Manuel Belmadani

566 total citations
8 papers, 270 citations indexed

About

Manuel Belmadani is a scholar working on Molecular Biology, Cognitive Neuroscience and Genetics. According to data from OpenAlex, Manuel Belmadani has authored 8 papers receiving a total of 270 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 2 papers in Cognitive Neuroscience and 2 papers in Genetics. Recurrent topics in Manuel Belmadani's work include CRISPR and Genetic Engineering (3 papers), Single-cell and spatial transcriptomics (2 papers) and Bioinformatics and Genomic Networks (2 papers). Manuel Belmadani is often cited by papers focused on CRISPR and Genetic Engineering (3 papers), Single-cell and spatial transcriptomics (2 papers) and Bioinformatics and Genomic Networks (2 papers). Manuel Belmadani collaborates with scholars based in Canada, Switzerland and United States. Manuel Belmadani's co-authors include Walter Keller, Georges Martín, Andreas Gruber, Andreas Gruber, Ralf Schmidt, Souvik Ghosh, Mihaela Zavolan, Paul Pavlidis, B. Ogan Mancarci and Matthew Jacobson and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Genome Research and Human Mutation.

In The Last Decade

Manuel Belmadani

8 papers receiving 269 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Manuel Belmadani Canada	5	220	40	35	30	23	8	270
Jasmin S. Revanna United States	6	222 1.0×	41 1.0×	17 0.5×	55 1.8×	16 0.7×	7	267
Samir Vaid Germany	10	243 1.1×	54 1.4×	14 0.4×	44 1.5×	28 1.2×	13	341
Ben Tsuda United States	4	191 0.9×	58 1.4×	42 1.2×	42 1.4×	58 2.5×	5	293
Alberto Parras Switzerland	8	157 0.7×	17 0.4×	31 0.9×	54 1.8×	14 0.6×	10	239
Linda Su-Feher United States	5	150 0.7×	17 0.4×	22 0.6×	47 1.6×	6 0.3×	9	214
Silvia Benito-Kwiecinski United Kingdom	6	262 1.2×	22 0.6×	21 0.6×	38 1.3×	15 0.7×	8	367
Marty G. Yang United States	7	383 1.7×	27 0.7×	40 1.1×	141 4.7×	18 0.8×	10	450
Giulio Deangeli Italy	2	155 0.7×	12 0.3×	36 1.0×	43 1.4×	11 0.5×	2	240
María T. Lopez-Cascales Spain	6	198 0.9×	19 0.5×	21 0.6×	77 2.6×	6 0.3×	8	272
Catherine Jaeger France	7	153 0.7×	16 0.4×	15 0.4×	14 0.5×	8 0.3×	10	264

Countries citing papers authored by Manuel Belmadani

Since Specialization

Citations

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

Fields of papers citing papers by Manuel Belmadani

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manuel Belmadani

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

All Works

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8 of 8 papers shown

1.

Lim, Nathaniel C., Manuel Belmadani, B. Ogan Mancarci, et al.. (2021). Curation of over 10 000 transcriptomic studies to enable data reuse. Database. 2021. 22 indexed citations

2.

McDiarmid, Troy A., Manuel Belmadani, Eleanor Mathews, et al.. (2019). Systematic phenomics analysis of autism-associated genes reveals parallel networks underlying reversible impairments in habituation. Proceedings of the National Academy of Sciences. 117(1). 656–667. 43 indexed citations

3.

Belmadani, Manuel, et al.. (2019). VariCarta: A Comprehensive Database of Harmonized Genomic Variants Found in Autism Spectrum Disorder Sequencing Studies. Autism Research. 12(12). 1728–1736. 21 indexed citations

4.

Tripathy, Shreejoy J., Lilah Toker, Claire Bomkamp, et al.. (2018). Assessing Transcriptome Quality in Patch-Seq Datasets. Frontiers in Molecular Neuroscience. 11. 363–363. 21 indexed citations

5.

Rogić, Sanja, Cameron J. McDonald, Matthew Jacobson, et al.. (2016). Interactive Exploration, Analysis, and Visualization of Complex Phenome-Genome Datasets with ASPIREdb. Human Mutation. 37(8). 719–726. 1 indexed citations

6.

Gruber, Andreas, Ralf Schmidt, Andreas Gruber, et al.. (2016). A comprehensive analysis of 3′ end sequencing data sets reveals novel polyadenylation signals and the repressive role of heterogeneous ribonucleoprotein C on cleavage and polyadenylation. Genome Research. 26(8). 1145–1159. 160 indexed citations

7.

Belmadani, Manuel & Marcel Turcotte. (2016). MotifGP: Using multi-objective evolutionary computing for mining network expressions in DNA sequences. 13. 1–6. 1 indexed citations

8.

Belmadani, Manuel. (2013). Optimizations in Bioinformatics using GPU Processing on Binary Data. 1 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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