Andrew MacKinlay

716 total citations
30 papers, 432 citations indexed

About

Andrew MacKinlay is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Andrew MacKinlay has authored 30 papers receiving a total of 432 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 19 papers in Molecular Biology and 4 papers in Information Systems. Recurrent topics in Andrew MacKinlay's work include Biomedical Text Mining and Ontologies (19 papers), Topic Modeling (18 papers) and Natural Language Processing Techniques (16 papers). Andrew MacKinlay is often cited by papers focused on Biomedical Text Mining and Ontologies (19 papers), Topic Modeling (18 papers) and Natural Language Processing Techniques (16 papers). Andrew MacKinlay collaborates with scholars based in Australia, United States and Norway. Andrew MacKinlay's co-authors include Timothy Baldwin, Antonio Jimeno Yepes, Marco Lui, Paul Cook, Li Wang, David Martínez, Timothy Baldwin, Karin Verspoor, Baden Hughes and Steven Bird and has published in prestigious journals such as BMC Bioinformatics, Artificial Intelligence in Medicine and BMC Medical Informatics and Decision Making.

In The Last Decade

Andrew MacKinlay

29 papers receiving 385 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Andrew MacKinlay Australia	12	313	116	46	37	25	30	432
Juan Antonio Lossio-Ventura United States	11	218 0.7×	110 0.9×	40 0.9×	70 1.9×	29 1.2×	37	391
Hansi Zhang United States	12	143 0.5×	91 0.8×	41 0.9×	34 0.9×	24 1.0×	28	379
Anthony Rios United States	13	503 1.6×	268 2.3×	23 0.5×	37 1.0×	23 0.9×	33	644
Maria Skeppstedt Sweden	14	546 1.7×	363 3.1×	28 0.6×	34 0.9×	16 0.6×	58	636
Zhiyu Wan United States	11	240 0.8×	42 0.4×	61 1.3×	34 0.9×	37 1.5×	40	450
Samuel Bayer United States	10	274 0.9×	123 1.1×	20 0.4×	30 0.8×	6 0.2×	21	409
Jean Charlet France	11	241 0.8×	230 2.0×	10 0.2×	58 1.6×	14 0.6×	77	408
George Demetriou United Kingdom	10	464 1.5×	408 3.5×	7 0.2×	64 1.7×	33 1.3×	42	670
Lorraine Goeuriot France	10	215 0.7×	88 0.8×	17 0.4×	58 1.6×	7 0.3×	33	270
Colin Price United Kingdom	6	230 0.7×	250 2.2×	13 0.3×	20 0.5×	16 0.6×	17	406

Countries citing papers authored by Andrew MacKinlay

Since Specialization

Citations

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

Fields of papers citing papers by Andrew MacKinlay

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andrew MacKinlay

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

All Works

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20 of 20 papers shown

MacKinlay, Andrew, Christine Schieber, Antonio Jimeno Yepes, et al.. (2018). Stage-based Variation in the Effect of Primary Tumor Side on All Stages of Colorectal Cancer Recurrence and Survival. Clinical Colorectal Cancer. 17(3). e569–e577. 27 indexed citations

Yepes, Antonio Jimeno, et al.. (2016). Temporal Modelling of Geospatial Words in Twitter.. 133–137. 1 indexed citations

Yepes, Antonio Jimeno & Andrew MacKinlay. (2016). NER for Medical Entities in Twitter using Sequence to Sequence Neural Networks.. 138–142. 11 indexed citations

MacKinlay, Andrew, Antonio Jimeno Yepes, & Bo Han. (2015). Identification and Analysis of Medical Entity Co-occurrences in Twitter. 22–22. 2 indexed citations

Liu, Haibin, Karin Verspoor, Donald C. Comeau, Andrew MacKinlay, & W. John Wilbur. (2015). Optimizing graph-based patterns to extract biomedical events from the literature. BMC Bioinformatics. 16(S16). S2–S2. 8 indexed citations

Yepes, Antonio Jimeno, Andrew MacKinlay, & Bo Han. (2015). Investigating Public Health Surveillance using Twitter. 164–170. 26 indexed citations

Martínez, David, Graham Pitson, Andrew MacKinlay, & Lawrence Cavedon. (2014). Cross-hospital portability of information extraction of cancer staging information. Artificial Intelligence in Medicine. 62(1). 11–21. 18 indexed citations

Han, Bo, Antonio Jimeno Yepes, Andrew MacKinlay, & Qiang Chen. (2014). Identifying Twitter Location Mentions. 157–162. 3 indexed citations

Yepes, Antonio Jimeno, et al.. (2014). Deep Belief Networks and Biomedical Text Categorisation. 123–127. 12 indexed citations

10.

MacKinlay, Andrew, David Martínez, Antonio Jimeno Yepes, et al.. (2013). Extracting Biomedical Events and Modifications Using Subgraph Matching with Noisy Training Data. Meeting of the Association for Computational Linguistics. 35–44. 9 indexed citations

11.

Baldwin, Timothy, Paul Cook, Marco Lui, Andrew MacKinlay, & Li Wang. (2013). How Noisy Social Media Text, How Diffrnt Social Media Sources?. International Joint Conference on Natural Language Processing. 356–364. 124 indexed citations

12.

MacKinlay, Andrew & Karin Verspoor. (2013). Information Extraction from Medication Prescriptions Within Drug Administration Data. 2 indexed citations

13.

Martínez, David, Andrew MacKinlay, Diego Mollá, Lawrence Cavedon, & Karin Verspoor. (2012). Simple similarity-based question answering strategies for biomedical text. 1178. 1–13. 4 indexed citations

14.

MacKinlay, Andrew, et al.. (2012). The Effects of Semantic Annotations on Precision Parse Ranking. Joint Conference on Lexical and Computational Semantics. 1. 228–236. 5 indexed citations

15.

MacKinlay, Andrew, David Martínez, & Timothy Baldwin. (2012). Detecting modification of biomedical events using a deep parsing approach. BMC Medical Informatics and Decision Making. 12(S1). S4–S4. 20 indexed citations

16.

Verspoor, Karin, Andrew MacKinlay, Judith D. Cohn, & Michael E. Wall. (2012). DETECTION OF PROTEIN CATALYTIC SITES IN THE BIOMEDICAL LITERATURE. PubMed. 433–444. 6 indexed citations

17.

MacKinlay, Andrew, et al.. (2011). Treeblazing: Using External Treebanks to Filter Parse Forests for Parse Selection and Treebanking. International Joint Conference on Natural Language Processing. 246–254. 2 indexed citations

18.

Baldwin, Timothy, David Martínez, Su Nam Kim, et al.. (2010). Intelligent Linux Information Access by Data Mining: the ILIAD Project. North American Chapter of the Association for Computational Linguistics. 1(1). 15–16. 8 indexed citations

19.

MacKinlay, Andrew & Timothy Baldwin. (2009). A Baseline Approach to the RTE5 Search Pilot. Theory and applications of categories. 6 indexed citations

20.

Hughes, Baden, et al.. (2006). Reconsidering language identification for written language resources. Language Resources and Evaluation. 485–488. 52 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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