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BMC Bioinformatics. 2018 Jan 25;19(1):21. doi: 10.1186/s12859-018-2029-1.

Deep learning of mutation-gene-drug relations from the literature.

Author information

1
Department of Computer Science and Engineering, Korea University, Seoul, South Korea.
2
Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, South Korea.
3
Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA. aikchoon.tan@ucdenver.edu.
4
Department of Computer Science and Engineering, Korea University, Seoul, South Korea. kangj@korea.ac.kr.
5
Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, South Korea. kangj@korea.ac.kr.

Abstract

BACKGROUND:

Molecular biomarkers that can predict drug efficacy in cancer patients are crucial components for the advancement of precision medicine. However, identifying these molecular biomarkers remains a laborious and challenging task. Next-generation sequencing of patients and preclinical models have increasingly led to the identification of novel gene-mutation-drug relations, and these results have been reported and published in the scientific literature.

RESULTS:

Here, we present two new computational methods that utilize all the PubMed articles as domain specific background knowledge to assist in the extraction and curation of gene-mutation-drug relations from the literature. The first method uses the Biomedical Entity Search Tool (BEST) scoring results as some of the features to train the machine learning classifiers. The second method uses not only the BEST scoring results, but also word vectors in a deep convolutional neural network model that are constructed from and trained on numerous documents such as PubMed abstracts and Google News articles. Using the features obtained from both the BEST search engine scores and word vectors, we extract mutation-gene and mutation-drug relations from the literature using machine learning classifiers such as random forest and deep convolutional neural networks. Our methods achieved better results compared with the state-of-the-art methods. We used our proposed features in a simple machine learning model, and obtained F1-scores of 0.96 and 0.82 for mutation-gene and mutation-drug relation classification, respectively. We also developed a deep learning classification model using convolutional neural networks, BEST scores, and the word embeddings that are pre-trained on PubMed or Google News data. Using deep learning, the classification accuracy improved, and F1-scores of 0.96 and 0.86 were obtained for the mutation-gene and mutation-drug relations, respectively.

CONCLUSION:

We believe that our computational methods described in this research could be used as an important tool in identifying molecular biomarkers that predict drug responses in cancer patients. We also built a database of these mutation-gene-drug relations that were extracted from all the PubMed abstracts. We believe that our database can prove to be a valuable resource for precision medicine researchers.

KEYWORDS:

BioNLP; Convolutional neural networks; Deep learning; Information extraction; Mutation; NLP; Precision medicine; Text mining

PMID:
29368597
PMCID:
PMC5784504
DOI:
10.1186/s12859-018-2029-1
[Indexed for MEDLINE]
Free PMC Article

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