Titlebook: Cancer Bioinformatics; Alexander Krasnitz Book 2019 Springer Science+Business Media, LLC, part of Springer Nature 2019 cancer informatics.

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GULLY

Computational Methods for Identification of T Cell Neoepitopes in Tumors,ethods have enabled discovery of tumor-specific mutations leading to protective T cell neoepitopes. Many of the successes are enabled by computational methods, which facilitate processing of raw data, mapping of mutations, and prediction of neoepitopes. In this book chapter, we provide an overview o

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CHARM

Computational Methods for Subtyping of Tumors and Their Applications for Deciphering Tumor Heterogemethylation, and gene expression) for a large number of tumors. This activity has provided unique opportunities and challenges to stratify tumors and decipher tumor heterogeneity. In this chapter, we summarize several computational methods to address the challenge of tumor stratification with differ

臥虎藏龍

Statistically Supported Identification of Tumor Subtypes,ide practical guidance to the use of a recently developed statistical subtyping tool, termed Tree Branches Evaluated Statistically for Tightness (TBEST), and its eponymous R language implementation. TBEST employs hierarchical clustering to partition the data at a user-specified level of significance

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