Andrew Secker

1.4k total citations
29 papers, 937 citations indexed

About

Andrew Secker is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Andrew Secker has authored 29 papers receiving a total of 937 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 7 papers in Computer Vision and Pattern Recognition and 5 papers in Computer Networks and Communications. Recurrent topics in Andrew Secker's work include Machine Learning in Bioinformatics (10 papers), Image and Signal Denoising Methods (6 papers) and Advanced Data Compression Techniques (6 papers). Andrew Secker is often cited by papers focused on Machine Learning in Bioinformatics (10 papers), Image and Signal Denoising Methods (6 papers) and Advanced Data Compression Techniques (6 papers). Andrew Secker collaborates with scholars based in United Kingdom, Australia and Denmark. Andrew Secker's co-authors include David Taubman, Alex A. Freitas, Jon Timmis, Darren R. Flower, Matthew N. Davies, Nishanth Sastry, Edward Clark, Dmytro Karamshuk, Matthew N Davies and David E. Gloriam and has published in prestigious journals such as Bioinformatics, IEEE Transactions on Image Processing and IEEE Journal on Selected Areas in Communications.

In The Last Decade

Andrew Secker

29 papers receiving 884 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Andrew Secker United Kingdom 15 470 315 253 110 100 29 937
Clement H. C. Leung Australia 13 203 0.4× 111 0.4× 44 0.2× 110 1.0× 207 2.1× 80 799
Aftab Ahmed Pakistan 11 142 0.3× 102 0.3× 161 0.6× 117 1.1× 92 0.9× 25 509
Weiguo Zheng China 15 281 0.6× 172 0.5× 117 0.5× 481 4.4× 90 0.9× 61 784
Jun Lang China 16 536 1.1× 66 0.2× 70 0.3× 142 1.3× 26 0.3× 53 783
Qingyun Shi China 13 573 1.2× 320 1.0× 57 0.2× 88 0.8× 101 1.0× 35 905
Sairam Subramanian United States 11 85 0.2× 169 0.5× 356 1.4× 156 1.4× 284 2.8× 16 975
K.-P. Vo United States 6 230 0.5× 73 0.2× 121 0.5× 140 1.3× 118 1.2× 9 601
Boris Škorić Netherlands 18 274 0.6× 175 0.6× 32 0.1× 390 3.5× 129 1.3× 64 1.1k
David Auber France 15 634 1.3× 172 0.5× 141 0.6× 166 1.5× 59 0.6× 42 857
Xuejia Lai China 14 401 0.9× 114 0.4× 206 0.8× 548 5.0× 147 1.5× 82 894

Countries citing papers authored by Andrew Secker

Since Specialization
Citations

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

Fields of papers citing papers by Andrew Secker

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andrew Secker

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew Secker. A scholar is included among the top collaborators of Andrew Secker 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 Andrew Secker. Andrew Secker 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.
Sánchez-Martínez, Felipe, Víctor M. Sánchez-Cartagena, Juan Antonio Pérez-Ortiz, et al.. (2020). An English-Swahili parallel corpus and its use for neural machine translation in the news domain. Zenodo (CERN European Organization for Nuclear Research). 299–308. 5 indexed citations
2.
Birch, Alexandra, Barry Haddow, Antonio Valerio Miceli Barone, et al.. (2019). Global Under-Resourced Media Translation (GoURMET). Edinburgh Research Explorer. 122–122. 3 indexed citations
3.
Vlachos, Andreas, et al.. (2019). Automated Fact Checking in the News Room. Bristol Research (University of Bristol). 3579–3583. 18 indexed citations
4.
Raman, Aravindh, et al.. (2018). Consume Local: Towards Carbon Free Content Delivery. Research Portal (King's College London). 9. 994–1003. 4 indexed citations
5.
Raman, Aravindh, et al.. (2018). Care to Share?. 27–31. 3 indexed citations
6.
Raman, Aravindh, et al.. (2017). Wi-Stitch. Zenodo (CERN European Organization for Nuclear Research). 13–18. 13 indexed citations
7.
Karamshuk, Dmytro, et al.. (2016). Take-Away TV: Recharging Work Commutes With Predictive Preloading of Catch-Up TV Content. IEEE Journal on Selected Areas in Communications. 34(8). 2091–2101. 26 indexed citations
8.
Davies, Matthew N., David E. Gloriam, Andrew Secker, et al.. (2011). Present Perspectives on the Automated Classification of the G-Protein Coupled Receptors (GPCRs) at the Protein Sequence Level. Current Topics in Medicinal Chemistry. 11(15). 1994–2009. 6 indexed citations
9.
Secker, Andrew, et al.. (2010). Hierarchical classification of G-Protein-Coupled Receptors with data-driven selection of attributes and classifiers. International Journal of Data Mining and Bioinformatics. 4(2). 191–191. 24 indexed citations
10.
MacFarlane, Andrew, et al.. (2010). An experimental comparison of a genetic algorithm and a hill‐climber for term selection. Journal of Documentation. 66(4). 513–531. 5 indexed citations
11.
Davies, Matthew N., Andrew Secker, Mark Halling‐Brown, et al.. (2008). GPCRTree: online hierarchical classification of GPCR function. BMC Research Notes. 1(1). 67–67. 28 indexed citations
12.
Davies, Matthew, Andrew Secker, Alex A. Freitas, et al.. (2008). Alignment-Independent Techniques for Protein Classification. Current Proteomics. 5(4). 217–223. 11 indexed citations
13.
Davies, Matthew N., David E. Gloriam, Andrew Secker, et al.. (2007). Proteomic applications of automated GPCR classification. PROTEOMICS. 7(16). 2800–2814. 35 indexed citations
14.
Davies, Matthew N., et al.. (2007). On the hierarchical classification of G protein-coupled receptors. Bioinformatics. 23(23). 3113–3118. 69 indexed citations
15.
Secker, Andrew & David Taubman. (2004). Highly scalable video compression with scalable motion coding. IEEE Transactions on Image Processing. 13(8). 1029–1041. 69 indexed citations
16.
Taubman, David & Andrew Secker. (2004). Highly scalable video compression with scalable motion coding. 2. III–273. 30 indexed citations
17.
Secker, Andrew, Alex A. Freitas, & Jon Timmis. (2003). A Danger Theory Approach to Web Mining. arXiv (Cornell University). 7 indexed citations
18.
Secker, Andrew, Alex A. Freitas, & Jon Timmis. (2003). AISEC: an artificial immune system for e-mail classification. Kent Academic Repository (University of Kent). 131–138 Vol.1. 57 indexed citations
19.
Secker, Andrew & David Taubman. (2003). Lifting-based invertible motion adaptive transform (LIMAT) framework for highly scalable video compression. IEEE Transactions on Image Processing. 12(12). 1530–1542. 173 indexed citations
20.
Taubman, David, Raymond Leung, & Andrew Secker. (2003). Scalable compression of volumetric images. Proceedings - International Conference on Image Processing. 2. II–249. 4 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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