Dimensionality and Structure in Cancer Genomics: A Statistical Learning Perspective
Computational analysis of genomic data has transformed research and clinical practice in oncology. Machine learning and AI advancements hold promise for answering theoretical and practical questions. While the modern researcher has access to a catalogue of tools from disciplines such as natural lang...
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
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IntechOpen
2020
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| Acceso en línea: | https://www.intechopen.com/chapters/72299 |
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| Sumario: | Computational analysis of genomic data has transformed research and clinical practice in oncology. Machine learning and AI advancements hold promise for answering theoretical and practical questions. While the modern researcher has access to a catalogue of tools from disciplines such as natural language processing and image recognition, before browsing for our favourite off-the-shelf technique it is worth asking a sequence of questions. What sort of data are we dealing with in cancer genomics? Do we have enough of it to be successful without designing into our models what we already know about its structure? If our methods do work, will we understand why? Are our tools robust enough to be applied in clinical practice? If so, are the technologies upon which they rely economically viable? While we will not answer all of these questions, we will provide language with which to discuss them. Understanding how much information we can expect to extract from data is a statistical question. |
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| ISBN: | 978-1-78984-689-8 |
| DOI: | 10.5772/intechopen.92574 |