Stefan Maetschke

1.2k total citations
28 papers, 756 citations indexed

About

Stefan Maetschke is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Genetics. According to data from OpenAlex, Stefan Maetschke has authored 28 papers receiving a total of 756 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 6 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Genetics. Recurrent topics in Stefan Maetschke's work include RNA and protein synthesis mechanisms (7 papers), Machine Learning in Bioinformatics (6 papers) and Bioinformatics and Genomic Networks (6 papers). Stefan Maetschke is often cited by papers focused on RNA and protein synthesis mechanisms (7 papers), Machine Learning in Bioinformatics (6 papers) and Bioinformatics and Genomic Networks (6 papers). Stefan Maetschke collaborates with scholars based in Australia, United States and Denmark. Stefan Maetschke's co-authors include Mark A. Ragan, Melissa J. Davis, Piyush B. Madhamshettiwar, Rahil Garnavi, Bhavna Antony, Joel S. Schuman, Gadi Wollstein, Hiroshi Ishikawa, Zheng Yuan and Antônio Reverter and has published in prestigious journals such as Bioinformatics, PLoS ONE and BMC Bioinformatics.

In The Last Decade

Stefan Maetschke

26 papers receiving 738 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Stefan Maetschke Australia 14 493 188 170 63 63 28 756
Jingjing Chen China 9 146 0.3× 96 0.5× 73 0.4× 24 0.4× 5 0.1× 29 328
Edward Kien Yee Yapp Singapore 9 363 0.7× 69 0.4× 4 0.0× 81 1.3× 50 0.8× 10 585
Zihan Guo China 12 283 0.6× 29 0.2× 6 0.0× 132 2.1× 114 1.8× 39 549
Amrita Amrita India 9 71 0.1× 49 0.3× 90 0.5× 66 1.0× 4 0.1× 56 327
Robin Coope Canada 13 328 0.7× 88 0.5× 4 0.0× 62 1.0× 8 0.1× 32 591
Daniele Raimondi Belgium 16 604 1.2× 37 0.2× 5 0.0× 29 0.5× 69 1.1× 41 802
Sebastian Meier‐Ewert Germany 16 823 1.7× 57 0.3× 5 0.0× 53 0.8× 18 0.3× 28 996
Chenyu Zhu China 14 644 1.3× 46 0.2× 4 0.0× 45 0.7× 13 0.2× 36 865
Dinanath Sulakhe United States 14 215 0.4× 36 0.2× 56 0.3× 32 0.5× 4 0.1× 37 442

Countries citing papers authored by Stefan Maetschke

Since Specialization
Citations

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

Fields of papers citing papers by Stefan Maetschke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stefan Maetschke

This figure shows the co-authorship network connecting the top 25 collaborators of Stefan Maetschke. A scholar is included among the top collaborators of Stefan Maetschke 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 Stefan Maetschke. Stefan Maetschke 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.
Yu, Hsin‐Hao, Stefan Maetschke, Bhavna Antony, et al.. (2020). Estimating Global Visual Field Indices in Glaucoma by Combining Macula and Optic Disc OCT Scans Using 3-Dimensional Convolutional Neural Networks. Ophthalmology Glaucoma. 4(1). 102–112. 33 indexed citations
2.
Maetschke, Stefan, et al.. (2019). Inference of visual field test results from OCT volumes using deep learning.. Investigative Ophthalmology & Visual Science. 60(9). 1487–1487. 2 indexed citations
3.
Maetschke, Stefan, Bhavna Antony, Hiroshi Ishikawa, et al.. (2019). A feature agnostic approach for glaucoma detection in OCT volumes. PLoS ONE. 14(7). e0219126–e0219126. 134 indexed citations
4.
Antony, Bhavna, Stefan Maetschke, & Rahil Garnavi. (2019). Automated summarisation of SDOCT volumes using deep learning: Transfer learning vs de novo trained networks. PLoS ONE. 14(5). e0203726–e0203726. 4 indexed citations
5.
Yu, Hsin‐Hao, Stefan Maetschke, Bhavna Antony, et al.. (2019). Estimating visual field functions in glaucoma patients using multi-regional neural networks on OCT images. 60(9). 1462–1462. 1 indexed citations
6.
Roy, Pallab, Ruwan Tennakoon, Suman Sedai, et al.. (2017). A novel hybrid approach for severity assessment of Diabetic Retinopathy in colour fundus images. 1078–1082. 25 indexed citations
7.
Maetschke, Stefan & Mark A. Ragan. (2014). Characterizing cancer subtypes as attractors of Hopfield networks. Bioinformatics. 30(9). 1273–1279. 21 indexed citations
8.
Madhamshettiwar, Piyush B., Stefan Maetschke, Melissa J. Davis, Antônio Reverter, & Mark A. Ragan. (2014). INsPeCT: INtegrative Platform for Cancer Transcriptomics. Cancer Informatics. 13. CIN.S13630–CIN.S13630. 4 indexed citations
9.
Maetschke, Stefan, Piyush B. Madhamshettiwar, Melissa J. Davis, & Mark A. Ragan. (2013). Supervised, semi-supervised and unsupervised inference of gene regulatory networks. Briefings in Bioinformatics. 15(2). 195–211. 106 indexed citations
10.
Wang, Chenwei, et al.. (2012). mCOPA: analysis of heterogeneous features in cancer expression data. QUT ePrints (Queensland University of Technology). 6 indexed citations
11.
Madhamshettiwar, Piyush B., Stefan Maetschke, Melissa J. Davis, Antônio Reverter, & Mark A. Ragan. (2012). Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets. Genome Medicine. 4(5). 41–41. 112 indexed citations
12.
Wang, Chenwei, et al.. (2012). mCOPA: analysis of heterogeneous features in cancer expression data. PubMed. 2(1). 22–22. 18 indexed citations
13.
Maetschke, Stefan, et al.. (2010). A visual framework for sequence analysis using n-grams and spectral rearrangement. Bioinformatics. 26(6). 737–744. 13 indexed citations
14.
Maetschke, Stefan & Zheng Yuan. (2009). Exploiting structural and topological information to improve prediction of RNA-protein binding sites. BMC Bioinformatics. 10(1). 341–341. 47 indexed citations
15.
Buske, Fabian A., Stefan Maetschke, & Mikael Bodén. (2008). It's about time: Signal recognition in staged models of protein translocation. Pattern Recognition. 42(4). 567–574. 1 indexed citations
16.
Hawkins, John, et al.. (2007). Identifying novel peroxisomal proteins. Proteins Structure Function and Bioinformatics. 69(3). 606–616. 21 indexed citations
17.
Maetschke, Stefan, et al.. (2007). Genome-wide analysis of chlamydiae for promoters that phylogenetically footprint. Research in Microbiology. 158(8-9). 685–693. 12 indexed citations
18.
Maetschke, Stefan, Michael Towsey, & James M. Hogan. (2007). BioPatML - an XML description language for patterns in biological sequences. QUT ePrints (Queensland University of Technology). 1 indexed citations
19.
Maetschke, Stefan, Michael Towsey, & James M. Hogan. (2006). Bacterial promoter modelling and prediction for E. coli and B. subtilis with Beagle. QUT ePrints (Queensland University of Technology).
20.
Maetschke, Stefan, Michael Towsey, & James M. Hogan. (2006). Bacterial promoter modeling and prediction for E. coli and B. subtilis with Beagle. 9–13. 3 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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