Table 2 |
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Brief description of function prediction methods used |
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| Submission identifier |
Approach |
Name |
Author initials |
|
|
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| A |
Compute several kernel matrices (SVM) for each data matrix, train one GO term specific
SVM per kernel, and map SVMs' discriminants to probabilities using logistic regression |
Calibrated ensembles of SVMs |
GO, GL, JQ, CG, MJ, and WSN |
| B |
Four different kernels are used per data set. Integration of best kernels and data
sources is done using the kernel logistic regression model |
Kernel logistic regression [55] |
HL, MD, TC, and FS |
| C |
Construct similarity kernels, assign a weight to each kernel using linear regression,
combine the weighted kernels, and use a graph based algorithm to obtain the score
vector |
geneMANIA |
SM, DW-F, CG, DR, and QM |
| D |
Train SVM classifiers on each GO term and individual data sets, construct several
Bayesian networks that incorporate diverse data sources and hierarchical relationships,
and chose for each GO term the Bayes net or the SVM yielding the highest AUC |
Multi-label hierarchical classification [56] and Bayesian integration |
YG, CLM, ZB, and OGT |
| E |
Combination of an ensemble of classifiers (naïve Bayes, decision tree, and boosted
tree) with guilt-by-association in a functional linkage network, choosing the maximum
score |
Combination of classifier ensemble and gene network |
WKK, CK, and EMM |
| F |
Code the relationship between functional similarity and the data into a functional
linkage graph and predict gene functions using Boltzmann machine and simulated annealing |
GeneFAS (gene function annotation system) [2,3] |
TJ, CZ, GNL, and DX |
| G |
Two methods with scores combined by logistic regression: guilt-by-association using
a weighted functional linkage graph generated by probabilistic decision trees; and
random forests trained on all binary gene attributes |
Funckenstein |
WT, MT, FDG, and FPR |
| H |
Pairwise similarity features for gene pairs were derived from the available data.
A Random Forest classifier was trained using pairs of genes for each GO term. Predictions
are based on similarity between the query gene and the positive examples for that
GO term |
Function prediction through query retrieval |
YQ, JK, and ZB |
| I |
Construct an interaction network per data set, merge data set graphs into a single
graph, and apply a belief propagation algorithm to compute the probability for each
protein to have a specific function given the functions assigned to the proteins in
the rest of the graph |
Function prediction with message passing algorithms [57] |
ML and AP |
|
|
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AUC, area under the receiver operating characteristic curve; GO, Gene Ontology. |
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Peña-Castillo et al. Genome Biology 2008 9(Suppl 1):S2 doi:10.1186/gb-2008-9-s1-s2 |
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