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Biomedical text mining

Can one model find diseases, genes and how they relate in text?

Bundschus M, Dejori M, Stetter M, et al. · BMC bioinformatics · 2008

Open access · cc by · source: Europe PMC

Treating relation extraction as a sequence-labelling problem with conditional random fields matched or beat older methods, and relation-specific features drove most of the gain.

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Study at a glance

Design
Computational / modelling — Supervised sequence labelling with CRFs, evaluated by cross-validation on two annotated corpora against several baselines, plus a feature-by-feature ablation.
N
Two corpora: 3570 sentences from PubMed abstracts (disease-treatment relations) and 5720 GeneRIF sentences (gene-disease relations); the trained model was then run on 110881 GeneRIFs.
Population
Biomedical text: MEDLINE abstracts and Entrez Gene GeneRIF phrases
Outcome
Precision, recall and F-measure for named-entity recognition and relation extraction; accuracy for relation classification

Structured fields used in claim comparison tables when every cited study has a complete layer.

Key findings

On disease-treatment data the cascaded CRF reached 72% F-measure for entity recognition (best graphical model 71%) and 96.9% relation accuracy when entities were given, similar to the neural network's 96.6%; with entities unknown, accuracy fell to 79.5%. On gene-disease data the cascaded CRF matched a tuned CRF+SVM pipeline while training about ten times faster, whereas the one-step CRF was slightly worse because it found disease entities less well. In the ablation, adding a dictionary window feature lifted F-measure from 39.65 to 67.38.

Methodology

The authors cast 'semantic relation extraction' (finding entities and labelling the type of relationship between them) as tagging each word in a sentence. They built a cascaded CRF, which first tags entities and then feeds those tags into a second CRF that labels relations, and a one-step CRF that does both at once when the key entity is already known. They tested on 3570 PubMed sentences about diseases and treatments and 5720 GeneRIF sentences about genes and diseases, comparing against an SVM, a multilayer neural network, generative graphical models and simple rule-based systems.

Limitations

Performance was judged by cross-validation on the authors' own annotated corpora, with a strict exact-boundary criterion that makes numbers hard to compare with other studies. Rare relation types (such as 'unrelated' and regulatory modification) had few examples and were extracted poorly. The relation types record association, not causation, and ignore the conditions under which a relation holds; the large gene-disease network built from GeneRIFs is noisy and was not independently validated. The work predates modern neural language models, so the baselines are dated.

How this study connects

Role on claims

Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.

  • SupportsBiomedical named entity recognitionconcept

    Before pretraining, dictionaries carried much of the knowledge.

    Domain knowledge features mattered a lot in pre-neural systems: in a CRF relation extractor, adding a dictionary window feature lifted F-measure from 39.65 to 67.38.

    Evidence for the claim as stated.

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