Approaches to uncovering cancer diagnostic and prognostic molecular signatures.
The recent rapid development of high-throughput technology enables the study of molecular signatures for cancer diagnosis and prognosis at multiple levels, from genomic and epigenomic to transcriptomic. These unbiased large-scale scans provide important insights into the detection of cancer-related signatures. In addition to single-layer signatures, such as gene expression and somatic mutations, integrating data from multiple heterogeneous platforms using a systematic approach has been proven to be particularly effective for the identification of classification markers. This approach not only helps to uncover essential driver genes and pathways in the cancer network that are responsible for the mechanisms of cancer development, but will also lead us closer to the ultimate goal of personalized cancer therapy.
Chinese Academy of Sciences Key Laboratory of Computational Biology; Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology; Shanghai Institutes for Biological Sciences; Chinese Academy of Sciences ; Shanghai, China.
Shengjun
S
Hong
Chinese Academy of Sciences Key Laboratory of Computational Biology; Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology; Shanghai Institutes for Biological Sciences; Chinese Academy of Sciences ; Shanghai, China.
Yi
Y
Huang
Chinese Academy of Sciences Key Laboratory of Computational Biology; Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology; Shanghai Institutes for Biological Sciences; Chinese Academy of Sciences ; Shanghai, China.
Yaqiang
Y
Cao
Chinese Academy of Sciences Key Laboratory of Computational Biology; Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology; Shanghai Institutes for Biological Sciences; Chinese Academy of Sciences ; Shanghai, China.
Xingwei
X
Chen
Chinese Academy of Sciences Key Laboratory of Computational Biology; Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology; Shanghai Institutes for Biological Sciences; Chinese Academy of Sciences ; Shanghai, China.
Jing-Dong J
JD
Han
Bayesian network; cancer diagnosis; cancer prognosis; molecular signature; subtype