The integration of psychology and computer science has become the mainstream contemporary research method on psychological data. Weibo, China's largest open platform for communication and information sharing betwe...
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Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies have attempted to combine meta-learning ...
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Cancer is a rapidly evolving disease, with complex physiological changes throughout its development. Different patients of the same cancer type may require distinct treatments depending on the level of development. He...
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ISBN:
(数字)9781728162157
ISBN:
(纸本)9781728162164
Cancer is a rapidly evolving disease, with complex physiological changes throughout its development. Different patients of the same cancer type may require distinct treatments depending on the level of development. Hence it is essential to identify the genes and biological processes strongly associated with cancer progression to design an effective treatment plan. Our study has found that cancer samples of the same development stage (or grade, subtype) tend to share highly unique co-expression patterns, providing considerably stronger discerning power than differential expressions for cancer staging (and grading, subtyping). Based on this, we have developed a framework for identification and analyses of genes and pathways strongly associated with a cancer's development through identification of co-expression patterns that become increasingly stronger or weaker over cancer samples from early through advanced stages. Functional analyses of such co-expressed genes reveal that (1) cell-cycle, immune response, ribosome, proteasome and oxidative phosphorylation, among others, strongly associate with cancer development, (2) the co-expression patterns among ribosome, proteasome and oxidative phosphorylation genes tend to become increasingly weaker as a cancer advances, for almost all cancer types, and (3) the co-expression patterns among cell cycle and immune response genes tend to become increasingly stronger with cancer progression. We anticipate that co-expression-based analyses like we present here will become a key technique for functional studies of cancer development and evolution.
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