8.1k
Bissan Al‐Lazikani United Kingdom	30	8.0k 1.6×	3.7k 2.3×	508 0.6×	700 0.9×	642 0.9×	58	11.2k
William J. Welsh United States	46	3.0k 0.6×	1.5k 0.9×	472 0.6×	734 1.0×	239 0.3×	231	7.5k
Feng Zhu China	58	8.5k 1.7×	3.8k 2.3×	1.0k 1.2×	454 0.6×	1.1k 1.6×	291	13.3k
Noel Southall United States	48	4.1k 0.8×	1.1k 0.7×	379 0.5×	295 0.4×	584 0.9×	189	8.6k

Countries citing papers authored by Ferrán Sanz

Since Specialization

Citations

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

Fields of papers citing papers by Ferrán Sanz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ferrán Sanz

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Pastor, Antoni, et al.. (2023). The role of PPAR-γ in memory deficits induced by prenatal and lactation alcohol exposure in mice. Molecular Psychiatry. 28(8). 3373–3383. 5 indexed citations

Cronin, M, Katharine Briggs, Steven J. Enoch, et al.. (2023). Making in silico predictive models for toxicology FAIR. Regulatory Toxicology and Pharmacology. 140. 105385–105385. 17 indexed citations

Casadevall, David, Joan Albanell, Margarita Posso, et al.. (2022). Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical Notes. JMIR Cancer. 8(3). e39003–e39003. 2 indexed citations

Giannoula, Alexia, Emilio Centeno, Miguel Ángel Mayer, Ferrán Sanz, & Laura I. Furlong. (2020). A system-level analysis of patient disease trajectories based on clinical, phenotypic and molecular similarities. Bioinformatics. 37(10). 1435–1443. 10 indexed citations

Ronzano, Francesco, et al.. (2020). Evaluating Behavioral and Linguistic Changes During Drug Treatment for Depression Using Tweets in Spanish: Pairwise Comparison Study. Journal of Medical Internet Research. 22(12). e20920–e20920. 1 indexed citations

Mayer, Miguel Ángel, Francesco Ronzano, Marta Torrens, et al.. (2020). Clinical-Based and Expert Selection of Terms Related to Depression for Twitter Streaming and Language Analysis. Studies in health technology and informatics. 270. 921–925. 2 indexed citations

Ronzano, Francesco, et al.. (2019). Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis. Journal of Medical Internet Research. 21(6). e14199–e14199. 70 indexed citations

Piñero, Janet, Juan Manuel Ramírez‐Anguita, Francesco Ronzano, et al.. (2019). The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Research. 48(D1). D845–D855. 1752 indexed citations breakdown →

Gutiérrez‐Sacristán, Alba, Álex Bravo, Alexia Giannoula, et al.. (2018). comoRbidity: an R package for the systematic analysis of disease comorbidities. Bioinformatics. 34(18). 3228–3230. 27 indexed citations

10.

Piñero, Janet, Laura I. Furlong, & Ferrán Sanz. (2018). In silico models in drug development: where we are. Current Opinion in Pharmacology. 42. 111–121. 31 indexed citations

11.

Mayer, Miguel Ángel, et al.. (2017). Using Electronic Health Records to Assess Depression and Cancer Comorbidities.. PubMed. 235. 236–240. 7 indexed citations

12.

Rubio-Pérez, Carlota, Emre Güney, Daniel Aguilar, et al.. (2017). Genetic and functional characterization of disease associations explains comorbidity. Scientific Reports. 7(1). 6207–6207. 27 indexed citations

13.

Queralt-Rosiñach, Núria, Janet Piñero, Álex Bravo, Ferrán Sanz, & Laura I. Furlong. (2016). DisGeNET-RDF: harnessing the innovative power of the Semantic Web to explore the genetic basis of diseases. Bioinformatics. 32(14). 2236–2238. 37 indexed citations

14.

Piñero, Janet, Álex Bravo, Núria Queralt-Rosiñach, et al.. (2016). DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Research. 45(D1). D833–D839. 1779 indexed citations breakdown →

15.

García-Remesal, Miguel, et al.. (2013). The Impact of Computer Science in Molecular Medicine: Enabling High- Throughput Research. Current Topics in Medicinal Chemistry. 13(5). 526–575. 8 indexed citations

16.

Molero, Eva, Ferrán Sanz, José Luís Oliveira, et al.. (2012). the Eu-adr Alliance: A Federated Collaborative Framework for Drug Safety Studies : 667.. Pharmacoepidemiology and Drug Safety. 21. 311–312. 1 indexed citations

17.

Gutiérrez‐de‐Terán, Hugo, Juan José Lozano, Vı́ctor Segarra, & Ferrán Sanz. (2002). Molecular Diversity Sample Generation on the Basis of Quantum-Mechanical Computations and Principal Component Analysis. Combinatorial Chemistry & High Throughput Screening. 5(1). 49–57. 3 indexed citations

18.

Sancho, Elena, Maya R. Vilà, Luis Sánchez‐Pulido, et al.. (1998). Role of UEV-1, an Inactive Variant of the E2 UbiquitinConjugating Enzymes, in In Vitro Differentiation and Cell Cycle Behavior of HT-29-M6 Intestinal Mucosecretory Cells. Molecular and Cellular Biology. 18(1). 576–589. 111 indexed citations

19.

Sitges‐Serra, Antonio, et al.. (1995). Influence of nutrition, thyroid hormones, and rectal temperature on in-hospital mortality of elderly patients with acute illness. American Journal of Clinical Nutrition. 61(3). 597–602. 16 indexed citations

20.

Sancho, J., et al.. (1994). Validation of the Medical Expert System RENOIR. Computers and Biomedical Research. 27(6). 456–471. 20 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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