ArticleMolecules (Basel, Switzerland)2018
ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers.
Article in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Systematic optimization of host-directed therapeutic targets and preclinical validation of repositioned antiviral drugs.Briefings in bioinformatics · 2022Article
- Molecular Computing and Bioinformatics.Molecules (Basel, Switzerland) · 2019Article
- Constructing a database for the relations between CNV and human genetic diseases via systematic text mining.BMC bioinformatics · 2018Article
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Authors and funding
6 authors.
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Abstract
A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing efficiency of text mining. In this paper, we address this challenge by introducing parallel processing on a supercomputer. We developed paraBTM, a runnable framework that enables parallel text mining on the Tianhe-2 supercomputer. It employs a low-cost yet effective load balancing strategy to maximize the efficiency of parallel processing. We evaluated the performance of paraBTM on several datasets, utilizing three types of named entity recognition tasks as demonstration. Results show that, in most cases, the processing efficiency can be greatly improved with parallel processing, and the proposed load balancing strategy is simple and effective. In addition, our framework can be readily applied to other tasks of biomedical text mining besides NER.
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Registered trials
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