Sökning: "GRNs"

Hittade 3 uppsatser innehållade ordet GRNs.

  1. 1. Imputing connections of random gene networks from time series data using ANNs

    Master-uppsats, Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisation

    Författare :Sofia Andersson; [2022]
    Nyckelord :artificial neural networks; ANNs; gene regulatory networks; GRNs; imputation; gene regulatory network imputation; GRN imputation; CNNs; convolutional neural network; randomly generated networks; ternary classification; binary classification; network inference; Physics and Astronomy;

    Sammanfattning : This thesis presents the architecture of a convolutional neural network which is trained to impute the connections of randomly generated gene regulatory networks under varying amounts of regularisation. The generated gene networks are simulated from 10 different starting conditions for each set of connections in order to obtain multiple time series. LÄS MER

  3. 2. Robust Community Predictions of Hubs in Gene Regulatory Networks

    Master-uppsats, Linköpings universitet/Bioinformatik

    Författare :Julia Åkesson; [2018]
    Nyckelord :Biological systems; Biological networks; Network inference; Gene regulatory networks; Hubs; Master regulators; Community predictions; Bioinformatics;

    Sammanfattning : Many diseases, such as cardiovascular diseases, cancer and diabetes, originate from several malfunctions in biological systems. The human body is regulated by a wide range of biological systems, composed of biological entities interacting in complex networks, responsible for carrying out specific functions. LÄS MER

  4. 3. Parameter optimization of linear ordinary differential equations with application in gene regulatory network inference problems

    Master-uppsats, KTH/Numerisk analys, NA

    Författare :Yue Deng; [2014]
    Nyckelord :Ordinary differential equations; parameter optimization; gene regulatory network inference; DREAM4 project;

    Sammanfattning : In this thesis we analyze parameter optimization problems governed by linear ordinary differential equations (ODEs) and develop computationally efficient numerical methods for their solution. In addition, a series of noise-robust finite difference formulas are given for the estimation of the derivatives in the ODEs. LÄS MER