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• W161379969 abstract "The exponential growth of high-throughput Omics data has provided an unprecedented opportunity for new target identification to fuel the dried-up drug discovery pipeline. However, the bioinformatics analysis of large amount and heterogeneous Omics data has posed a great deal of technical challenges for experimentalists who lack statistical skills. Moreover, due to the complexity of human diseases, it is essential to analyze the Omics data in the context of molecular networks to detect meaningful biological targets and understand disease processes. Here, we describe an integrated bioinformatics analysis strategy and provide a running example to identify suitable targets for our in-house Enzyme-Mediated Cancer Imaging and Therapy (EMCIT) technology. In addition, we go through a few key concepts in the process, including corrected false discovery rate (FDR), Gene Ontology (GO), pathway analysis, and tissue specificity. We also describe popular programs and databases which allow the convenient annotation and network analysis of Omics data. We provide a practical guideline for researchers to quickly follow the protocol described and identify those targets that are pertinent to their work." @default.
• W161379969 created "2016-06-24" @default.
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• W161379969 date "2011-01-01" @default.
• W161379969 modified "2023-10-17" @default.
• W161379969 title "Integrated Bioinformatics Analysis for Cancer Target Identification" @default.
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• W161379969 doi "https://doi.org/10.1007/978-1-61779-027-0_25" @default.
• W161379969 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/21370101" @default.
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