ArticlePloS one2017
Extracting microRNA-gene relations from biomedical literature using distant supervision.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Biomedical literature mining: graph kernel-based learning for gene-gene interaction extraction.European journal of medical research · 2024Article
- Large Language Models and Genomics for Summarizing the Role of microRNA in Regulating mRNA Expression.Biomedicines · 2024Article
- Large language model based framework for automated extraction of genetic interactions from unstructured data.PloS one · 2024Article
- Article
- Literature Mining of Disease Associated Noncoding RNA in the Omics Era.Molecules (Basel, Switzerland) · 2022Review
- Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes.BMC bioinformatics · 2021Article
- Machine learning applications for therapeutic tasks with genomics data.Patterns (New York, N.Y.) · 2021Review
- Improving accessibility and distinction between negative results in biomedical relation extraction.Genomics & informatics · 2020Article
- CoCoScore: context-aware co-occurrence scoring for text mining applications using distant supervision.Bioinformatics (Oxford, England) · 2020Article
- Treating Different Diseases With the Same Method-A Traditional Chinese Medicine Concept Analyzed for Its Biological Basis.Frontiers in pharmacology · 2020Article
- Automated assessment of biological database assertions using the scientific literature.BMC bioinformatics · 2019Article
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.Human genetics · 2019Review
- Extraction of chemical-protein interactions from the literature using neural networks and narrow instance representation.Database : the journal of biological databases and curation · 2019Article
- Using distant supervision to augment manually annotated data for relation extraction.PloS one · 2019Article
- Statistical principle-based approach for recognizing and normalizing microRNAs described in scientific literature.Database : the journal of biological databases and curation · 2019Article
- Relation extraction for biological pathway construction using node2vec.BMC bioinformatics · 2018Article
- Article
- Generating a Tolerogenic Cell Therapy Knowledge Graph from Literature.Frontiers in immunology · 2017Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text. MicroRNA regulation is an important biological process due to its close association with human diseases. The proposed method, IBRel, is based on distantly supervised multi-instance learning. We evaluated IBRel on three datasets, and the results were compared with a co-occurrence approach as well as a supervised machine learning algorithm. While supervised learning outperformed on two of those datasets, IBRel obtained an F-score 28.3 percentage points higher on the dataset for which there was no training set developed specifically. To demonstrate the applicability of IBRel, we used it to extract 27 miRNA-gene relations from recently published papers about cystic fibrosis. Our results demonstrate that our method can be successfully used to extract relations from literature about a biological process without an annotated corpus. The source code and data used in this study are available at https://github.com/AndreLamurias/IBRel.
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