Search Results for author: Natalie Stanley

Found 5 papers, 3 papers with code

Distribution-based Sketching of Single-Cell Samples

1 code implementation30 Jun 2022 Vishal Athreya Baskaran, Jolene Ranek, Siyuan Shan, Natalie Stanley, Junier B. Oliva

Modern high-throughput single-cell immune profiling technologies, such as flow and mass cytometry and single-cell RNA sequencing can readily measure the expression of a large number of protein or gene features across the millions of cells in a multi-patient cohort.

Transparent Single-Cell Set Classification with Kernel Mean Embeddings

1 code implementation18 Jan 2022 Siyuan Shan, Vishal Baskaran, Haidong Yi, Jolene Ranek, Natalie Stanley, Junier Oliva

Each profiled biological sample is thus represented by a set of hundreds of thousands of multidimensional cell feature vectors, which incurs a high computational cost to predict each biological sample's associated phenotype with machine learning models.

BIG-bench Machine Learning Classification

Stochastic Block Models with Multiple Continuous Attributes

no code implementations7 Mar 2018 Natalie Stanley, Thomas Bonacci, Roland Kwitt, Marc Niethammer, Peter J. Mucha

While there are recent examples in the literature that combine connectivity and attribute information to inform community detection, our model is the first augmented stochastic block model to handle multiple continuous attributes.

Collaborative Filtering Community Detection +2

Compressing networks with super nodes

1 code implementation13 Jun 2017 Natalie Stanley, Roland Kwitt, Marc Niethammer, Peter J. Mucha

Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns.

Social and Information Networks Physics and Society

Clustering Network Layers With the Strata Multilayer Stochastic Block Model

no code implementations7 Jul 2015 Natalie Stanley, Saray Shai, Dane Taylor, Peter J. Mucha

While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes.

Stochastic Block Model

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