Chemical identification and indexing in PubMed full-text articles using deep learning and heuristics

Abstract

The identification of chemicals in articles has attracted a large interest in the biomedical scientific community, given its importance in drug development research. Most of previous research have focused on PubMed abstracts, and further investigation using full-text documents is required because these contain additional valuable information that must be explored. The manual expert task of indexing Medical Subject Headings (MeSH) terms to these articles later helps researchers find the most relevant publications for their ongoing work. The BioCreative VII NLM-Chem track fostered the development of systems for chemical identification and indexing in PubMed full-text articles. Chemical identification consisted in identifying the chemical mentions and linking these to unique MeSH identifiers. This manuscript describes our participation system and the post-challenge improvements we made. We propose a three-stage pipeline that individually performs chemical mention detection, entity normalization and indexing. Regarding chemical identification, we adopted a deep-learning solution that utilizes the PubMedBERT contextualized embeddings followed by a multilayer perceptron and a conditional random field tagging layer. For the normalization approach, we use a sieve-based dictionary filtering followed by a deep-learning similarity search strategy. Finally, for the indexing we developed rules for identifying the more relevant MeSH codes for each article. During the challenge, our system obtained the best official results in the normalization and indexing tasks despite the lower performance in the chemical mention recognition task. In a post-contest phase we boosted our results by improving our named entity recognition model with additional techniques. The final system achieved 0.8731, 0.8275 and 0.4849 in the chemical identification, normalization and indexing tasks, respectively. The code to reproduce our experiments and run the pipeline is publicly available. Database URL https://github.com/bioinformatics-ua/biocreativeVII_track2

BELB: a biomedical entity linking benchmark

“…For diseases and chemicals, we include in the RBES category two systems which are only “partly” rule-based (stretching our definition), as they better represent the state of the art of disease/chemical-specific models. We use “TaggerOne” ( Leaman and Lu 2016 ), a semi-Markov model, for diseases, and opt for the system that won the BioCreative VII NLM-Chem track ( Almeida et al 2022 ) for chemicals (“BC7T2W”), which uses both string matching and neural embeddings. To the best of our knowledge there exists no linking approach specific for cell lines.…”

Extracting Structured Data from Organic Synthesis Procedures Using a Fine-Tuned Large Language Model

“…Various statistical model-based NER algorithms have also been proposed, often as a sequence labeling problem where the tokens in a sentence are assigned most likely tags based on token features. A popular strategy is the use of conditional random fields 11 in combination with expertselected features 12 or contextualized word embeddings from neural networks (recurrent networks [13][14][15] , or transformers [16][17][18][19] ).…”