Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)

Algebraic shortcuts for leave-one-out cross-validation in supervised network inference




Julkaisun tekijät: Stock M, Pahikkala T, Airola A, Waegeman W, De Baets B

Kustantaja: Oxford University Press

Julkaisuvuosi: 2020

Journal: Briefings in Bioinformatics

Tietokannassa oleva lehden nimi: Briefings in bioinformatics

Lehden akronyymi: Brief Bioinform

Volyymi: 21

Julkaisunumero: 1

ISSN: 1467-5463

eISSN: 1477-4054

DOI: http://dx.doi.org/10.1093/bib/bby095

Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/37642437


Tiivistelmä
Supervised machine learning techniques have traditionally been very successful at reconstructing biological networks, such as protein-ligand interaction, protein-protein interaction and gene regulatory networks. Many supervised techniques for network prediction use linear models on a possibly nonlinear pairwise feature representation of edges. Recently, much emphasis has been placed on the correct evaluation of such supervised models. It is vital to distinguish between using a model to either predict new interactions in a given network or to predict interactions for a new vertex not present in the original network. This distinction matters because (i) the performance might dramatically differ between the prediction settings and (ii) tuning the model hyperparameters to obtain the best possible model depends on the setting of interest. Specific cross-validation schemes need to be used to assess the performance in such different prediction settings.In this work we discuss a state-of-the-art kernel-based network inference technique called two-step kernel ridge regression. We show that this regression model can be trained efficiently, with a time complexity scaling with the number of vertices rather than the number of edges. Furthermore, this framework leads to a series of cross-validation shortcuts that allow one to rapidly estimate the model performance for any relevant network prediction setting. This allows computational biologists to fully assess the capabilities of their models. The machine learning techniques with the algebraic shortcuts are implemented in the RLScore software package: https://github.com/aatapa/RLScore.

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Last updated on 2022-07-04 at 17:43