Marc Da Costa

508 total citations
9 papers, 293 citations indexed

About

Marc Da Costa is a scholar working on Molecular Biology, Cell Biology and Genetics. According to data from OpenAlex, Marc Da Costa has authored 9 papers receiving a total of 293 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 5 papers in Cell Biology and 2 papers in Genetics. Recurrent topics in Marc Da Costa's work include Zebrafish Biomedical Research Applications (5 papers), Neurogenetic and Muscular Disorders Research (2 papers) and Amyotrophic Lateral Sclerosis Research (2 papers). Marc Da Costa is often cited by papers focused on Zebrafish Biomedical Research Applications (5 papers), Neurogenetic and Muscular Disorders Research (2 papers) and Amyotrophic Lateral Sclerosis Research (2 papers). Marc Da Costa collaborates with scholars based in United Kingdom, United States and Finland. Marc Da Costa's co-authors include Pamela J. Shaw, Oliver Bandmann, Gerald Goodall, Mbombe Kazoka, Rachel Waller, Johnathan Cooper‐Knock, Janine Kirby, Esther Hobson, Paul R. Heath and Marta Milo and has published in prestigious journals such as Arteriosclerosis Thrombosis and Vascular Biology, Journal of Neurology Neurosurgery & Psychiatry and European Journal of Neuroscience.

In The Last Decade

Marc Da Costa

9 papers receiving 286 citations

Peers

Marc Da Costa

MB

NEURO

GENET

CB

CR

W Ambrose McGee United States

Mehri Moradi Germany

Sivakumar Boopathy United States

Barbara E. Stopschinski Germany

Ching-Ann Liu Taiwan

Sukhleen Kour United States

Alexander McGown United Kingdom

Lina Dahl Sweden

Razina Aziz‐Bose United States

Emily A. Scarborough United States

W Ambrose McGee United States

Marc Da Costa

159 ×1.0

MB

108 ×0.9

NEURO

47 ×0.6

GENET

27 ×0.6

CB

44 ×1.2

CR

Citations per year, relative to Marc Da Costa Marc Da Costa (= 1×) peers W Ambrose McGee

Countries citing papers authored by Marc Da Costa

Since Specialization

Citations

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

Fields of papers citing papers by Marc Da Costa

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marc Da Costa. 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 Marc Da Costa. The network helps show where Marc Da Costa may publish in the future.

Co-authorship network of co-authors of Marc Da Costa

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

All Works

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9 of 9 papers shown

1.

Waller, Rachel, Gerald Goodall, Marta Milo, et al.. (2017). Serum miRNAs miR-206, 143-3p and 374b-5p as potential biomarkers for amyotrophic lateral sclerosis (ALS). Neurobiology of Aging. 55. 123–131. 111 indexed citations

2.

Soman, Smijin, Marcus Keatinge, Marc Da Costa, et al.. (2016). Inhibition of the mitochondrial calcium uniporter rescues dopaminergic neurons in pink1^−/− zebrafish. European Journal of Neuroscience. 45(4). 528–535. 75 indexed citations

3.

Payne, Thomas, Marcus Keatinge, Marc Da Costa, & Oliver Bandmann. (2015). MPP+ IN A ZEBRAFISH MODEL OF GLUCOCEREBROSIDASE 1 DEFICIENCY. Journal of Neurology Neurosurgery & Psychiatry. 86(11). e4.93–e4. 1 indexed citations

4.

Dong, Bo, Ling Shao, Marc Da Costa, Oliver Bandmann, & Alejandro F. Frangi. (2015). Deep learning for automatic cell detection in wide-field microscopy zebrafish images. Research Explorer (The University of Manchester). 772–776. 35 indexed citations

5.

Keatinge, Marcus, Marc Da Costa, Aswin Menke, et al.. (2015). ZEBRAFISH AS A MODEL OF GLUCOCEREBROSIDASE 1 (GBA1) DEFICIENCY. Journal of Neurology Neurosurgery & Psychiatry. 86(11). e4.46–e4. 1 indexed citations

6.

Dong, Bo, Ling Shao, Alejandro F. Frangi, Oliver Bandmann, & Marc Da Costa. (2014). Three-Dimensional Deconvolution of Wide Field Microscopy with Sparse Priors: Application to Zebrafish Imagery. Research Explorer (The University of Manchester). 41. 865–870. 3 indexed citations

7.

Gray, Caroline, D Bratt, Marc Da Costa, et al.. (2013). Loss of Function of Parathyroid Hormone Receptor 1 Induces Notch-Dependent Aortic Defects During Zebrafish Vascular Development. Arteriosclerosis Thrombosis and Vascular Biology. 33(6). 1257–1263. 11 indexed citations

8.

Costa, Marc Da, Claire Allen, Adrian Higginbottom, et al.. (2013). A new zebrafish model produced by TILLING of SOD1-related amyotrophic lateral sclerosis replicates key features of the disease and represents a tool for in vivo therapeutic screening. Disease Models & Mechanisms. 7(1). 73–81. 51 indexed citations

9.

Novodvorský, Peter, Marc Da Costa, & Tim Chico. (2012). Zebrafish-based small molecule screens for novel cardiovascular drugs. Drug Discovery Today Technologies. 10(1). e109–e114. 5 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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