Experimental-Theoretic Approach to Drug-Lymphocyte Interactome Networks with Flow Cytometry and Spectral Moments Perturbation Theory

Modelos matematicos y citometria de flujoAbstract: We can combine experimental techniques like Flow Cytometry Analysis (FCA) with Chemoinformatics methods to predict the complex networks of interactions between organic compounds and targets in the immune system. In this work, we determined experimentally the values of EC50 = 17.82 μg/mL and Cytotoxicity = 20.6 % for the antimicrobial / anti-parasite drug Dermofural over Balb/C CD9 lymphocytes using flow cytometry. After that, we developed a new Perturbation-theory model for Drug-Cell Target Interactome in Lymphocytes based on dispersion-polarization moments of drug structure. The models correctly classifies 34591 out of 42715 (Accuracy = 80.9%) cases of perturbations in assay endpoints of 11492 drugs (including both train and validation series). Each endpoint correspond to one out of 2616 assays, 38 molecular and cellular targets, 77 standard type measures, in four possible (human and rodentsCONACY

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