31 citations · 31 across the 3 of their papers we have counts for
4 papers
The C-SHIFT algorithm for normalizing covariances
Evgenia Chunikhina, Paul Logan, Yevgeniy Kovchegov +3
Omics technologies are powerful tools for analyzing patterns in gene expression data for thousands of genes. Due to a number of systematic variations in experiments, the raw gene e…
Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions
Richard R. Rodrigues, Natalia Shulzhenko, Andrey Morgun
Improvements in sequencing technologies and reduced experimental costs have resulted in a vast number of studies generating high-throughput data. Although the number of methods to…
Reverse enGENEering of regulatory networks from Big Data: a guide for a biologist
Xiaoxi Dong, Anatoly Yambartsev, Stephen Ramsey +3
Omics technologies enable unbiased investigation of biological systems through massively parallel sequence acquisition or molecular measurements, bringing the life sciences into th…
Unexpected links reflect the noise in networks
Anatoly Yambartsev, Michael Perlin, Yevgeniy Kovchegov +4
Gene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially conside…