Open AccessHigh dimensionality reduction by matrix factorization for systems pharmacology
Author(s) -
Adel Mehrpooya,
Farid Saberi-Movahed,
Najmeh Azizizadeh,
Mohammad Rezaei-Ravari,
Farshad Saberi-Movahed,
Mohammad Eftekhari,
Iman Tavassoly
Publication year - 2021
Publication title -
briefings in bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.204
H-Index - 113
eISSN - 1477-4054
pISSN - 1467-5463
DOI - 10.1093/bib/bbab410
Subject(s) - dimensionality reduction , computer science , feature selection , matrix decomposition , feature (linguistics) , artificial intelligence , curse of dimensionality , computational biology , pattern recognition (psychology) , machine learning , biology , linguistics , eigenvalues and eigenvectors , physics , philosophy , quantum mechanics
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 MF 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 treatment response in the Cancer Cell Line Encyclopedia.