Lee Harland

1.9k total citations
22 papers, 750 citations indexed

About

Lee Harland is a scholar working on Molecular Biology, Computational Theory and Mathematics and Information Systems and Management. According to data from OpenAlex, Lee Harland has authored 22 papers receiving a total of 750 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 9 papers in Computational Theory and Mathematics and 5 papers in Information Systems and Management. Recurrent topics in Lee Harland's work include Biomedical Text Mining and Ontologies (12 papers), Bioinformatics and Genomic Networks (11 papers) and Computational Drug Discovery Methods (9 papers). Lee Harland is often cited by papers focused on Biomedical Text Mining and Ontologies (12 papers), Bioinformatics and Genomic Networks (11 papers) and Computational Drug Discovery Methods (9 papers). Lee Harland collaborates with scholars based in United Kingdom, United States and Netherlands. Lee Harland's co-authors include Paul Groth, Steve Pettifer, Carole Goble, Christine Chichester, Antony Williams, Bryn Williams–Jones, William Loging, Chris T. Evelo, Gerhard F. Ecker and Niklas Blomberg and has published in prestigious journals such as Nature Reviews Drug Discovery, Drug Discovery Today and Genomics.

In The Last Decade

Lee Harland

22 papers receiving 718 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lee Harland United Kingdom 13 534 267 134 111 66 22 750
Christine Chichester Switzerland 17 594 1.1× 184 0.7× 246 1.8× 46 0.4× 90 1.4× 31 958
Micheal Hewett United States 7 410 0.8× 176 0.7× 138 1.0× 103 0.9× 149 2.3× 16 669
Vijayalakshmi Chelliah United Kingdom 17 1.2k 2.2× 227 0.9× 36 0.3× 102 0.9× 46 0.7× 22 1.5k
Camille Laibe United Kingdom 14 1.2k 2.2× 137 0.5× 135 1.0× 96 0.9× 19 0.3× 20 1.5k
Giuseppe Agapito Italy 19 442 0.8× 93 0.3× 144 1.1× 77 0.7× 53 0.8× 72 906
Jeremy L. Muhlich United States 19 1.1k 2.0× 402 1.5× 73 0.5× 56 0.5× 58 0.9× 25 1.4k
Yael Garten United States 12 472 0.9× 178 0.7× 182 1.4× 71 0.6× 99 1.5× 14 606
Tianyi Zang China 14 610 1.1× 255 1.0× 120 0.9× 69 0.6× 22 0.3× 65 916
Tommi Nyrönen Finland 17 420 0.8× 140 0.5× 18 0.1× 62 0.6× 102 1.5× 36 814
Otto Ritter Germany 5 550 1.0× 106 0.4× 55 0.4× 54 0.5× 19 0.3× 7 747

Countries citing papers authored by Lee Harland

Since Specialization
Citations

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

Fields of papers citing papers by Lee Harland

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lee Harland

This figure shows the co-authorship network connecting the top 25 collaborators of Lee Harland. A scholar is included among the top collaborators of Lee Harland 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 Lee Harland. Lee Harland 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.
Gray, Alasdair J. G., Paul Groth, Antonis Loizou, et al.. (2014). Applying linked data approaches to pharmacology: Architectural decisions and implementation. Semantic Web. 5(2). 101–113. 36 indexed citations
2.
Sidders, Ben S., Alex Gutteridge, Lee Harland, et al.. (2014). Precompetitive activity to address the biological data needs of drug discovery. Nature Reviews Drug Discovery. 13(2). 83–84. 12 indexed citations
3.
Groth, Paul, Antonis Loizou, Alasdair J. G. Gray, et al.. (2014). Api-Centric Linked Data Integration: The Open Phacts Discovery Platform Case Study. SSRN Electronic Journal. 7 indexed citations
4.
Chichester, Christine, Daniela Digles, Ronald Siebes, et al.. (2014). Drug discovery FAQs: workflows for answering multidomain drug discovery questions. Drug Discovery Today. 20(4). 399–405. 14 indexed citations
5.
Groth, Paul, Antonis Loizou, Alasdair J. G. Gray, et al.. (2014). API-centric Linked Data integration: The Open PHACTS Discovery Platform case study. Journal of Web Semantics. 29. 12–18. 37 indexed citations
6.
Harland, Lee, et al.. (2012). Open source software in life science research: Practical solutions to common challenges in the pharmaceutical industry and beyond. Woodhead Publishing Limited eBooks. 3 indexed citations
7.
Williams, Antony, Lee Harland, Paul Groth, et al.. (2012). Open PHACTS: semantic interoperability for drug discovery. Drug Discovery Today. 17(21-22). 1188–1198. 211 indexed citations
8.
Pettifer, Steve, Jan Velterop, Teresa K. Attwood, et al.. (2012). Reuniting data and narrative in scientific articles. Insights the UKSG journal. 25(3). 288–293. 1 indexed citations
9.
Gray, Alasdair J. G., Christian Brenninkmeijer, Kees Burger, et al.. (2012). The Pharmacology Workspace: A Platform for Drug Discovery. Research Explorer (The University of Manchester). 2 indexed citations
10.
Harland, Lee, et al.. (2012). Open source software in life science research. Woodhead Publishing Limited eBooks. 11 indexed citations
11.
Harland, Lee, Christopher Larminie, Susanna‐Assunta Sansone, et al.. (2011). Empowering industrial research with shared biomedical vocabularies. Drug Discovery Today. 16(21-22). 940–947. 11 indexed citations
12.
Campbell, Stephen J., et al.. (2011). Visualizing the drug target landscape. Drug Discovery Today. 17. S3–S15. 5 indexed citations
13.
Wild, David, Ying Ding, Amit Sheth, et al.. (2011). Systems chemical biology and the Semantic Web: what they mean for the future of drug discovery research. Drug Discovery Today. 17(9-10). 469–474. 44 indexed citations
14.
Campbell, Stephen J., et al.. (2009). Visualizing the drug target landscape. Drug Discovery Today. 15(1-2). 3–15. 39 indexed citations
15.
Harland, Lee & Anna Gaulton. (2009). Drug target central. Expert Opinion on Drug Discovery. 4(8). 857–872. 10 indexed citations
16.
Barnes, Michael R., Lee Harland, Steven M. Foord, et al.. (2009). Lowering industry firewalls: pre-competitive informatics initiatives in drug discovery. Nature Reviews Drug Discovery. 8(9). 701–708. 54 indexed citations
17.
Loging, William, Lee Harland, & Bryn Williams–Jones. (2007). High-throughput electronic biology: mining information for drug discovery. Nature Reviews Drug Discovery. 6(3). 220–230. 65 indexed citations
18.
Antoniou, Michael, Lee Harland, Steven G. Williams, et al.. (2003). Transgenes encompassing dual-promoter CpG islands from the human TBP and HNRPA2B1 loci are resistant to heterochromatin-mediated silencing. Genomics. 82(3). 269–279. 111 indexed citations
19.
Harland, Lee, et al.. (2002). Transcriptional Regulation of the Human TATA Binding Protein Gene. Genomics. 79(4). 479–482. 24 indexed citations
20.
Athanassiadou, Aglaia, et al.. (2002). LCR-mediated, long-term tissue-specific gene expression within replicating episomal plasmid and cosmid vectors. Gene Therapy. 9(5). 327–336. 22 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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