Identification of Cancer Development Related Pathways Based on Co-Expression Analyses

Jiang, Hongyang; Wang, Zhihang; Yin, Chaoyi; Sun, Peishuo; Xu, Ying; Sun, Huiyan

doi:10.1109/bibm49941.2020.9313240

Cited by 1 publication

(1 citation statement)

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“…Hence, to ensure the robustness of each subtype specific network, we construct multiple co-expression networks for each molecular subtype by sampling and then integrate them. Our previous study had proved that sampling-based co-expression network construction could avoid the bias caused by both data noise and imbalanced sample size among different subtypes ( Jiang et al, 2020 ). Class-specific modules are identified by a top-down approach (i.e.…”

Section: Discussionmentioning

confidence: 99%

Molecular Subtyping of Cancer Based on Distinguishing Co-Expression Modules and Machine Learning

Sun

Yin

et al. 2022

Front. Genet.

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Molecular subtyping of cancer is recognized as a critical and challenging step towards individualized therapy. Most existing computational methods solve this problem via multi-classification of gene-expressions of cancer samples. Although these methods, especially deep learning, perform well in data classification, they usually require large amounts of data for model training and have limitations in interpretability. Besides, as cancer is a complex systemic disease, the phenotypic difference between cancer samples can hardly be fully understood by only analyzing single molecules, and differential expression-based molecular subtyping methods are reportedly not conserved. To address the above issues, we present here a new framework for molecular subtyping of cancer through identifying a robust specific co-expression module for each subtype of cancer, generating network features for each sample by perturbing correlation levels of specific edges, and then training a deep neural network for multi-class classification. When applied to breast cancer (BRCA) and stomach adenocarcinoma (STAD) molecular subtyping, it has superior classification performance over existing methods. In addition to improving classification performance, we consider the specific co-expressed modules selected for subtyping to be biologically meaningful, which potentially offers new insight for diagnostic biomarker design, mechanistic studies of cancer, and individualized treatment plan selection.

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Section: Discussionmentioning

confidence: 99%