Titlebook: Computational Methods for Precision Oncology; Alessandro Laganà Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive

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Patient-Derived In Vitro and In Vivo Models of Cancer,ing to identify the genomic alterations that give rise to and sustain individual tumors. This expansion has allowed researchers to identify and target highly recurrent alterations in specific cancer contexts, such as EGFR mutations in non-small cell lung cancer (Lynch et al, N Engl J Med 350:2129–21

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Artificial Intelligence for Precision Oncology,tic and molecular profile. The rapid development of novel high-throughput omics technologies in recent years has led to the generation of massive amount of complex patient data, which in turn has prompted the development of novel computational infrastructures, platforms, and tools to store, retrieve

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Sediment instability on subaqueous slopes,guide cancer therapeutics, contributing to significant progress in precision oncology. In this chapter, we start by introducing CNVs. Then, we discuss the main approaches and methods developed for detecting somatic CNV for next-generation sequencing data, along with its challenges. Finally, we descr

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