QIAGEN OmicSoft and Biomedical Knowledge Base
Target exploration and cell line selection for drug discovery
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Cancer cell line models have been a cornerstone of cancer research for decades. Profiling cancer cell lines can be a powerful tool to identify gene alterations or cancer-related pathways and aid in discovering putative drug targets. In this webinar, we'll use QIAGEN OmicSoft Lands and QIAGEN Ingenuity Pathway Analysis (IPA) to help you select cell lines and translate insights from your cell line experiments for drug target discovery.
During this 90-minute discussion, we'll explore how you can use these software tools to:
• Select appropriate cancer cell lines for a variety of applications such as drug discovery, precision disease modeling, understanding gene function in cancer, immune-oncology research and more
• Examine various 'omics data for genes of interest for expression, mutation, hotspots and gene dependency data
• Generate networks for hypotheses and test them in silico to improve the translation of insights derived from cell line models to the drug target identification
• Integrate analyses of public 'omics data and drug response phenotypes using cell line model systems by exploring data from the Library of Integrated Network-Based Cellular Signatures (LINCS)
• Prioritize drug targets and profile phenotypic/downstream effects of drug action by overlaying public data on user-generated networks
Our system uses millions of curated literature findings from QIAGEN Knowledge Base and the OmicSoft digital warehouse. This discussion is intended for both those familiar with QIAGEN IPA and newcomers interested in learning more.
During this 90-minute discussion, we'll explore how you can use these software tools to:
• Select appropriate cancer cell lines for a variety of applications such as drug discovery, precision disease modeling, understanding gene function in cancer, immune-oncology research and more
• Examine various 'omics data for genes of interest for expression, mutation, hotspots and gene dependency data
• Generate networks for hypotheses and test them in silico to improve the translation of insights derived from cell line models to the drug target identification
• Integrate analyses of public 'omics data and drug response phenotypes using cell line model systems by exploring data from the Library of Integrated Network-Based Cellular Signatures (LINCS)
• Prioritize drug targets and profile phenotypic/downstream effects of drug action by overlaying public data on user-generated networks
Our system uses millions of curated literature findings from QIAGEN Knowledge Base and the OmicSoft digital warehouse. This discussion is intended for both those familiar with QIAGEN IPA and newcomers interested in learning more.
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