A text-mining analysis of the human phenome

By M. V. Driel, Jorn Bruggeman, G. Vriend, H. Brunner, J. Leunissen. European Journal of Human Genetics, 2006

653 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/68d92a1f3d92536c50732ff19bc3dd0e9ac5e936

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Citations in the corpus

Visualization of genetic disease-phenotype similarities by multiple maps t-SNE with Laplacian regularization (2014, methodology): “The analysis shows that although the direct similarity between ABS and MNS is 0 as measured by the text mining approach from [15], our method indeed inferred their real relationships from the data.”
Inductive matrix completion for predicting gene–disease associations (2014, methodology): “Analogous to the use of HumanNet for obtaining latent features for genes, we extract 100 latent disease features from the disease similarity network MimMiner (Van Driel et al., 2006).”
Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks (2012, methodology): “In order to produce a disease-disease phenotype similarity network, van-Driel et al[44] text-mined OMIM phenotype entries and created a feature vector for each one, composed of the”
Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm (2016, methodology): “Disease similarity Sim d p based on disease phenotypes is calculated using MimMiner (Van Driel et al. , 2006), which measure disease similarity by computing similarity between MeSH terms (Lipscomb, 2000) appearing in the medical description of diseases from the OMIM database.”
Network-based global inference of human disease genes (2008, methodology): “Comparison with other methods Various methods (Perez-Iratxeta et al, 2002; Turner et al, 2003; van Driel et al, 2003; Adie et al, 2005; Aerts et al, 2006; Franke et al, 2006; Oti et al, 2006; Lage et al, 2007) have been proposed for prioritizing candidate genes, but few of them have reported the…”
Network-based methods for human disease gene prediction. (2011, methodology): “A couple of text mining techniques were used to map OMIM diseases to different standard vocabularies, Medical Subject Headings (MeSH) or the Unified Medical Language System (UMLS), to score pair-wise similarities among the disease records [49, 50].”
The power of protein interaction networks for associating genes with diseases (2010, neutral): “…2007; Radivojac et al., 2008), biological pathways (Aerts et al., 2006; Franke et al., 2006; George et al., 2006), text mining (Ozgur et al., 2008; van Driel et al., 2006), transcription factor binding sites (Aerts et al., 2006) and various phenotypic traits of diseases (Freudenberg and Propping,…”
Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors (2013, methodology): “We obtained the similarity between any two of 5080 disease phenotypes from the literature [30].”