Óscar Dias

1.3k total citations
52 papers, 761 citations indexed

About

Óscar Dias is a scholar working on Molecular Biology, Biomedical Engineering and Ecology. According to data from OpenAlex, Óscar Dias has authored 52 papers receiving a total of 761 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 20 papers in Biomedical Engineering and 6 papers in Ecology. Recurrent topics in Óscar Dias's work include Microbial Metabolic Engineering and Bioproduction (26 papers), Biofuel production and bioconversion (19 papers) and Enzyme Catalysis and Immobilization (6 papers). Óscar Dias is often cited by papers focused on Microbial Metabolic Engineering and Bioproduction (26 papers), Biofuel production and bioconversion (19 papers) and Enzyme Catalysis and Immobilization (6 papers). Óscar Dias collaborates with scholars based in Portugal, Brazil and United States. Óscar Dias's co-authors include Eugénio C. Ferreira, Isabel Rocha, Miguel Rocha, D. P. Mesquita, A. L. Amaral, Hugo Oliveira, Joana Azeredo, Andreas Gombert, Pedro Geada and António A. Vicente and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Óscar Dias

50 papers receiving 736 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Óscar Dias Portugal 17 432 224 139 73 68 52 761
Yübo Wang United States 15 155 0.4× 86 0.4× 110 0.8× 44 0.6× 364 5.4× 46 909
Stuart M. Stocks Denmark 18 564 1.3× 424 1.9× 61 0.4× 48 0.7× 34 0.5× 32 1.1k
Bhaskar Bhadra India 21 494 1.1× 203 0.9× 166 1.2× 33 0.5× 43 0.6× 37 1.0k
Embalil Mathachan Aneesh India 15 147 0.3× 140 0.6× 22 0.2× 35 0.5× 44 0.6× 48 653
Andreas Bremges Germany 15 571 1.3× 160 0.7× 259 1.9× 17 0.2× 93 1.4× 23 873
Muhammad Nawaz Pakistan 15 99 0.2× 242 1.1× 33 0.2× 19 0.3× 85 1.3× 56 786
Alberto Garre Spain 16 64 0.1× 60 0.3× 40 0.3× 37 0.5× 31 0.5× 63 793
Manju Lata India 11 173 0.4× 66 0.3× 41 0.3× 29 0.4× 28 0.4× 29 575

Countries citing papers authored by Óscar Dias

Since Specialization
Citations

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

Fields of papers citing papers by Óscar Dias

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Óscar Dias

This figure shows the co-authorship network connecting the top 25 collaborators of Óscar Dias. A scholar is included among the top collaborators of Óscar Dias 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 Óscar Dias. Óscar Dias 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.
Viana, Romeu, et al.. (2025). Unveiling new features of the human pathogen Cryptococcus neoformans through the reconstruction and exploitation of a dedicated genome-scale metabolic model. Computational and Structural Biotechnology Journal. 27. 2336–2346. 1 indexed citations
2.
Ridder, Dick de, et al.. (2025). Predicting precursors of plant specialized metabolites using DeepMol automated machine learning. Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics. 22(2). 1 indexed citations
3.
Rocha, Miguel, et al.. (2024). A multi-omics approach for understanding grape metabolism throughout development. IFAC-PapersOnLine. 58(23). 43–48. 1 indexed citations
4.
Vicente, António A., et al.. (2024). Development of highly effective growth strategies aiming at improving the content of carotenoids in Dunaliella salina IFDSAL-JY215. Sustainable Food Technology. 2(6). 1735–1746.
5.
Rodrigues, Joana L., et al.. (2024). Optimization of chondroitin production in E. coli using genome scale models. Molecular Systems Design & Engineering. 9(6). 597–611. 4 indexed citations
6.
Sousa, Vítor C., et al.. (2024). Towards a genome-scale metabolic model of Dunaliella salina. IFAC-PapersOnLine. 58(23). 37–42. 1 indexed citations
7.
Rocha, Miguel, et al.. (2024). A diel multi-tissue genome-scale metabolic model of Vitis vinifera. PLoS Computational Biology. 20(10). e1012506–e1012506. 3 indexed citations
8.
Chaves, Inês, et al.. (2023). The first multi-tissue genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways in Quercus suber. PLoS Computational Biology. 19(9). e1011499–e1011499. 10 indexed citations
9.
10.
Faria, José P., et al.. (2023). TranSyT, an innovative framework for identifying transport systems. Bioinformatics. 39(8). 7 indexed citations
11.
Barbosa, Ana, et al.. (2022). merlin , an improved framework for the reconstruction of high-quality genome-scale metabolic models. Nucleic Acids Research. 50(11). 6052–6066. 27 indexed citations
12.
Oliveira, Ricardo, Eva Pinho, Ana Luísa Sousa, et al.. (2022). Modelling aptamers with nucleic acid mimics (NAM): From sequence to three-dimensional docking. PLoS ONE. 17(3). e0264701–e0264701. 23 indexed citations
13.
Saraiva, J., Alexander Bartholomäus, René Kallies, et al.. (2021). OrtSuite: from genomes to prediction of microbial interactions within targeted ecosystem processes. Life Science Alliance. 4(12). e202101167–e202101167. 2 indexed citations
14.
Fernandes, Bruna Soares, Óscar Dias, Gisela Lara da Costa, et al.. (2019). Genome-wide sequencing and metabolic annotation of Pythium irregulare CBS 494.86: understanding Eicosapentaenoic acid production. BMC Biotechnology. 19(1). 9 indexed citations
15.
Dias, Óscar, et al.. (2019). iDS372, a Phenotypically Reconciled Model for the Metabolism of Streptococcus pneumoniae Strain R6. Frontiers in Microbiology. 10. 1283–1283. 13 indexed citations
16.
Dias, Óscar, Thiago Olitta Basso, Isabel Rocha, Eugénio C. Ferreira, & Andreas Gombert. (2017). Quantitative physiology and elemental composition of Kluyveromyces lactis CBS 2359 during growth on glucose at different specific growth rates. Antonie van Leeuwenhoek. 111(2). 183–195. 3 indexed citations
17.
Dias, Óscar, Rui Pereira, Andreas Gombert, Eugénio C. Ferreira, & Isabel Rocha. (2014). iOD907, the first genome‐scale metabolic model for the milk yeast Kluyveromyces lactis. Biotechnology Journal. 9(6). 776–790. 39 indexed citations
18.
Dias, Óscar, Andreas Gombert, Eugénio C. Ferreira, & Isabel Rocha. (2012). Genome-wide metabolic (re-) annotation of Kluyveromyces lactis. BMC Genomics. 13(1). 517–517. 11 indexed citations
19.
Mesquita, D. P., et al.. (2010). Dilution and Magnification Effects on Image Analysis Applications in Activated Sludge Characterization. Microscopy and Microanalysis. 16(5). 561–568. 15 indexed citations
20.
Mesquita, D. P., Óscar Dias, Ana M.A. Dias, A. L. Amaral, & Eugénio C. Ferreira. (2009). Correlation between sludge settling ability and image analysis information using partial least squares. Analytica Chimica Acta. 642(1-2). 94–101. 44 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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