scholarly journals Inferring robust gene networks from expression data by a sensitivity-based incremental evolution method

2012 ◽  
Vol 13 (S7) ◽  
Author(s):  
Yu-Ting Hsiao ◽  
Wei-Po Lee
Keyword(s):  
Gene Networks ◽  
Expression Data ◽  
Incremental Evolution

scholarly journals Discovering Causal Relationships in Grapevine Expression Data to Expand Gene Networks. A Case Study: Four Networks Related to Climate Change

2018 ◽  
Vol 9 ◽  
Author(s):  
Giulia Malacarne ◽  
Stefania Pilati ◽  
Samuel Valentini ◽  
Francesco Asnicar ◽  
Marco Moretto ◽  
...  
Keyword(s):  
Climate Change ◽  
Gene Networks ◽  
Expression Data ◽  
Causal Relationships

Building Gene Networks by Analyzing Gene Expression Profiles

2019 ◽  
pp. 27-44
Author(s):  
Crescenzio Gallo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Gene Expressions ◽  
Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

scholarly journals Space-log: a novel approach to inferring gene-gene net-works using SPACE model with log penalty

F1000Research ◽  
2022 ◽  
Vol 9 ◽  
pp. 1159
Author(s):  
Qian (Vicky) Wu ◽  
Wei Sun ◽  
Li Hsu
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Penalized Regression ◽  
R Package ◽  
Expression Data ◽  
Computationally Efficient ◽  
P Gene ◽  
Novel Approach

Gene expression data have been used to infer gene-gene networks (GGN) where an edge between two genes implies the conditional dependence of these two genes given all the other genes. Such gene-gene networks are of-ten referred to as gene regulatory networks since it may reveal expression regulation. Most of existing methods for identifying GGN employ penalized regression with L1 (lasso), L2 (ridge), or elastic net penalty, which spans the range of L1 to L2 penalty. However, for high dimensional gene expression data, a penalty that spans the range of L0 and L1 penalty, such as the log penalty, is often needed for variable selection consistency. Thus, we develop a novel method that em-ploys log penalty within the framework of an earlier network identification method space (Sparse PArtial Correlation Estimation), and implement it into a R package space-log. We show that the space-log is computationally efficient (source code implemented in C), and has good performance comparing with other methods, particularly for networks with hubs.Space-log is open source and available at GitHub, https://github.com/wuqian77/SpaceLog

Inferring Gene Networks using Robust Statistical Techniques

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Venkat R. Nadadoor ◽  
Amos Ben-Zvi ◽  
Sirish L. Shah
Keyword(s):  
Gene Networks ◽  
Gene Network ◽  
Information Criterion ◽  
Statistical Techniques ◽  
Connectivity Matrix ◽  
Expression Data ◽  
Statistical Tools ◽  
Linear Ordinary Differential Equations ◽  
Leave One Out ◽  
Novel Algorithm

Inference of gene networks is an important step in understanding cellular dynamics. In this work, a novel algorithm is proposed for inferring gene networks from gene expression data using linear ordinary differential equations. Under the proposed method, a combination of known statistical tools including partial least squares (PLS), leave-one-out jackknifing, and the Akaike information criterion (AIC) are used for robust estimation of gene connectivity matrix. The proposed approach is tested and validated using a computer simulated gene network model and an experimental data on a nine gene network in Eschericia coli.

Data Integration of Hybrid Microarray and Single Cell Expression Data to Enhance Gene Network Inference

2019 ◽  
Vol 14 (3) ◽  
pp. 255-268 ◽  
Author(s):  
Wei Zhang ◽  
Wenchao Li ◽  
Jianming Zhang ◽  
Ning Wang
Keyword(s):  
Single Cell ◽  
In Silico ◽  
Gene Networks ◽  
Network Inference ◽  
Performance Metrics ◽  
Information Source ◽  
Inference Algorithm ◽  
Expression Data ◽  
Complementary Information ◽  
Cell Expression

Background: Gene Regulatory Network (GRN) inference algorithms aim to explore casual interactions between genes and transcriptional factors. High-throughput transcriptomics data including DNA microarray and single cell expression data contain complementary information in network inference. Objective: To enhance GRN inference, data integration across various types of expression data becomes an economic and efficient solution. Method: In this paper, a novel E-alpha integration rule-based ensemble inference algorithm is proposed to merge complementary information from microarray and single cell expression data. This paper implements a Gradient Boosting Tree (GBT) inference algorithm to compute importance scores for candidate gene-gene pairs. The proposed E-alpha rule quantitatively evaluates the credibility levels of each information source and determines the final ranked list. Results: Two groups of in silico gene networks are applied to illustrate the effectiveness of the proposed E-alpha integration. Experimental outcomes with size50 and size100 in silico gene networks suggest that the proposed E-alpha rule significantly improves performance metrics compared with single information source. Conclusion: In GRN inference, the integration of hybrid expression data using E-alpha rule provides a feasible and efficient way to enhance performance metrics than solely increasing sample sizes.

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