Skip to content
PaperFren

Biomedical text mining

Does focusing on nearby words help find gene and disease names?

Zhu Q, Li X, Conesa A, et al. · Bioinformatics (Oxford, England) · 2018

Open access · cc by · source: Europe PMC

A convolutional network that looks only at the words around each term matched or beat most existing tools at spotting biomedical names in text, without hand-made features.

Save to libraryView originalAsk PaperFrenStudy view

Study at a glance

Design
Computational / modelling — New neural architecture (word/character/POS embeddings, multi-size CNN kernels, CRF output) compared with published NER systems on three benchmark corpora.
N
No single N: BC2 has 15 000 training and 5000 test sentences; NCBI has 6892 disease mentions from 793 abstracts; JNLPBA has 20 546 training and 4260 test sentences.
Population
Biomedical text corpora (gene mentions, disease mentions, and five entity classes such as protein, DNA and cell type)
Outcome
Precision, recall and F1-score for entity recognition

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

Key findings

GRAM-CNN reached an F1-score of 87.26% on the gene-mention set, ranking second overall and first among single (non-ensemble) models. On the disease corpus it ranked first with the same F1 and the highest recall. On the harder five-class JNLPBA corpus it scored 72.57%, just behind a rule-based system built specifically for that task, and it was the only method near the top on all three datasets.

Methodology

The authors built GRAM-CNN, which represents each word by a pretrained biomedical word embedding, a character-level embedding and a part-of-speech tag embedding, then runs convolution kernels of several widths over the neighbouring words. A conditional random field layer on top picks the most likely sequence of labels. They trained and tested it on three public biomedical named-entity datasets and compared it with previously published systems.

Limitations

There is no ablation isolating how much the local-context idea itself (versus the embeddings or the CRF) drives the gains, and no direct head-to-head retraining of an LSTM under identical settings; comparisons rely on numbers reported by other papers. The model cannot handle overlapping, disjoint or table-based mentions, needs thousands of labelled training examples and took days to train. Error analysis suggests some test-set labels are inconsistent, so the scores partly reflect annotation noise.

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

    Progress came in small steps on fixed benchmarks.

    Each method generation reported gains of a few F-score points on shared corpora: NERBio's additions raised JNLPBA F-score by 3.28 points to 72.98%; GRAM-CNN reached 87.26% on gene mentions; a context-LSTM reached 85.68% on NCBI disease, about 1.5 points above GRAM-CNN.

    Evidence for the claim as stated.

Related papers in this topic

Same topic cluster — not a recommendation engine.