Sentence examples for mining of biomedical from inspiring English sources

Natural language processing (NLP) is a common approach to text mining of biomedical corpora [2], [3].

However, we believe this is the baseline of an advanced platform to support interactive mining of biomedical information.

Text mining of biomedical literature is a research area that has attracted lot of attention in the last 5 to 10 years.

For example, through text mining of biomedical literature, Liu et al. found that genes MNN1, CLN1, and CLN2 were all influenced by the same activators [ 28].

eVOC, a controlled vocabulary for unifying gene expression data, is a purposely developed anatomical ontology that can integrate text mining of biomedical literature and data mining of available human gene expression data [ 39].

The database is filled by text-mining of biomedical literature and comprises all grades of association.

Selection of the most probable of these candidate genes for further empirical analysis requires integration of data-mining of gene expression data and text-mining of biomedical literature.

Different data sources provide evidence for genes and diseases: text-mining of biomedical literature, functional annotations, phenotype relationships, protein protein interactions, regulatory information, orthologous phenotypes in other species and gene-expression information.

The survey article by Piro and Di Cunto (2012) discusses the following different types of evidence used by prioritization tools: text-mining of biomedical literature, functional annotations, pathways and ontologies, phenotype relationships, intrinsic gene properties, sequence data, protein protein interactions, regulatory information, orthologous relationships and gene expression information.

G2D prioritization strategy is based on a combination of data mining on biomedical databases and sequence features (Perez-Iratxeta et al., 2005).

This process addresses the limitations of human experts when performing data mining on biomedical databases.