Unveiling complex patterns: An information-theoretic approach to high-order behaviors in microarray data
Antonio Lacalamita, Alfonso Monaco, Grazia Serino, Daniele Marinazzo, Nicola Amoroso, Loredana Bellantuono, Marianna La Rocca, Tommaso Maggipinto, Ester Pantaleo, Emanuele Piccinno, Viviana Scalavino, Sabina Tangaro, Gianluigi Giannelli, Sebastiano Stramaglia, Roberto Bellotti
TL;DR
This paper uses an information-theoretic method to uncover complex gene interactions in microarray data from HCC and ASD patients.
Contribution
The novel application of partial information decomposition (PID) reveals higher-order gene behaviors not captured by traditional methods.
Findings
PID identifies differential genes and enriched functions linked to disease phenotypes.
Synergy clusters reveal higher-order behaviors not detected by classical correlation methods.
Abstract
The information-theoretic approach can shed light on the role of groups of correlated elements within a network. While there are already established methods for measuring new information, storage and transmission, the definition and application of methods for measuring information change remains an unresolved challenge. The change of information in a network is associated with redundancy and synergy between systems that share information about a target. Redundancy involves shared information about the target that can be retrieved using the individual source systems, while synergy involves information that can only be obtained by sharing the systems. A more refined approach, called partial information decomposition (PID), separates the unique, redundant and synergetic contributions of the shared information. However, these contributions cannot be directly derived from the classical…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39
Figure 40
Figure 41
Figure 42
Figure 43
Figure 44
Figure 45
Figure 46
Figure 47
Figure 48
Figure 49
Figure 50Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGene expression and cancer classification · Bioinformatics and Genomic Networks · Gene Regulatory Network Analysis