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    You are here: Home Softwares Mode-of-action by Network Inference MNI

    Mode-of-action by Network Inference MNI

    The manuscript describing MNI can be downloaded here:

    • Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks Diego di Bernardo, Michael J Thompson, Timothy S Gardner, Sarah E Chobot, Erin L Eastwood, Andrew P Wojtovich, Sean J Elliott, Scott E Schaus, James J Collins. Nature Biotechnology; 23,377 - 383, 2005. abstract - PDF
    • The mode-of-action by network identification (MNI) algorithm: a network biology approach for molecular target identification. Xing H, Gardner TS. Nat Protoc. 2006;1(6):2551-4. PDF


    The mode-of-action by network identification (MNI) is an algorithm to identify the gene targets of a drug treatment based on gene-expression data. In a typical use of the algorithm, a single expression profile, say obtained as a result of a treatment under study, is used as the test profile while a set of hundreds of expression profiles is used as the training set. The MNI algorithm uses the large training data set of expression profiles to construct a statistical model of gene-regulatory networks in a cell or tissue. The model describes combinatorial influences of genes on one another. The algorithm then uses the model to filter the expression profile of the treatment under study and thereby distinguish the molecular targets or mediators of the treatment response from hundreds of additional genes that also exhibit expression changes.

    Below is the command-line to run MNI and a short description of its arguments:

    [rank_idx, mean_zscr] = mni_v2(DATA, sDATA, TESTX, sTESTX, ITER, thP, Q, MODZ, NROUNDS, KEEPFRAC, CHK_CONVERGE)

    DATA: matrix that contains the experimental expression values; with dimensions N x M corresponding to number of genes x number of experiments;
    sDATA: matrix that contains the standard deviation of each observed value in DATA; it has the same dimensions as DATA;
    TESTX: array containing the test experiment, of dimensions N x 1;
    sTESTX: array that contains the standard deviation of each observed value in TESTX; it has the same dimensions as TESTX;
    ITER: number of iterations in the mnicore algorithm (not necessary; default value = 5);
    thP: threshold for selection of "perturbed" gene (not necessary: default value = 0.25);
    Q: number of reduced dimensions (not necessary; default value = 50);
    MODZ: if MODZ flag = 1, use modified Z score for ranking (not necessary; default value = 1);
    NROUNDS: number of rounds in tournament (not necessary; default value = 3);
    KEEPFRAC: number of genes to keep in each round of tournament (not necessary; default value = 1/3);
    CHK_CONVERGE: if CHK_CONVERGE = 1, check for convergence in mnicore (not necessary; default value = 0);

    The MNI matlab cobe can be dowloaded here

    This software is distributed only for non-commercial purposes and only for acedemic use - Commercial users please contact Diego di Bernardo (