6,751 research outputs found
CLP-based protein fragment assembly
The paper investigates a novel approach, based on Constraint Logic
Programming (CLP), to predict the 3D conformation of a protein via fragments
assembly. The fragments are extracted by a preprocessor-also developed for this
work- from a database of known protein structures that clusters and classifies
the fragments according to similarity and frequency. The problem of assembling
fragments into a complete conformation is mapped to a constraint solving
problem and solved using CLP. The constraint-based model uses a medium
discretization degree Ca-side chain centroid protein model that offers
efficiency and a good approximation for space filling. The approach adapts
existing energy models to the protein representation used and applies a large
neighboring search strategy. The results shows the feasibility and efficiency
of the method. The declarative nature of the solution allows to include future
extensions, e.g., different size fragments for better accuracy.Comment: special issue dedicated to ICLP 201
Algebraic shortcuts for leave-one-out cross-validation in supervised network inference
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
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