R Code for Machine learning search engine : Ranks/reveals combinations of genes/proteins in time series data

The search engine reveals ground-breaking results in the form of higher order (un)explored/(un)tested combinations of genes/proteins (as biological hypotheses), based on sensitivity indices that capture the strength of influence of factors (here genes/proteins) that affect a signaling pathway. The pipeline uses kernel-based sensitivity indices to capture the influence of the factors in a pathway and employs powerful support vector ranking algorithm. Because of the above point, biologists/oncologists will be able to narrow down their search to particular combinations that are ranked and, if a synergistic functioning is confirmed, will be able to study the mechanism between the components of a combination, in a  pathway. The search engine design is not only limited to one dataset and a range of combinations of genes/proteins. The framework can be applied/modified to all problems where one is interested in searching for particular combinations of factors involved in a particular phenomena. Recording the changing rankings of the combinations over time points and durations reveals how higher order interactions behave within the pathway and when and where an intervention might be necessary to influence the pathway, for therapeutic purpose.

Used packages -

• SVM Rank by Thorsten Joachims at - https://www.cs.cornell.edu/people/tj/svm_light/svm_rank.html 
• Sensitivity Pacakge in R at - https://cran.r-project.org/web/packages/sensitivity/index.html

Note - for ease, i have included these packages so that the search engine pipeline can be run. Please cite the above packages also for publication.

How to run the code - 

• The link in the section "identifiers" below, shows the sequence in which the files need to be executed in R.