A NEW RELEVANCE ESTIMATOR FOR THE COMPILATION AND VISUALIZATION OF DISEASE PATTERNS AND POTENTIAL DRUG TARGETS

摘要

A new computational method is presented to extract disease patterns from heterogeneous and text-based data. For this study, 22 million PubMed records were mined for co-occurrences of gene name synonyms and disease MeSH terms. The resulting publication counts were transferred into a matrix M_(data). In this matrix, a disease was represented by a row and a gene by a column. Each field in the matrix represented the publication count for a co-occurring disease-gene pair. A second matrix with identical dimensions M_(relevance) was derived from M_(data). To create M_(relevance) the values from M_(data) were normalized. The normalized values were multiplied by the column-wise calculated Gini coefficient. This multiplication resulted in a relevance estimator for every gene in relation to a disease. From M_(relevance) the similarities between all row vectors were calculated. The resulting similarity matrix S_(relevance) related 5,000 diseases by the relevance estimators calculated for 15,000 genes. Three diseases were analyzed in detail for the validation of the disease patterns and the relevant genes. Cytoscape was used to visualize and to analyze M_(relevance) and S_(relevance) together with the genes and diseases. Summarizing the results, it can be stated that the relevance estimator introduced here was able to detect valid disease patterns and to identify genes that encoded key proteins and potential targets for drug discovery projects.