High Dimensionality Reduction by Matrix Factorization for Systems Pharmacology

The extraction of predictive features from the complex high-dimensional multi-omic data is necessary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of features in in vitro, in vivo and clinical data is essential to discover the evolution and mechanisms of the drug responses and drug resistance. In this paper, we have utilized the Matrix Factorization (MF) as a modality for high dimensionality reduction in systems pharmacology. In this respect, we have proposed three novel feature selection methods using the mathematical conception of a basis for features. We have applied these techniques as well as three other matrix factorization methods to analyze eight different gene expression datasets to investigate and compare their performance for feature selection. Our results show that these methods are capable of reducing the feature spaces and find predictive features in terms of phenotype determination. The three proposed techniques outperform the other methods used and can extract a 2-gene signature predictive of a Tyrosine Kinase Inhibitor (TKI) treatment response in the Cancer Cell Line Encyclopedia (CCLE). Key Points Matrix Factorization (MF) is a useful framework for high dimensionality reduction in systems pharmacology. Novel feature selection methods using the incorporation of the mathematical conception of a basis for features into MF increases the performance of feature selection process. Feature selection based on the basis-concept in MF can provide predictive gene signatures for therapeutic responses in systems pharmacology.