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

View on Amazon

Details are supplied by the publisher catalogue. Confirm the edition, price, and availability with the retailer before buying.

0 reader favourites

Real votes on More Lovingly. This is an interest count, not a review score.

More Lovingly / The reading view

Make space for the human question.

Looking for a new direction in Bioinformatics? “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. The most rewarding way into an unfamiliar subject is often a concrete question. Choose an example that matters to you and give the reading a result you can explain or explore. Keep a person and a situation in view as you explore. Ask who would use the idea, what they would need to understand, and how their experience could change your interpretation. That human question is a productive creative starting point. A promising addition to a reading list with a purpose.

Find your reading angle →

Put someone in the picture

Explore Computational Biology, Cancer Research, Bioinformatics with a reading approach that gives the ideas somewhere to go. The most rewarding way into an unfamiliar subject is often a concrete question. Choose an example that matters to you and give the reading a result you can explain or explore.

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

A question before you choose

Whose needs and circumstances are represented in the available examples?

Try this with the topic

Choose a person and a task related to a listed topic. Ask what the approach would require them to understand and whose experience might be missing.

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.

Make room for the reading