Ramón Viñas

1.2k total citations · 1 hit paper
9 papers, 523 citations indexed

About

Ramón Viñas is a scholar working on Molecular Biology, Artificial Intelligence and Cancer Research. According to data from OpenAlex, Ramón Viñas has authored 9 papers receiving a total of 523 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 4 papers in Artificial Intelligence and 3 papers in Cancer Research. Recurrent topics in Ramón Viñas's work include Single-cell and spatial transcriptomics (2 papers), Machine Learning in Healthcare (2 papers) and Cancer Genomics and Diagnostics (2 papers). Ramón Viñas is often cited by papers focused on Single-cell and spatial transcriptomics (2 papers), Machine Learning in Healthcare (2 papers) and Cancer Genomics and Diagnostics (2 papers). Ramón Viñas collaborates with scholars based in United Kingdom, Spain and United States. Ramón Viñas's co-authors include Yu Fu, Moritz Gerstung, Artem Shmatko, Lucy Yates, Alexander W. Jung, Santiago González, Mercedes Jimenez‐Liñan, Harald Vöhringer, Luiza Moore and Píetro Lió and has published in prestigious journals such as Nature Biotechnology, Bioinformatics and Frontiers in Genetics.

In The Last Decade

Ramón Viñas

9 papers receiving 513 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.

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

2020 367 citations Yu Fu, Alexander W. Jung et al. Nature Cancer profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ramón Viñas United Kingdom	6	263	187	134	133	82	9	523
Maha Shady United States	5	354 1.3×	270 1.4×	97 0.7×	98 0.7×	70 0.9×	10	522
Charlie Saillard France	6	379 1.4×	433 2.3×	116 0.9×	145 1.1×	146 1.8×	12	754
Benoît Schmauch France	8	370 1.4×	448 2.4×	111 0.8×	134 1.0×	126 1.5×	14	763
Yi‐Jia Lin Taiwan	14	240 0.9×	174 0.9×	108 0.8×	55 0.4×	85 1.0×	30	509
Mane Williams United States	5	431 1.6×	338 1.8×	127 0.9×	114 0.9×	107 1.3×	6	707
Andrew Zhang United States	4	362 1.4×	265 1.4×	88 0.7×	58 0.4×	84 1.0×	6	619
Meriem Sefta France	5	347 1.3×	305 1.6×	143 1.1×	155 1.2×	157 1.9×	6	688
Alexander W. Jung United Kingdom	3	221 0.8×	180 1.0×	78 0.6×	120 0.9×	85 1.0×	5	381
Jerome Cheng United States	14	305 1.2×	185 1.0×	117 0.9×	58 0.4×	101 1.2×	49	627
Jenny L. Smith United States	12	384 1.5×	310 1.7×	193 1.4×	112 0.8×	95 1.2×	44	882

Countries citing papers authored by Ramón Viñas

Since Specialization

Citations

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

Fields of papers citing papers by Ramón Viñas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ramón Viñas

This figure shows the co-authorship network connecting the top 25 collaborators of Ramón Viñas. A scholar is included among the top collaborators of Ramón Viñas 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 Ramón Viñas. Ramón Viñas 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:

9 of 9 papers shown

1.

Viñas, Ramón, Zoe Piran, Shilong Fan, et al.. (2025). Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation. Nature Biotechnology. 3 indexed citations

2.

Viñas, Ramón, Chaitanya K. Joshi, P. Charles Lin, et al.. (2023). Hypergraph factorization for multi-tissue gene expression imputation. Nature Machine Intelligence. 5(7). 739–753. 20 indexed citations

3.

Viñas, Ramón, et al.. (2021). Adversarial generation of gene expression data. Bioinformatics. 38(3). 730–737. 29 indexed citations

4.

Barbiero, Pietro, Ramón Viñas, & Píetro Lió. (2021). Graph Representation Forecasting of Patient's Medical Conditions: Toward a Digital Twin. Frontiers in Genetics. 12. 652907–652907. 38 indexed citations

5.

Viñas, Ramón, Tiago Azevedo, Eric R. Gamazon, & Píetro Lió. (2021). Deep Learning Enables Fast and Accurate Imputation of Gene Expression. Frontiers in Genetics. 12. 624128–624128. 11 indexed citations

6.

Scherer, Paul, Maja Trębacz, Nikola Simidjievski, et al.. (2021). Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases. Bioinformatics. 38(5). 1320–1327. 2 indexed citations

7.

Fu, Yu, Alexander W. Jung, Ramón Viñas, et al.. (2020). Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nature Cancer. 1(8). 800–810. 367 indexed citations breakdown →

8.

López, Beatriz, et al.. (2017). Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction. Artificial Intelligence in Medicine. 85. 43–49. 51 indexed citations

9.

López, Beatriz, et al.. (2016). Handling Missing Phenotype Data With Random Forests For Diabetes Risk Prognosis. Universitat de Girona Digital Repository (Universitat de Girona). 2 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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