Add like
Add dislike
Add to saved papers

Learning a structural and functional representation for gene expressions: To systematically dissect complex cancer phenotypes.

Cancer is a heterogeneous disease, thus one of the central problems is how to dissect the resulting complex phenotypes in terms of their biological building blocks. Computationally, this is to represent and interpret high dimensional observations through a structural and conceptual abstraction into the most influential determinants underlying the problem. The working hypothesis of this report is to consider gene interaction to be largely responsible for the manifestation of complex cancer phenotypes, thus where the representation is to be conceptualized. Here we report a representation learning strategy combined with regularizations, in which gene expressions are described in terms of a regularized product of meta-genes and their expression levels. The meta-genes are constrained by gene interactions thus representing their original topological contexts. The expression levels are supervised by their conditional dependencies among the observations thus providing a cluster-specific constraint. We obtain both of these structural constraints using a node-based graphical model. Our representation allows the selection of more influential modules, thus implicating their possible roles in neoplastic transformations. We validate our representation strategy by its robust recognitions of various cancer phenotypes comparing with various classical methods. The modules discovered are either shared or specify for different types or stages of human cancers, all of which are consistent with literature and biology.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app