Introduction to Computational Cancer Biology

By Kenwright

A practical guide to the intersection of data science and oncology. Discover how computational tools are revolutionizing cancer research and enabling precision medicine.

Pages: 884
Publication: 20 October 2025
Language: English
ISBN: 9798273100732

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Curiosity Killed the Cat / The reading view

For the question you cannot leave alone.

Looking for a new direction in Medical Data Science? “Introduction to Computational Cancer Biology” makes a persuasive case for giving the subject your attention. Bring a specific question and turn the reading into a purposeful exploration. Computational biology brings models and biological questions into the same conversation. Focus on one relationship and distinguish the model, its assumptions, and the observations it is meant to help interpret. Follow the most surprising question first. Sketch an explanation, imagine an alternative, and see what you would need to distinguish them. The best part of an interesting subject is often the next question it unlocks. A promising addition to a reading list with a purpose.

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Curiosity Killed the Cat reading lens

Follow the surprising question

Explore Computational Biology, Cancer Research, Bioinformatics with a reading approach that gives the ideas somewhere to go. Computational biology brings models and biological questions into the same conversation. Focus on one relationship and distinguish the model, its assumptions, and the observations it is meant to help interpret.

The listed 884 pages give a sense of extent; page count alone does not establish depth or difficulty.

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Write an observation connected to one of the topics. List two possible explanations and the evidence that would help you tell them apart.

Publisher’s synopsis

Cancer is one of the most complex diseases known to science, and understanding it requires more than biology alone. This book introduces readers to the powerful role of computational techniques in cancer research. From modeling tumor growth and analyzing genomic data to applying machine learning for diagnosis and treatment prediction, this guide offers a comprehensive overview of the tools and technologies reshaping oncology. Designed for students, researchers, and clinicians, it bridges the gap between biological insight and computational power.

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