Deep learning of mutation-gene-drug relations from the literature
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 preclinica...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-01-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2029-1 |
| _version_ | 1818862757514051584 |
|---|---|
| author | Kyubum Lee Byounggun Kim Yonghwa Choi Sunkyu Kim Wonho Shin Sunwon Lee Sungjoon Park Seongsoon Kim Aik Choon Tan Jaewoo Kang |
| author_facet | Kyubum Lee Byounggun Kim Yonghwa Choi Sunkyu Kim Wonho Shin Sunwon Lee Sungjoon Park Seongsoon Kim Aik Choon Tan Jaewoo Kang |
| author_sort | Kyubum Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T10:04:56Z |
| format | Article |
| id | doaj.art-386d88c6d68142de95188c596a5735a1 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-19T10:04:56Z |
| publishDate | 2018-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-386d88c6d68142de95188c596a5735a12022-12-21T20:26:32ZengBMCBMC Bioinformatics1471-21052018-01-0119111310.1186/s12859-018-2029-1Deep learning of mutation-gene-drug relations from the literatureKyubum Lee0Byounggun Kim1Yonghwa Choi2Sunkyu Kim3Wonho Shin4Sunwon Lee5Sungjoon Park6Seongsoon Kim7Aik Choon Tan8Jaewoo Kang9Department of Computer Science and Engineering, Korea UniversityInterdisciplinary Graduate Program in Bioinformatics, Korea UniversityDepartment of Computer Science and Engineering, Korea UniversityDepartment of Computer Science and Engineering, Korea UniversityInterdisciplinary Graduate Program in Bioinformatics, Korea UniversityDepartment of Computer Science and Engineering, Korea UniversityDepartment of Computer Science and Engineering, Korea UniversityDepartment of Computer Science and Engineering, Korea UniversityTranslational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical CampusDepartment of Computer Science and Engineering, Korea UniversityAbstract 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.http://link.springer.com/article/10.1186/s12859-018-2029-1Deep learningConvolutional neural networksInformation extractionText miningNLPBioNLP |
| spellingShingle | Kyubum Lee Byounggun Kim Yonghwa Choi Sunkyu Kim Wonho Shin Sunwon Lee Sungjoon Park Seongsoon Kim Aik Choon Tan Jaewoo Kang Deep learning of mutation-gene-drug relations from the literature BMC Bioinformatics Deep learning Convolutional neural networks Information extraction Text mining NLP BioNLP |
| title | Deep learning of mutation-gene-drug relations from the literature |
| title_full | Deep learning of mutation-gene-drug relations from the literature |
| title_fullStr | Deep learning of mutation-gene-drug relations from the literature |
| title_full_unstemmed | Deep learning of mutation-gene-drug relations from the literature |
| title_short | Deep learning of mutation-gene-drug relations from the literature |
| title_sort | deep learning of mutation gene drug relations from the literature |
| topic | Deep learning Convolutional neural networks Information extraction Text mining NLP BioNLP |
| url | http://link.springer.com/article/10.1186/s12859-018-2029-1 |
| work_keys_str_mv | AT kyubumlee deeplearningofmutationgenedrugrelationsfromtheliterature AT byounggunkim deeplearningofmutationgenedrugrelationsfromtheliterature AT yonghwachoi deeplearningofmutationgenedrugrelationsfromtheliterature AT sunkyukim deeplearningofmutationgenedrugrelationsfromtheliterature AT wonhoshin deeplearningofmutationgenedrugrelationsfromtheliterature AT sunwonlee deeplearningofmutationgenedrugrelationsfromtheliterature AT sungjoonpark deeplearningofmutationgenedrugrelationsfromtheliterature AT seongsoonkim deeplearningofmutationgenedrugrelationsfromtheliterature AT aikchoontan deeplearningofmutationgenedrugrelationsfromtheliterature AT jaewookang deeplearningofmutationgenedrugrelationsfromtheliterature |