Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Frost, H. Robert"'
Autor:
Frost, H. Robert
We describe a set of network analysis methods based on the rows of the Krylov subspace matrix computed from a network adjacency matrix via power iteration using a non-random initial vector. We refer to these node-specific row vectors as Krylov subspa
Externí odkaz:
http://arxiv.org/abs/2403.01269
Autor:
Frost, H. Robert
We describe a family of iterative algorithms that involve the repeated execution of discrete and inverse discrete Fourier transforms. One interesting member of this family is motivated by the discrete Fourier transform uncertainty principle and invol
Externí odkaz:
http://arxiv.org/abs/2211.09284
Autor:
Frost, H. Robert
We present a novel approach for computing a variant of eigenvector centrality for multilayer networks with inter-layer constraints on node importance. Specifically, we consider a multilayer network defined by multiple edge-weighted, potentially direc
Externí odkaz:
http://arxiv.org/abs/2205.01478
Autor:
Schiebout, Courtney1 (AUTHOR) courtney.taylor.schiebout@dartmouth.edu, Frost, H. Robert1 (AUTHOR)
Publikováno v:
BMC Bioinformatics. 6/13/2024, Vol. 25 Issue 1, p1-16. 16p.
Autor:
Frost, H. Robert1 (AUTHOR) rob.frost@dartmouth.edu
Publikováno v:
PLoS Computational Biology. 4/29/2024, Vol. 20 Issue 4, p1-26. 26p.
Autor:
Frost, H. Robert
We present a novel technique for sparse principal component analysis. This method, named Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA), is based on the formula for computing squared eigenvector loadings of a Hermitian mat
Externí odkaz:
http://arxiv.org/abs/2006.01924
Autor:
Frost, H. Robert1 (AUTHOR) rob.frost@dartmouth.edu
Publikováno v:
PLoS Computational Biology. 1/11/2024, Vol. 20 Issue 1, p1-22. 22p.
Autor:
Javaid, Azka1 azka.javaid.gr@dartmouth.edu, Frost, H Robert1 azka.javaid.gr@dartmouth.edu
Publikováno v:
Bioinformatics Advances. 2023, Vol. 3 Issue 1, p1-12. 12p.
Publikováno v:
BMC Bioinformatics 2015, 16:70 (3 March 2015)
Motivation: Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absenc
Externí odkaz:
http://arxiv.org/abs/1405.3241
Publikováno v:
BioData Mining 2015, 8:25
Motivation: Although principal component analysis (PCA) is widely used for the dimensional reduction of biomedical data, interpretation of PCA results remains daunting. Most existing methods attempt to explain each principal component (PC) in terms o
Externí odkaz:
http://arxiv.org/abs/1403.5148