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Iain McKendrick

dblp:77/10200 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › biomedical text mining
biomedical event extraction
0.112011
Automatic extraction of angiogenesis bioprocess from text · Bioinform. 2011
Bioinformatics and computational biology
biomedical text mining
0.112011
Automatic extraction of angiogenesis bioprocess from text · Bioinform. 2011

Methods — techniques the papers use, named apart from their topics

supervised machine learning · 0.1lexicon-based extraction · 0.1
YearPublicationVenuePosition
2011 Automatic extraction of angiogenesis bioprocess from text
abstract
MOTIVATION: Understanding key biological processes (bioprocesses) and their relationships with constituent biological entities and pharmaceutical agents is crucial for drug design and discovery. One way to harvest such information is searching the literature. However, bioprocesses are difficult to capture because they may occur in text in a variety of textual expressions. Moreover, a bioprocess is often composed of a series of bioevents, where a bioevent denotes changes to one or a group of cells involved in the bioprocess. Such bioevents are often used to refer to bioprocesses in text, which current techniques, relying solely on specialized lexicons, struggle to find. RESULTS: This article presents a range of methods for finding bioprocess terms and events. To facilitate the study, we built a gold standard corpus in which terms and events related to angiogenesis, a key biological process of the growth of new blood vessels, were annotated. Statistics of the annotated corpus revealed that over 36% of the text expressions that referred to angiogenesis appeared as events. The proposed methods respectively employed domain-specific vocabularies, a manually annotated corpus and unstructured domain-specific documents. Evaluation results showed that, while a supervised machine-learning model yielded the best precision, recall and F1 scores, the other methods achieved reasonable performance and less cost to develop. AVAILABILITY: The angiogenesis vocabularies, gold standard corpus, annotation guidelines and software described in this article are available at http://text0.mib.man.ac.uk/~mbassxw2/angiogenesis/ CONTACT: [email protected].
Xinglong Wang, Iain McKendrick, Ian P. Barrett, Ian Dix, Tim French 0003, Jun'ichi Tsujii, Sophia Ananiadou
Bioinform.2