Background:
Predicting the value of gene expression in a given condition is a challenging
topic in computational systems biology. Only a limited number of studies in this area have
provided solutions to predict the expression in a particular pattern, whether or not it can be done
effectively. However, the value of expression for the measurement is usually needed for further
meta-data analysis.
Methods:
Because the problem is considered as a regression task where a feature representation of
the gene under consideration is fed into a trained model to predict a continuous variable that refers
to its exact expression level, we introduced a novel feature representation scheme to support work
on such a task based on two-way collaborative filtering. At this point, our main argument is that
the expressions of other genes in the current condition are as important as the expression of the
current gene in other conditions. For regression analysis, linear regression and a recently popularized
method, called Relevance Vector Machine (RVM), are used. Pearson and Spearman correlation
coefficients and Root Mean Squared Error are used for evaluation. The effects of regression
model type, RVM kernel functions, and parameters have been analysed in our study in a gene expression
profiling data comprising a set of prostate cancer samples.
Results:
According to the findings of this study, in addition to promising results from the experimental
studies, integrating data from another disease type, such as colon cancer in our case, can
significantly improve the prediction performance of the regression model.
Conclusion:
The results also showed that the performed new feature representation approach and
RVM regression model are promising for many machine learning problems in microarray and high
throughput sequencing analysis.
Estimation, diagnostics, and extensions of nonparametric Hawkes processes with kernel functions
