Text mining for contexts and relationships in cancer genomics literature.
Text mining for contexts and relationships in cancer genomics literature.
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DOI:
10.1093/bioinformatics/btae021
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发表时间:
2024-01-02
期刊:
影响因子:
--
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文献类型:
--
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Scientific advances build on the findings of existing research. The 2001 publication of the human genome has led to the production of huge volumes of literature exploring the context-specific functions and interactions of genes. Technology is needed to perform large-scale text mining of research papers to extract the reported actions of genes in specific experimental contexts and cell states, such as cancer, thereby facilitating the design of new therapeutic strategies. We present a new corpus and Text Mining methodology that can accurately identify and extract the most important details of cancer genomics experiments from biomedical texts. We build a Named Entity Recognition model that accurately extracts relevant experiment details from PubMed abstract text, and a second model that identifies the relationships between them. This system outperforms earlier models and enables the analysis of gene function in diverse and dynamically evolving experimental contexts. Code and data are available here: https://github.com/cambridgeltl/functional-genomics-ie.
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影响因子:
14.9
作者:
Luo H;Lin Y;Liu T;Lai FL;Zhang CT;Gao F;Zhang R
通讯作者:
Zhang R
影响因子:
14.9
作者:
Kuleshov MV;Jones MR;Rouillard AD;Fernandez NF;Duan Q;Wang Z;Koplev S;Jenkins SL;Jagodnik KM;Lachmann A;McDermott MG;Monteiro CD;Gundersen GW;Ma'ayan A
通讯作者:
Ma'ayan A
影响因子:
3.1
作者:
Goncalves, Carlos Adriano;Vieira, Adrian Seara;Diz, Lourdes Borrajo
通讯作者:
Diz, Lourdes Borrajo
影响因子:
2.7
作者:
Oliveira Goncalves, Carlos Adriano;Camacho, Rui;Lorenzo Iglesias, Eva
通讯作者:
Lorenzo Iglesias, Eva
DOI:
10.1038/s41571-018-0002-6
发表时间:
2018-06
期刊:
Nature reviews. Clinical oncology
影响因子:
--
作者:
Berger MF;Mardis ER
通讯作者:
Mardis ER