A maximum flow-based network approach for identification of stable noncoding biomarkers associated with the multigenic neurological condition, autism

Background: Machine learning approaches for predicting disease risk from high-dimensional whole genome sequence (WGS) data often result in unstable models that can be difficult to interpret, limiting the identification of putative sets of biomarkers. Here, we design and validate a graph-based method...

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Bibliographic Details
Main Authors:	Chrisman, B.S (Author), Jung, J.-Y (Author), Paskov, K.M (Author), Stockham, N.T (Author), Sun, M.W (Author), Varma, M. (Author), Wall, D.P (Author), Washington, P.Y (Author)
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2021
Subjects:	ADAM12 gene ADAM12 protein algorithm Article autism biological marker controlled study disease association DNA structure Feature selection Feature stability gene gene expression gene linkage disequilibrium genetic association genetic variability human linear support vector classification with L1 regularization linear support vector machine Linkage disequilibrium logistic regression analysis logistic regression with elastic net regularization logistic regression with L1 regularization machine learning Machine learning major clinical study Maximum flow maximum flow based network approach measurement accuracy methodology Network neurologic disease prediction priority journal protein expression simple repeat sequence simulation untranslated RNA
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View Fulltext in Publisher

A maximum flow-based network approach for identification of stable noncoding biomarkers associated with the multigenic neurological condition, autism

Internet

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