A comprehensive comparison of association estimators for gene network inference algorithms
Zeyneb Kurt1, Nizamettin Aydin1, Gökmen Altay1
1Department of Computer Engineering, Yildiz Technical University, Davutpasa, 34220 Esenler, Istanbul, Turkey and Department of Biomedical Engineering, Bahcesehir University, 34349 Besiktas, Istanbul, Turkey.
Bioinformatics (Oxford, England)
|April 15, 2014
Summary
This study evaluated 14 gene network inference estimators, finding B-spline, Pearson-based Gaussian, and Spearman-based Gaussian methods optimal. Copula Transform pre-processing generally improved inference performance across datasets.
Area of Science:
- Bioinformatics
- Computational Biology
- Systems Biology
Background:
- Gene network inference (GNI) algorithms reveal gene and gene product interactions.
- Estimating gene association scores is crucial for GNI, but no single best estimator is universally accepted.
- This study addresses the need for a comprehensive evaluation of GNI estimators.
Purpose of the Study:
- To review and evaluate 27 different interaction estimators for GNI applications.
- To assess the performance of 14 selected estimators using popular GNI algorithms.
- To investigate the impact of Copula Transform (CT) pre-processing on estimator performance.
Main Methods:
- Reviewed 27 interaction estimators and selected 14 for evaluation.
- Applied selected estimators with three GNI algorithms to synthetic and real biological datasets (E. coli, S. cerevisiae).
- Analyzed the influence of Copula Transform (CT) pre-processing on estimator performance.
Main Results:
- B-spline, Pearson-based Gaussian, and Spearman-based Gaussian estimators demonstrated superior performance and runtime.
- Copula Transform (CT) pre-processing generally enhanced inference performance, particularly for synthetic datasets.
- Detailed experimental results and discussions are provided for all evaluated estimators.
Conclusions:
- Identified optimal association score estimators for gene network inference.
- Highlighted the benefits of Copula Transform pre-processing for improving GNI accuracy.
- Provided guidance for researchers selecting GNI methods and estimators.
More Related Videos
Related Concept Videos
02:26Comparing Copy Number Variations and SNPs
11.6K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
11.6K
02:26Protein Networks
3.7K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
3.7K
02:26Protein Networks
1.8K
1.8K
01:11Genome-wide Association Studies-GWAS
12.6K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
GWAS does not require the identification of the target gene involved in...
12.6K
01:24One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
1.5K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
1.5K
02:54Evolutionary Relationships through Genome Comparisons
5.8K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
5.8K