Search Results for author: John Quackenbush

Found 6 papers, 2 papers with code

Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification

no code implementations NeurIPS 2021 Alkis Gotovos, Rebekka Burkholz, John Quackenbush, Stefanie Jegelka

Modeling the time evolution of discrete sets of items (e. g., genetic mutations) is a fundamental problem in many biomedical applications.

DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks

1 code implementation4 Apr 2021 Katherine H. Shutta, Deborah Weighill, Rebekka Burkholz, Marouen Ben Guebila, Dawn L. DeMeo, Helena U. Zacharias, John Quackenbush, Michael Altenbuchinger

The increasing quantity of multi-omics data, such as methylomic and transcriptomic profiles, collected on the same specimen, or even on the same cell, provide a unique opportunity to explore the complex interactions that define cell phenotype and govern cellular responses to perturbations.

Gene targeting in disease networks

no code implementations11 Jan 2021 Deborah Weighill, Marouen Ben Guebila, Kimberly Glass, John Platig, Jen Jen Yeh, John Quackenbush

This example demonstrates that gene targeting scores are an invaluable addition to gene expression analysis in the characterization of diseases and other complex phenotypes.

Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently

no code implementations9 Sep 2019 Rebekka Burkholz, John Quackenbush

Cascade models are central to understanding, predicting, and controlling epidemic spreading and information propagation.


Estimating sample-specific regulatory networks

3 code implementations24 May 2015 Marieke Lydia Kuijjer, Matthew Tung, Guo-Cheng Yuan, John Quackenbush, Kimberly Glass

We demonstrate the accuracy and applicability of our approach in several data sets, including simulated data, microarray expression data from synchronized yeast cells, and RNA-seq data collected from human lymphoblastoid cell lines.

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