Search Results for author: Joshua Cape

Found 7 papers, 3 papers with code

A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS

no code implementations17 Mar 2023 Weiqiong Huang, Emily C. Hector, Joshua Cape, Chris McKennan

The recent explosion of genetic and high dimensional biobank and 'omic' data has provided researchers with the opportunity to investigate the shared genetic origin (pleiotropy) of hundreds to thousands of related phenotypes.

Bayesian Inference

Spectral embedding and the latent geometry of multipartite networks

1 code implementation8 Feb 2022 Alexander Modell, Ian Gallagher, Joshua Cape, Patrick Rubin-Delanchy

Spectral embedding finds vector representations of the nodes of a network, based on the eigenvectors of its adjacency or Laplacian matrix, and has found applications throughout the sciences.

Multiple Network Embedding for Anomaly Detection in Time Series of Graphs

1 code implementation23 Aug 2020 Guodong Chen, Jesús Arroyo, Avanti Athreya, Joshua Cape, Joshua T. Vogelstein, Youngser Park, Chris White, Jonathan Larson, Weiwei Yang, Carey E. Priebe

We examine two related, complementary inference tasks: the detection of anomalous graphs within a time series, and the detection of temporally anomalous vertices.


On spectral algorithms for community detection in stochastic blockmodel graphs with vertex covariates

1 code implementation4 Jul 2020 Cong Mu, Angelo Mele, Lingxin Hao, Joshua Cape, Avanti Athreya, Carey E. Priebe

In network inference applications, it is often desirable to detect community structure, namely to cluster vertices into groups, or blocks, according to some measure of similarity.

Clustering Community Detection

Spectral inference for large Stochastic Blockmodels with nodal covariates

no code implementations18 Aug 2019 Angelo Mele, Lingxin Hao, Joshua Cape, Carey E. Priebe

In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure.

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