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Our goal is to systematically evaluate multiple linguistic features, context features, keywords and patterns as cues in the automatic extraction of interaction relation to improve our understanding of how individual features or their combination affect system performance.
Many high efficient tools were developed for recognition of named entity such as protein and gene names in free text, identification of subcellular localization of proteins, extraction of interaction of proteins, and association of genes according to functional concepts such as gene ontology and MeSH terms [ 10– 16].
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In the extraction of interactions, there are times that negative interactions are extracted.
It is important to note that our approach differs from typical extraction approach, which focuses on the extraction of interactions that are stated explicitly in the text.
Such a double-ranking has been tentatively studied for extraction of interactions from text, using a high-value gold-standard as a training set (37).
For the extraction from published literature the software Pathway Studio V9 provides a gene relation database termed ResNet Mammalian, which has been compiled by automatic extraction of interactions from PubMed, as evaluated by [ 15].
With the identification of protein families and extraction of negative interactions, we perform data cleaning on the extracted interactions by using AnsProlog logic rules to refine the interactions.
As our approach in finding DDIs heavily relies on the extraction of relevant interactions, we first evaluated the correctness of the interactions extracted from 13K Medline abstracts.
This step aided in the extraction of RBC parasite interactions in biological context and elimination of large number of interactions that are unlikely to occur in vivo.
This in turn allows the extraction of all interactions that are between pairs of promoters (promoter:promoter interactions), or between other features of interest (e.g. promoter:enhancer or enhancer:enhancer interactions).
We only evaluated the precision of the extraction of negative interactions by analyzing each of the extracted relation and its supporting evidence without creating a gold standard.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com