Predicting genetic interactions in Caenorhabditis elegans using machine learning

[摘要] (cont.) We adapt the factorization-based and the neighborhood-aware CF [13] to deal with a mixture of continuous and discrete entries. We use collaborative filtering to input missing values, assess how much information relevant to genetic interactions is present, and, finally, to predict genetic interactions. We also show how CF can reduce input dimensionality. Our last development is the application of Support Vector Machines (SVM), an adapted machine learning classification method, to predicting genetic interactions. We find that SVM with nonlinear radial basis function (RBF) kernel has greater predictive power over CF. Its performance, however, greatly benefits from using CF to fill in missing entries in the input data. We show that SVM performance further improves if we constrain the group of genes to a specific functional category. Throughout this thesis, we emphasize the features of the studied datasets and explain our findings from a biological perspective. In this respect, we hope that this work possesses an independent biological significance. The final step would be to confirm our predictions experimentally. This would allow us to gain new insights into C. elegans biology: specific genes orchestrating developmental and regulatory pathways, response to stress, etc.

[发布机构] Massachusetts Institute of Technology