Chapter title |
Design of Bioengineered Peptides/Proteases as Anti-cancer Reagents with Integrated Omics and Machine Learning Approaches.
|
---|---|
Chapter number | 22 |
Book title |
Proteases and Cancer
|
Published in |
Methods in molecular biology, January 2024
|
DOI | 10.1007/978-1-0716-3589-6_22 |
Pubmed ID | |
Book ISBNs |
978-1-07-163588-9, 978-1-07-163589-6
|
Authors |
Zuo, Weimin, Kwok, Hang Fai |
Abstract |
Cancer is a heterogeneous disorder of uncontrolled growth of cells, which has proven to be a major burden worldwide. Many treatment options are available for cancer therapy, yet side effects and drug resistance remain major hurdles. Therefore, it is necessary to develop novel drugs for cancer therapy. Anti-cancer peptides (ACPs) are attractive candidates with remarkable potency, low toxicity, and high specificity advantages. However, traditional experimental identification of ACPs is time-consuming and expensive. Integrated omics combined with machine learning (ML) is considered a new powerful and cost-effective strategy to discover ACPs from natural products. In this chapter, we describe in detail experimental procedures for collecting both transcriptomic and proteomic data from venoms, followed by descriptive approaches to ML prediction. |
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