The Text-mining based PubChem Bioassay neighboring analysis

Background: In recent years, the number of High Throughput Screening (HTS) assays deposited in PubChem has grown quickly. As a result, the volume of both the structured information (i.e. molecular structure, bioactivities) and the unstructured information (such as descriptions of bioassay experiments), has been increasing exponentially. As a result, it has become even more demanding and challenging to efficiently assemble the bioactivity data by mining the huge amount of information to identify and interpret the relationships among the diversified bioassay experiments. In this work, we propose a text-mining based approach for bioassay neighboring analysis from the unstructured text descriptions contained in the PubChem BioAssay database. Results: The neighboring analysis is achieved by evaluating the cosine scores of each bioassay pair and fraction of overlaps among the human-curated neighbors. Our results from the cosine score distribution analysis and assay neighbor clustering analy...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Lianyi Han, Tugba O. Suzek, Yanli Wang, Steve H. Bryant
• Keywords: Bioassay, BMCBI 2010, Neighboring Analysis, PubChem