PT  - JOURNAL ARTICLE
AU  - Michiel Stock
AU  - Tapio Pahikkala
AU  - Antti Airola
AU  - Willem Waegeman
AU  - Bernard De Baets
TI  - Algebraic Shortcuts for Leave-One-Out Cross-Validation in Supervised Network Inference
AID  - 10.1101/242321
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 242321
4099  - http://biorxiv.org/content/early/2018/01/03/242321.1.short
4100  - http://biorxiv.org/content/early/2018/01/03/242321.1.full
AB  - Motivation 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. Recently, much emphasis has been placed on the correct evaluation of such supervised models. It is vital to distinguish between using the model to either predict new interactions in a given network or to predict interactions for a new vertex not present in the original network. Specific cross-validation schemes need to be used to assess the performance in such different prediction settings.Results We present a series of leave-one-out cross-validation shortcuts to rapidly estimate the performance of state-of-the-art kernel-based network inference techniques.Availability The machine learning techniques with the algebraic shortcuts are implemented in the RLScore software package.