ArticleChinese journal of cancer2015
Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling.
Article in Chinese journal of cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Systematic analyses of glutamine and glutamate metabolisms across different cancer types.Chinese journal of cancer · 2017Article
- Deep Sequencing of Urinary RNAs for Bladder Cancer Molecular Diagnostics.Clinical cancer research : an official journal of the American Association for Cancer Research · 2017Article
- Structural analysis of tumor-related single amino acid mutations in human MxA protein.Chinese journal of cancer · 2015Article
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Authors and funding
3 authors.
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Abstract
We examine the role of big data and machine learning in cancer research. We describe an example in cancer research where gene-level data from The Cancer Genome Atlas (TCGA) consortium is interpreted using a pathway-level model. As the complexity of computational models increases, their sample requirements grow exponentially. This growth stems from the fact that the number of combinations of variables grows exponentially as the number of variables increases. Thus, a large sample size is needed. The number of variables in a computational model can be reduced by incorporating biological knowledge. One particularly successful way of doing this is by using available gene regulatory, signaling, metabolic, or context-specific pathway information. We conclude that the incorporation of existing biological knowledge is essential for the progress in using big data for cancer research.
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