Juan L. Mateo
- Molecular Biology top 10%
- Genetics top 10%
- Plant Science top 10%
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Joachim WittbrodtThomas ThumbergerManuel StemmerJosé A. GámezJosé M. PuertaAntonio Fernández‐CaballeroAdrian SchwarzerMaurice Labuhn
- Topics
- Bayesian Modeling and Causal Inference (6 papers)Genomics and Chromatin Dynamics (4 papers)RNA Research and Splicing (4 papers)
- Partner nations
- SpainGermanyUnited States
In The Last Decade
Juan L. Mateo
28 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 135
- Molecular Biology 1.1k
- Genetics 230
- Plant Science 212
- Artificial Intelligence 132
- Computer Vision and Pattern Recognition 102
Countries citing papers authored by Juan L. Mateo
This map shows the geographic impact of Juan L. Mateo'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 Juan L. Mateo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Juan L. Mateo more than expected).
Fields of papers citing papers by Juan L. Mateo
This network shows the impact of papers produced by Juan L. Mateo. 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 Juan L. Mateo. The network helps show where Juan L. Mateo may publish in the future.
Co-authorship network of co-authors of Juan L. Mateo
This figure shows the co-authorship network connecting the top 25 collaborators of Juan L. Mateo. A scholar is included among the top collaborators of Juan L. Mateo 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 Juan L. Mateo. Juan L. Mateo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 20 | |
| 5 | 13 | |
| 6 | 3 | |
| 7 | 24 | |
| 8 | 16 | |
| 9 | Refined sgRNA efficacy prediction improves large- and small-scale CRISPR–Cas9 applicationsbreakdown → | 196 |
| 10 | 21 | |
| 11 | 33 | |
| 12 | 19 | |
| 13 | CCTop: An Intuitive, Flexible and Reliable CRISPR/Cas9 Target Prediction Toolbreakdown → | 709 |
| 14 | 47 | |
| 15 | 160 | |
| 16 | 2 | |
| 17 | 7 | |
| 18 | 111 | |
| 19 | Comparative Evaluation of PL languages. | 2 |
| 20 | Dependency networks based classifiers: learning models by using independence. | 4 |
About Juan L. Mateo
Juan L. Mateo is a scholar working on Developmental Neuroscience, Physiology and Artificial Intelligence, having authored 29 papers that have together received 1.6k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (6 papers), Genomics and Chromatin Dynamics (4 papers) and RNA Research and Splicing (4 papers). The work is most often cited by research in Aging (61 citations), Business and International Management (61 citations) and Developmental Neuroscience (102 citations). Juan L. Mateo has collaborated with scholars based in Spain, Germany and United States. Frequent co-authors include Joachim Wittbrodt, Thomas Thumberger, Manuel Stemmer, José A. Gámez, José M. Puerta, Antonio Fernández‐Caballero, Adrian Schwarzer, Maurice Labuhn, Dirk Heckl and Axel Schambach. Their work appears in journals such as Nucleic Acids Research, Genes & Development and PLoS ONE.
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.