Open AccessA survey of some tensor analysis techniques for biological systems
Author(s) -
Yahyanejad Farzane,
Albert Réka,
DasGupta Bhaskar
Publication year - 2019
Publication title -
quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.1007/s40484-019-0186-5
Subject(s) - tensor (intrinsic definition) , pace , computer science , complex system , interpretation (philosophy) , theoretical computer science , data science , mathematics , artificial intelligence , physics , pure mathematics , astronomy , programming language
Background Since biological systems are complex and often involve multiple types of genomic relationships, tensor analysis methods can be utilized to elucidate these hidden complex relationships. There is a pressing need for this, as the interpretation of the results of high‐throughput experiments has advanced at a much slower pace than the accumulation of data. Results In this review we provide an overview of some tensor analysis methods for biological systems. Conclusions Tensors are natural and powerful generalizations of vectors and matrices to higher dimensions and play a fundamental role in physics, mathematics and many other areas. Tensor analysis methods can be used to provide the foundations of systematic approaches to distinguish significant higher order correlations among the elements of a complex systems via finding ensembles of a small number of reduced systems that provide a concise and representative summary of these correlations.