Terence A. Etchells

514 total citations
23 papers, 325 citations indexed

About

Terence A. Etchells is a scholar working on Artificial Intelligence, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Terence A. Etchells has authored 23 papers receiving a total of 325 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 7 papers in Molecular Biology and 4 papers in Computational Theory and Mathematics. Recurrent topics in Terence A. Etchells's work include Statistical Methods and Inference (4 papers), Neural Networks and Applications (4 papers) and Bayesian Modeling and Causal Inference (3 papers). Terence A. Etchells is often cited by papers focused on Statistical Methods and Inference (4 papers), Neural Networks and Applications (4 papers) and Bayesian Modeling and Causal Inference (3 papers). Terence A. Etchells collaborates with scholars based in United Kingdom, Italy and France. Terence A. Etchells's co-authors include Paulo Lisböa, Ian H. Jarman, Federico Ambrogi, Elia Biganzoli, Patrizia Boracchi, Davide Bacciu, Thorsteinn Rögnvaldsson, Daniel Garwicz, Min Hane Aung and Jonathan M. Garibaldi and has published in prestigious journals such as BMC Bioinformatics, Neurocomputing and Neural Networks.

In The Last Decade

Terence A. Etchells

22 papers receiving 309 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Terence A. Etchells 139 68 32 25 23 23 325
C. Gunavathi 110 0.8× 93 1.4× 14 0.4× 38 1.5× 16 0.7× 29 282
Chao Jiang 132 0.9× 61 0.9× 6 0.2× 20 0.8× 10 0.4× 35 359
Yazeed Zoabi 147 1.1× 77 1.1× 21 0.7× 12 0.5× 57 2.5× 11 425
Rui Almeida 89 0.6× 30 0.4× 15 0.5× 22 0.9× 9 0.4× 57 343
Mohammed Alawad 342 2.5× 112 1.6× 18 0.6× 20 0.8× 32 1.4× 49 564
Oleg Sysoev 71 0.5× 72 1.1× 95 3.0× 22 0.9× 13 0.6× 34 477
Serkan Savaş 108 0.8× 19 0.3× 29 0.9× 35 1.4× 32 1.4× 37 372
Seyed Abolghasem Mirroshandel 200 1.4× 19 0.3× 34 1.1× 39 1.6× 26 1.1× 41 568
Nicola Paoletti 77 0.6× 66 1.0× 42 1.3× 42 1.7× 6 0.3× 36 312

Countries citing papers authored by Terence A. Etchells

Since Specialization
Citations

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

Fields of papers citing papers by Terence A. Etchells

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Terence A. Etchells. 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 Terence A. Etchells. The network helps show where Terence A. Etchells may publish in the future.

Co-authorship network of co-authors of Terence A. Etchells

This figure shows the co-authorship network connecting the top 25 collaborators of Terence A. Etchells. A scholar is included among the top collaborators of Terence A. Etchells 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 Terence A. Etchells. Terence A. Etchells 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.
Taylor, Mark, et al.. (2024). A chaos theory view of accidental dwelling fire injuries. Fire and Materials. 48(7). 715–724.
2.
Nedergaard, Niels Jensby, Jasper Verheul, Barry Drust, et al.. (2018). The feasibility of predicting ground reaction forces during running from a trunk accelerometry driven mass-spring-damper model. PeerJ. 6. e6105–e6105. 26 indexed citations
3.
Etchells, Terence A., et al.. (2013). Finding reproducible cluster partitions for the k-means algorithm. BMC Bioinformatics. 14(S1). S8–S8. 31 indexed citations
4.
Jarman, Ian H., et al.. (2013). Inference of number of prototypes with a framework approach to K-means clustering. International Journal of Biomedical Engineering and Technology. 13(4). 323–323. 7 indexed citations
5.
Bacciu, Davide, Terence A. Etchells, Paulo Lisböa, & Joe Whittaker. (2012). Efficient identification of independence networks using mutual information. Computational Statistics. 28(2). 621–646. 13 indexed citations
6.
Soria, Daniele, Jonathan M. Garibaldi, Federico Ambrogi, et al.. (2010). A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients. Computers in Biology and Medicine. 40(3). 318–330. 45 indexed citations
7.
Rögnvaldsson, Thorsteinn, et al.. (2009). How to find simple and accurate rules for viral protease cleavage specificities. BMC Bioinformatics. 10(1). 149–149. 27 indexed citations
8.
Lisböa, Paulo, Terence A. Etchells, Ian H. Jarman, et al.. (2009). Partial Logistic Artificial Neural Network for Competing Risks Regularized With Automatic Relevance Determination. IEEE Transactions on Neural Networks. 20(9). 1403–1416. 31 indexed citations
9.
Bacciu, Davide, Ian H. Jarman, Terence A. Etchells, & Paulo Lisböa. (2009). Patient stratification with competing risks by multivariate Fisher distance. CINECA IRIS Institutial research information system (University of Pisa). 12. 213–220. 7 indexed citations
10.
Bacciu, Davide, et al.. (2009). p-Health in Breast Oncology: A Framework for Predictive and Participatory e-Systems. CINECA IRIS Institutial research information system (University of Pisa). 91. 123–129. 1 indexed citations
11.
Jarman, Ian H., Terence A. Etchells, José D. Martín‐Guerrero, & Paulo Lisböa. (2008). An integrated framework for risk profiling of breast cancer patients following surgery. Artificial Intelligence in Medicine. 42(3). 165–188. 11 indexed citations
12.
Lisböa, Paulo, Terence A. Etchells, Ian H. Jarman, et al.. (2008). Time-to-event analysis with artificial neural networks: An integrated analytical and rule-based study for breast cancer. Neural Networks. 21(2-3). 414–426. 14 indexed citations
13.
Fernandes, Ana S., Ian H. Jarman, Terence A. Etchells, et al.. (2008). Missing Data Imputation in Longitudinal Cohort Studies: Application of PLANN-ARD in Breast Cancer Survival. 644–649. 3 indexed citations
14.
Etchells, Terence A., Ian H. Jarman, Min Hane Aung, et al.. (2007). Development of a Rule Based Prognostic Tool for HER 2 Positive Breast Cancer Patients. Conference proceedings. 2007. 5416–5419. 2 indexed citations
15.
Garibaldi, Jonathan M., Daniele Soria, Federico Ambrogi, et al.. (2007). O-59 Identification of sub-classes of breast cancer through consensus derived from automated clustering methods. European Journal of Cancer Supplements. 5(3). 18–18. 1 indexed citations
16.
Lisböa, Paulo, Elia Biganzoli, Azzam Taktak, et al.. (2007). Assessing flexible models and rule extraction from censored survival data. IEEE International Conference on Neural Networks. 1663–1668. 1 indexed citations
17.
Etchells, Terence A., Àngela Nebot, Alfredo Vellido, Paulo Lisböa, & Francisco Mugica. (2006). Learning what is important: feature selection and rule extraction in a virtual course. The European Symposium on Artificial Neural Networks. 401–406. 20 indexed citations
18.
Etchells, Terence A. & Paulo Lisböa. (2006). Orthogonal Search-Based Rule Extraction (OSRE) for Trained Neural Networks: A Practical and Efficient Approach. IEEE Transactions on Neural Networks. 17(2). 374–384. 61 indexed citations
19.
Etchells, Terence A. & Michael J. Harrison. (2006). Orthogonal search‐based rule extraction for modelling the decision to transfuse. Anaesthesia. 61(4). 335–338. 6 indexed citations
20.
Lisböa, Paulo, et al.. (2002). Minimal MLPs do not model the XOR logic. Neurocomputing. 48(1-4). 1033–1037. 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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