Supervised prediction of aging-related genes from a context-specific protein interaction subnetwork
Contact: Prof. Tijana Milenković
References:
1. Li, Q. and Milenković, T. (2019). Supervised prediction of aging-related
genes from a context-specific protein interaction subnetwork.
IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp, 130–137.
2. Li, Q. and Milenković, T. (2021). Supervised prediction of aging-related
genes from a context-specific protein interaction subnetwork,
IEEE/ACM Transactions on Computational Biology and Bioinformatics, , DOI: 10.1109/TCBB.2021.3076961. (This is extended journal version of the above IEEE BIBM conference paper.)
The considered entire static context-unspecific PPI network:
This network is publicly available and it comes from
Human Protein Reference Database (HPRD).
The two considered aging-specific subnetworks:
The dynamic aging-specific subnetwork (consisting of 37 age-specific network shapshots) and the static aging-specific subnetwork that we use can be downloaded
here.
The six considered aging-related ground truth data sets:
All six data sets originate from other groups' studies and are publicly available:
1. GenAge genes can be downloaded from
https://genomics.senescence.info/genes/. "Tacutu, R. et al. (2017). Human Ageing Genomic Resources: new and updated databases.
Nucleic Acids Research, 46(D1), D1083–D1090."
2-3. GTEx-DAG genes and GTEx-UAG genes can be downloaded from the Supplementary data of "Jia, K. et al. (2018). An analysis of aging-related genes derived from the genotype-tissue expression project (GTEx).
Cell Death Discovery, 5(1), 26."
4. BEx2004 genes can be downloaded from the supplementary data of "Lu, T. et al. (2004). Gene regulation and DNA damage in the ageing human brain.
Nature, 429(6994), 883."
5. BEx2008 genes can be downloaded from
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11882. "Berchtold, N. C. et al. (2008). Gene expression changes in the course of normal brain aging are sexually dimorphic.
Proceedings of the National Academy of Sciences, 105(40), 15605–15610."
6. ADEx2011 genes can be downloaded from the Supplementary data of "Simpson, J. E. et al. (2011). Microarray analysis of the astrocyte transcriptome in the aging brain: relationship to Alzheimer’s pathology and APOE genotype.
Neurobiology of Aging, 32(10), 1795–1807."
The two considered definitions of (non-)aging-related genes (i.e., node labels for classification):
1. The GenAge-based list of 187 aging-related and 2,499 non-aging-related genes can be found
here.
2. The GTEx-DAG-based list of 439 aging-related and 2,499 non-aging-related genes can be found
here.
The 11 features considered in this study:
All features originate from other groups' studies and their impementations are publicly available:
1. To compute DGDV of each node in a dynamic network, please refer to
DGDV.
2. To compute GoT of each node in a dynamic network, please refer to
GoT-WAVE.
3. To compute GDC, ECC, KC, or DegC of each node in a static network or in a snapshot of a dynamic network, please refer to
node centrality.
4. CentraMV works as follows. For a given centrality-based feature (out of GDC, ECC, KC, and DegC), the mean and the corresponding variation are computed over a given node’s 37 centrality values corresponding to the 37 snapshots of the dynamic subnetwork. The mean is self-explanatory, and the variation of node
u is
var(u) = sum(centrality(u)_{i+1} − centrality(u)_i)/36, i = 1,2,...,36 . These two quantities are computed for each of the four centrality-based features, and the resulting eight values form the CentraMV node feature.
4. To compute SGDV of each node in a static network or in a snapshot of a dynamic network, please refer to
Orca.
5. To compute cSGDV of each node in a static network or in a snapshot of a dynamic network, please refer to
colored graphlets.
6. To compute UniNet of each node in a static network, please refer to "Kerepesi, C. et al. (2018). Prediction and characterization of human ageing-related proteins by using machine learning.
Scientific Reports, 8(1), 4094.".
7. To compute
mBPIs of each node in a static network, please refer to "Freitas, A. A. et al. (2011). A data mining approach for classifying DNA repair genes into ageing-related or non-ageing-related.
BMC Genomics, 12(1), 27.".