Learning signaling network structures with sparsely distributed data
Flow cytometric measurement of signaling protein abundances has proved particularly useful for elucidation of signaling pathway structure. The single cell nature of the data ensures a very large dataset size, providing a statistically robust dataset for structure learning. Moreover, the approach is...
Main Authors: | Sachs, Karen, Itani, Solomon, Carlisle, Jennifer, Nolan, Garry P., Pe'er, Dana, Lauffenburger, Douglas A. |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Biological Engineering |
Format: | Article |
Language: | en_US |
Published: |
Mary Ann Liebert, Inc.
2010
|
Online Access: | http://hdl.handle.net/1721.1/60319 |
Similar Items
-
Learning Signaling Network Structures with Sparsely Distributed Data
by: Sachs, Karen, et al.
Published: (2011) -
Structured sparse signal recovery algorithms and their applications
by: Wang, Lu
Published: (2014) -
Structured Bayesian learning for recovery of clustered sparse signal
by: Wang, Lu, et al.
Published: (2022) -
Bayesian network models of biological signaling pathways
by: Sachs, Karen, Ph. D. Massachusetts Institute of Technology
Published: (2007) -
Sparse Representations of Multiple Signals
by: Evgeniou, Theodoros, et al.
Published: (2004)