TY - GEN
T1 - A Network-based Approach for Inferring Thresholds in Co-expression Networks
AU - López-Rozo, Nicolás
AU - Romero, Miguel
AU - Finke, Jorge
AU - Rocha, Camilo
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Gene co-expression networks (GCNs) specify binary relationships between genes and are of biological interest because significant network relationships suggest that two co-expressed genes rise and fall together across different cellular conditions. GCNs are built by (i) calculating a co-expression measure between each pair of genes and (ii) selecting a significance threshold to remove spurious relationships among genes. This paper introduces a threshold criterion based on the underlying topology of the network. More specifically, the criterion considers both the rate at which isolated nodes are added to the network and the density of its components when the threshold varies. In addition to Pearson’s correlation measure, the biweight midcorrelation, the distance correlation, and the maximal information coefficient are used to build different GCNs from the same data and showcase the advantages of the proposed approach. Finally, a case study presents a comparison of the predictive performance of the different networks when trying to predict gene functional annotations using hierarchical multi-label classification.
AB - Gene co-expression networks (GCNs) specify binary relationships between genes and are of biological interest because significant network relationships suggest that two co-expressed genes rise and fall together across different cellular conditions. GCNs are built by (i) calculating a co-expression measure between each pair of genes and (ii) selecting a significance threshold to remove spurious relationships among genes. This paper introduces a threshold criterion based on the underlying topology of the network. More specifically, the criterion considers both the rate at which isolated nodes are added to the network and the density of its components when the threshold varies. In addition to Pearson’s correlation measure, the biweight midcorrelation, the distance correlation, and the maximal information coefficient are used to build different GCNs from the same data and showcase the advantages of the proposed approach. Finally, a case study presents a comparison of the predictive performance of the different networks when trying to predict gene functional annotations using hierarchical multi-label classification.
KW - Correlation metrics
KW - Gene co-expression network
KW - Gene function prediction
KW - Hierarchical multi-label classification
KW - Network density
UR - http://www.scopus.com/inward/record.url?scp=85148769626&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-21127-0_22
DO - 10.1007/978-3-031-21127-0_22
M3 - Conference contribution
AN - SCOPUS:85148769626
SN - 9783031211263
T3 - Studies in Computational Intelligence
SP - 265
EP - 276
BT - Complex Networks and Their Applications XI - Proceedings of The 11th International Conference on Complex Networks and Their Applications
A2 - Cherifi, Hocine
A2 - Mantegna, Rosario Nunzio
A2 - Rocha, Luis M.
A2 - Cherifi, Chantal
A2 - Miccichè, Salvatore
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2022
Y2 - 8 November 2022 through 10 November 2022
ER -