A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles

Lin, Sheng-Jie; Yeh, Wen-Chao; Chiu, Yu-Wen; Chang, Yung Chun; Hsu, Min-Huei; Chen, Yi-Shin; Hsu, Wen-Lian

doi:10.1093/database/baac056

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Notable datasets produced from these workshops include BC5, which covers disease and chemical annotations, and BC4, focused on chemical annotations. Additionally, 8 further papers reported utilizing a BioCreative corpus, with LitCovid and DrugProt being the most prominent [61, 62, 29, 63].…”

Section: Resultsmentioning

confidence: 99%

Large Language Models to process, analyze, and synthesize biomedical texts – a scoping review

Doneva,

Qin,

Sick

et al. 2024

Preprint

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The advent of large language models (LLMs) such as BERT and, more recently, GPT, is transforming our approach of analyzing and understanding biomedical texts. To stay informed about the latest advancements in this area, there is a need for up-to-date summaries on the impact of LLM on Natural Language Processing (NLP) in the biomedical field. Thus, this scoping review aims to provide a detailed overview of the current state of biomedical NLP research and its applications, with a special focus on the evolving role of LLMs. We conducted a systematic search of PubMed, EMBASE, and Google Scholar for studies and conference proceedings published from 2017 to December 19, 2023, that develop or utilize LLMs for NLP tasks in biomedicine. From 13,823 references, we selected 199 publications and conference proceedings for our review. LLMs are being applied to a wide array of tasks in the biomedical field, including knowledge management, text mining, drug discovery, and evidence synthesis. Prominent among these tasks are text classification, relation extraction, and named entity recognition. Although BERT-based models remain prevalent, the use of GPT-based models has substantially increased since 2023.

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Section: Resultsmentioning

confidence: 99%

Large Language Models to process, analyze, and synthesize biomedical texts – a scoping review

Doneva,

Qin,

Sick

et al. 2024

Preprint

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Semantic features analysis for biomedical lexical answer type prediction using ensemble learning approach

Hussain,

Wasim,

Cheema

et al. 2024

Knowl Inf Syst

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Lexical answer type prediction is integral to biomedical question–answering systems. LAT prediction aims to predict the expected answer’s semantic type of a factoid or list-type biomedical question. It also aids in the answer processing stage of a QA system to assign a high score to the most relevant answers. Although considerable research efforts exist for LAT prediction in diverse domains, it remains a challenging biomedical problem. LAT prediction for the biomedical field is a multi-label classification problem, as one biomedical question might have more than one expected answer type. Achieving high performance on this task is challenging as biomedical questions have limited lexical features. One biomedical question must be assigned multiple labels given these limited lexical features. In this paper, we develop a novel feature set (lexical, noun concepts, verb concepts, protein–protein interactions, and biomedical entities) from these lexical features. Using ensemble learning with bagging, we use the label power set transformation technique to classify multi-label. We evaluate the integrity of our proposed methodology on the publicly available multi-label biomedical questions dataset (MLBioMedLAT) and compare it with twelve state-of-the-art multi-label classification algorithms. Our proposed method attains a micro-F1 score of 77%, outperforming the baseline model by 25.5%.

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Research category classification of scientific articles on human health risks of electromagnetic fields using pre-trained BERT

Lee,

Kwon,

Kim

et al. 2024

ICT Express

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A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles

Cited by 3 publications

References 26 publications

Large Language Models to process, analyze, and synthesize biomedical texts – a scoping review

Large Language Models to process, analyze, and synthesize biomedical texts – a scoping review

Semantic features analysis for biomedical lexical answer type prediction using ensemble learning approach

Research category classification of scientific articles on human health risks of electromagnetic fields using pre-trained BERT

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