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Mathematical and Computational Oncology

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Cover of 'Mathematical and Computational Oncology'

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    Book Overview
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    Chapter 1 Phylogenies Derived from Matched Transcriptome Reveal the Evolution of Cell Populations and Temporal Order of Perturbed Pathways in Breast Cancer Brain Metastases
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    Chapter 2 Modeling the Evolution of Ploidy in a Resource Restricted Environment
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    Chapter 3 cmIF: A Python Library for Scalable Multiplex Imaging Pipelines
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    Chapter 4 Accurate and Flexible Bayesian Mutation Call from Multi-regional Tumor Samples
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    Chapter 5 Flexible Data Trimming for Different Machine Learning Methods in Omics-Based Personalized Oncology
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    Chapter 6 Towards Model-Based Characterization of Biomechanical Tumor Growth Phenotypes
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    Chapter 7 Population Modeling of Tumor Growth Curves, the Reduced Gompertz Model and Prediction of the Age of a Tumor
Attention for Chapter 4: Accurate and Flexible Bayesian Mutation Call from Multi-regional Tumor Samples
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Chapter title
Accurate and Flexible Bayesian Mutation Call from Multi-regional Tumor Samples
Chapter number 4
Book title
Mathematical and Computational Oncology
Published by
Springer, Cham, October 2019
DOI 10.1007/978-3-030-35210-3_4
Book ISBNs
978-3-03-035209-7, 978-3-03-035210-3
Authors

Takuya Moriyama, Seiya Imoto, Satoru Miyano, Rui Yamaguchi, Moriyama, Takuya, Imoto, Seiya, Miyano, Satoru, Yamaguchi, Rui

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