Automatic extraction of gene-disease associations from literature using joint ensemble learning

Bhasuran, Balu; Natarajan, Jeyakumar

doi:10.1371/journal.pone.0200699

Cited by 62 publications

(47 citation statements)

References 48 publications

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“…The best scores are in bold, and the second best scores are underlined. The scores on GAD and EU-ADR were obtained from Bhasuran and Natarajan (2018), and the scores on CHEMPROT were obtained from Lim and Kang (2018). Notes: Strict Accuracy (S), Lenient Accuracy (L) and Mean Reciprocal Rank (M) scores on each dataset are reported.…”

Section: Resultsmentioning

confidence: 99%

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

et al. 2019

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Motivation: Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. Results: We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. Availability and implementation: We make the pre-trained weights of BioBERT freely available at https://github. com/naver/biobert-pretrained, and the source code for fine-tuning

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Section: Resultsmentioning

confidence: 99%

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

et al. 2019

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“…Pre-processing: We anonymize the named entities in the sentence by replacing them with predefined tags like @PROT1$, @DRUG$ (Bhasuran and Natarajan, 2018).…”

Section: Methodsmentioning

confidence: 99%

Biomedical Relation Classification by single and multiple source domain adaptation

Chakraborty

Sarkar

Goyal

et al. 2019

Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

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Relation classification is crucial for inferring semantic relatedness between entities in a piece of text. These systems can be trained given labelled data. However, relation classification is very domain-specific and it takes a lot of effort to label data for a new domain. In this paper, we explore domain adaptation techniques for this task. While past works have focused on single source domain adaptation for bio-medical relation classification, we classify relations in an unlabeled target domain by transferring useful knowledge from one or more related source domains. Our experiments with the model have shown to improve state-of-the-art F1 score on 3 benchmark biomedical corpora for single domain and on 2 out of 3 for multi-domain scenarios. When used with contextualized embeddings, there is further boost in performance outperforming neural-network based domain adaptation baselines for both the cases.

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“…For this reason, their SVM was trained with positive, negative, neutral, and irrelevant relations, which allowed assigning the polarity in the form of “ risk .” For instance, particular food can either increase risk, reduce risk, be neutral, or be irrelevant for a disease. Recently, Bhasuran and Natarajan ( 2018 ) extended the study by Özgür et al ( 2008 ) using an ensemble of SVMs trained with small samples of stratified and bootstrapped data. This method also included a word2vec representation in combination with rich semantic and syntactic features.…”

Section: Inferring Relationsmentioning

confidence: 98%

Named Entity Recognition and Relation Detection for Biomedical Information Extraction

Perera

Dehmer

Emmert‐Streib

2020

Front. Cell Dev. Biol.

103

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The number of scientific publications in the literature is steadily growing, containing our knowledge in the biomedical, health, and clinical sciences. Since there is currently no automatic archiving of the obtained results, much of this information remains buried in textual details not readily available for further usage or analysis. For this reason, natural language processing (NLP) and text mining methods are used for information extraction from such publications. In this paper, we review practices for Named Entity Recognition (NER) and Relation Detection (RD), allowing, e.g., to identify interactions between proteins and drugs or genes and diseases. This information can be integrated into networks to summarize large-scale details on a particular biomedical or clinical problem, which is then amenable for easy data management and further analysis. Furthermore, we survey novel deep learning methods that have recently been introduced for such tasks.

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Automatic extraction of gene-disease associations from literature using joint ensemble learning

Cited by 62 publications

References 48 publications

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

Biomedical Relation Classification by single and multiple source domain adaptation

Named Entity Recognition and Relation Detection for Biomedical Information Extraction

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