Semi-supervised network inference using simulated gene expression dynamics

  title={Semi-supervised network inference using simulated gene expression dynamics},
  author={Phan Trung Hai Nguyen and Rosemary Braun},
Motivation Inferring the structure of gene regulatory networks from high-throughput datasets remains an important and unsolved problem. Current methods are hampered by problems such as noise, low sample size, and incomplete characterizations of regulatory dynamics, leading to networks with missing and anomalous links. Integration of prior network information (e.g. from pathway databases) has the potential to improve reconstructions. Results We developed a semi-supervised network… 

Figures from this paper

Experimental Guidance for Discovering Genetic Networks through Iterative Hypothesis Reduction on Time Series
This work introduces an approach of iterative network hypothesis reduction from time-series data in which increasingly complex features of the dynamic expression of individual, pairs, and entire collections of genes are used to infer functional network models that can produce the observed transcriptional program.
Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects
This review has reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks, and discussed the applications of these approaches in biomedical research.
Improving glomerular filtration rate estimation by semi-supervised learning: a development and external validation study
The hypothesis that semi-supervised learning technology could improve glomerular filtration rate estimation performance is supported by head-to-head comparisons and consistently achieved superior results in all three performance indicators compared with corresponding revised CKD-EPI equations in the external validation data set.
Statistical methods for the network-based analysis of genomic data
This paper presents statistical methods for the network-Based Analysis of Genomic Data that show clear trends in the number of hits on the ULTIMATE sequences over time and in the strength of the relationships between these hits and the values in the data.
Time-lagged Ordered Lasso for network inference
A semi-supervised method that embeds prior network information into the Ordered Lasso to discover novel regulatory dependencies in existing pathways is developed and evaluated on simulated data for a repressilator, time-course data from past DREAM challenges, and a HeLa cell cycle dataset to show that they can produce accurate networks subject to the dynamics and assumptions of the time-lagged Ordering Lasso regression.


Reconstructing biological networks using conditional correlation analysis
A network reconstruction algorithm based on the conditional correlation of the mRNA equilibrium concentration between two genes given that one of them was knocked down is proposed, which can reconstruct networks with the topology of the transcriptional regulatory network in Escherichia coli with a reasonably low error rate.
Revealing strengths and weaknesses of methods for gene network inference
The results of this community-wide experiment show that reliable network inference from gene expression data remains an unsolved problem, and they indicate potential ways of network reconstruction improvements.
A fast and efficient gene-network reconstruction method from multiple over-expression experiments
A novel algorithm for gene network reconstruction on the basis of steady-state gene-chip data from over-expression experiments, based on a straight forward solution of a linear gene-dynamics equation, which can be used in principle for large networks.
TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach
This paper shows how the ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) algorithm can be used for gene regulatory network inference in the case of time-course expression profiles and proposes an approach to detect dependencies between genes at different time delays starting from a well known algorithm based on information theory.
ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context
This approach should enhance the ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
This article presents GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge and compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli.
Gene networks inference using dynamic Bayesian networks
This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach that can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement.
Review on statistical methods for gene network reconstruction using expression data.
Addressing false discoveries in network inference
A confidence score recalibration method (CoRe) is suggested that reduces the false discovery rate and enables a reliable performance estimation and considerably improves the results of network inference methods that exploit known targets.
Regularization and Noise Injection for Improving Genetic Network Models
This chapter presents an approach that can provide rough estimates of the underlying genetic network based on limited time-course gene expression data by employing the fact that gene expression measurements are relatively noisy and genetic networks are thought to be robust.