Journal ArticleDOI
BENIN: Biologically enhanced network inference
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TLDR
BENIN is a general framework that jointly considers different types of prior knowledge with expression datasets to improve the network inference and uses a popular penalized regression method, the Elastic net, combined with bootstrap resampling to solve it.Abstract:
Gene regulatory network inference is one of the central problems in computational biology. We need models that integrate the variety of data available in order to use their complementarity information to overcome the issues of noisy and limited data. BENIN: Biologically Enhanced Network INference is our proposal to integrate data and infer more accurate networks. BENIN is a general framework that jointly considers different types of prior knowledge with expression datasets to improve the network inference. The method states the network inference as a feature selection problem and uses a popular penalized regression method, the Elastic net, combined with bootstrap resampling to solve it. BENIN significantly outperforms the state-of-the-art methods on the simulated data from the DREAM 4 challenge when combining genome-wide location data, knockout gene expression data, and time series expression data.read more
Citations
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Proceedings ArticleDOI
Ensemble Regression Modelling for Genetic Network Inference
TL;DR: The proposed simple ensemble regression-based feature selection model is able to eliminate overfitting, multi co-linearity issues, and irrelevant genes within one computational approach, and outperformed other state-of-the-art methods, producing high true positives, reducing false positives, and obtaining high Structural Accuracy.
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Structure primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference
Andreas Tjärnberg,Maggie Beheler-Amass,Christopher A. Jackson,Lionel Christiaen,David Gresham,Richard Bonneau +5 more
TL;DR: Recently, this paper proposed a deep learning autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor), that scales to single cell genomic data and maintains interpretability.
Journal ArticleDOI
Introduction to the JBCB special issue on CSBio 2019.
Proceedings ArticleDOI
Ensemble Regression Modelling for Genetic Network Inference
TL;DR: In this paper , a simple ensemble regression-based feature selection model was proposed for reconstructing gene regulatory networks (GRNs) from time series gene expression data, which is able to eliminate overfitting, multi-linearity issues, and irrelevant genes within one computational approach.
Posted ContentDOI
dynUGENE: an R package for uncertainty-aware gene regulatory network inference, simulation, and visualization
TL;DR: Lu et al. as mentioned in this paper implemented an extension to the dynGENIE3 algorithm that accounts for model uncertainty as an R package, providing users with an easy to use interface for model selection and gene expression profile simulation.
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