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.
The importance of interpretability and visualization in machine learning for applications in medicine and health care
Citations per year, relative to Alfredo Vellido Alfredo Vellido (= 1×)
peers
Paulo Lisböa
Countries citing papers authored by Alfredo Vellido
Since
Specialization
Citations
This map shows the geographic impact of Alfredo Vellido'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 Alfredo Vellido with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alfredo Vellido more than expected).
This network shows the impact of papers produced by Alfredo Vellido. 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 Alfredo Vellido. The network helps show where Alfredo Vellido may publish in the future.
Co-authorship network of co-authors of Alfredo Vellido
This figure shows the co-authorship network connecting the top 25 collaborators of Alfredo Vellido.
A scholar is included among the top collaborators of Alfredo Vellido 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 Alfredo Vellido. Alfredo Vellido is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bacciu, Davide, Battista Biggio, Paulo Lisböa, et al.. (2019). Societal issues in machine learning: when learning from data is not enough. QRU Quaderns de Recerca en Urbanisme. 455–464.1 indexed citations
5.
Ortega‐Martorell, Sandra, Johannes Slotboom, Urspeter Knecht, et al.. (2016). A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases. RECERCAT (Consorci de Serveis Universitaris de Catalunya). 247–252.4 indexed citations
6.
Vellido, Alfredo, et al.. (2016). Bayesian semi non-negative matrix factorisation. The European Symposium on Artificial Neural Networks. 195–200.2 indexed citations
7.
Lisböa, Paulo, et al.. (2016). Physics and machine learning: Emerging paradigms. The European Symposium on Artificial Neural Networks. 319–326.2 indexed citations
8.
Vellido, Alfredo, et al.. (2014). Misclassification of class C G-protein-coupled receptors as a label noise problem. RECERCAT (Consorci de Serveis Universitaris de Catalunya). 695–700.2 indexed citations
9.
Vellido, Alfredo, et al.. (2013). Advances in Semi-Supervised Alignment-Free Classication of G Protein-Coupled Receptors.. 759–766.4 indexed citations
10.
Nebot, Àngela, et al.. (2013). Visualizing pay-per-view television customers churn using cartograms and flow maps.. The European Symposium on Artificial Neural Networks.2 indexed citations
11.
Vellido, Alfredo, et al.. (2013). Robust cartogram visualization of outliers in manifold learning. RECERCAT (Consorci de Serveis Universitaris de Catalunya). 555–560.1 indexed citations
12.
Olier, Iván, Alfredo Vellido, & Jesús Giraldo. (2010). Kernel Generative Topographic Mapping. The European Symposium on Artificial Neural Networks.12 indexed citations
13.
Romero, Enrique, Margarida Julià‐Sapé, & Alfredo Vellido. (2008). DSS-oriented exploration of a multi-centre magnetic resonance spectroscopy brain tumour dataset through visualization. The European Symposium on Artificial Neural Networks. 95–100.1 indexed citations
Etchells, Terence A., Àngela Nebot, Alfredo Vellido, Paulo Lisböa, & Francisco Mugica. (2006). Learning what is important: feature selection and rule extraction in a virtual course. The European Symposium on Artificial Neural Networks. 401–406.20 indexed citations
16.
Nebot, Àngela, Félix Castro, Francisco Mugica, & Alfredo Vellido. (2006). Identification of fuzzy models to predict students performance in an e-learning environment. 74–79.15 indexed citations
17.
Vellido, Alfredo, et al.. (2005). Handling outliers and missing data in brain tumour clinical assessment using t-GTM.. The European Symposium on Artificial Neural Networks. 121–126.3 indexed citations
Vellido, Alfredo, Paulo Lisböa, & Karon Meehan. (2000). The generative topographic mapping as a principal model for data visualization and market segmentation: an electronic commerce case.. 1. 119–138.2 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.