Network-Guided Biomarker Discovery

@article{Azencott2016NetworkGuidedBD,
  title={Network-Guided Biomarker Discovery},
  author={Chlo{\'e}-Agathe Azencott},
  journal={ArXiv},
  year={2016},
  volume={abs/1607.08161}
}
Identifying measurable genetic indicators (or biomarkers) of a specific condition of a biological system is a key element of precision medicine. Indeed it allows to tailor diagnostic, prognostic and treatment choice to individual characteristics of a patient. In machine learning terms, biomarker discovery can be framed as a feature selection problem on whole-genome data sets. However, classical feature selection methods are usually underpowered to process these data sets, which contain orders… 
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References

SHOWING 1-10 OF 95 REFERENCES
The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures
TLDR
It is observed that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures, and a simple Student's t-test seems to provide the best results.
Stable Feature Selection for Biomarker Discovery
Network-guided regression for detecting associations between DNA methylation and gene expression
TLDR
Network-sparse Reduced-Rank Regression (NsRRR), a multivariate regression framework capable of using prior biological knowledge expressed as gene interaction networks to guide the search for associations between gene expression and DNA methylation signatures, is proposed.
Insights into colon cancer etiology via a regularized approach to gene set analysis of GWAS data.
Pathway and network-based analysis of genome-wide association studies in multiple sclerosis
TLDR
A pathway-oriented analysis of two GWAS in MS that takes into account all SNPs with nominal evidence of association (P < 0.05) and reports here for the first time the potential involvement of neural pathways in MS susceptibility.
Statistical Estimation of Correlated Genome Associations to a Quantitative Trait Network
TLDR
This study proposes a new statistical framework called graph-guided fused lasso (GFlasso) to directly and effectively incorporate the correlation structure of multiple quantitative traits such as clinical metrics and gene expressions in association analysis.
A Network-Based Approach to Prioritize Results from Genome-Wide Association Studies
TLDR
Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data, efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses.
Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps
TLDR
In a comparison study with an alternative pathways method based on univariate SNP statistics, this method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small.
Evaluation of Feature Ranking Ensembles for High-Dimensional Biomedical Data: A Case Study
TLDR
A case study consisting of 429 samples of exhaled air from smokers, 83% of whom suffer from COPD, and the t-statistic was rated the best among the 16 feature rankers, outperforming the currently favourite SVM ranker.
Identification of genes associated with multiple cancers via integrative analysis
TLDR
The Mc.TGD (Multi-cancer Threshold Gradient Descent), an integrative analysis approach capable of analyzing multiple microarray studies on different cancers, is proposed, which is the first regularized approach to conduct "two-dimensional" selection of genes with joint effects on cancer development.
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