Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.
Description
Enter the Matrix: Factorization Uncovers Knowledge from Omics. - PubMed - NCBI
%0 Journal Article
%1 SteinOBrien:2018:Trends-Genet:30143323
%A Stein-O'Brien, G L
%A Arora, R
%A Culhane, A C
%A Favorov, A V
%A Garmire, L X
%A Greene, C S
%A Goff, L A
%A Li, Y
%A Ngom, A
%A Ochs, M F
%A Xu, Y
%A Fertig, E J
%D 2018
%J Trends Genet
%K fulltext shouldread software statistics
%N 10
%P 790-805
%R 10.1016/j.tig.2018.07.003
%T Enter the Matrix: Factorization Uncovers Knowledge from Omics
%U https://www.ncbi.nlm.nih.gov/pubmed/30143323
%V 34
%X Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.
@article{SteinOBrien:2018:Trends-Genet:30143323,
abstract = {Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.},
added-at = {2020-01-22T22:25:23.000+0100},
author = {Stein-O'Brien, G L and Arora, R and Culhane, A C and Favorov, A V and Garmire, L X and Greene, C S and Goff, L A and Li, Y and Ngom, A and Ochs, M F and Xu, Y and Fertig, E J},
biburl = {https://www.bibsonomy.org/bibtex/25799183d8f47a605ffcac117c6de0e9c/marcsaric},
description = {Enter the Matrix: Factorization Uncovers Knowledge from Omics. - PubMed - NCBI},
doi = {10.1016/j.tig.2018.07.003},
interhash = {f9fcef0a219eb779f90e4a77608b368f},
intrahash = {5799183d8f47a605ffcac117c6de0e9c},
journal = {Trends Genet},
keywords = {fulltext shouldread software statistics},
month = {10},
number = 10,
pages = {790-805},
pmid = {30143323},
timestamp = {2020-01-22T22:25:23.000+0100},
title = {Enter the Matrix: Factorization Uncovers Knowledge from Omics},
url = {https://www.ncbi.nlm.nih.gov/pubmed/30143323},
volume = 34,
year = 2018
}