Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.03.09.531915v1?rss=1
Authors: Fernandez, A., Locatelli, M., Bertoni, M., Aloy, P.
Abstract:
Living a Big Data era in Biomedicine, there is an unmet need to systematically assess experimental observations in the context of available information. This assessment would offer a means for an unbiased validation of the results and provide an initial estimate of the potential novelty of the findings. Here we present BQsupports, a web-based tool built upon the Bioteque biomedical descriptors that systematically analyzes and quantifies the current support to a given set of observations. The tool relies on over 1,000 distinct types of biomedical descriptors, covering over 11 different biological and chemical entities, including genes, cell lines, diseases and small molecules. By exploring hundreds of descriptors, BQsupports provide support scores for each observation across a wide variety of biomedical contexts. These scores are then aggregated to summarize the biomedical support of the assessed dataset as a whole. Finally, the BQsupports also suggests predictive features of the given dataset, which can be exploited in downstream machine learning applications.
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