Devin C. Koestler

Orcid: 0000-0002-0598-0146

According to our database1, Devin C. Koestler authored at least 13 papers between 2010 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
optima: an open-source R package for the Tapestri platform for integrative single cell multiomics data analysis.
Bioinform., October, 2023

Simulating time-to-event data under the Cox proportional hazards model: assessing the performance of the non-parametric Flexible Hazards Method.
Frontiers Appl. Math. Stat., 2023

2022
Assessing equivalent and inverse change in genes between diverse experiments.
Frontiers Bioinform., 2022

A Novel Framework for the Identification of Reference DNA Methylation Libraries for Reference-Based Deconvolution of Cellular Mixtures.
Frontiers Bioinform., 2022

Letter to the Editor: on the stability and internal consistency of component-wise sparse mixture regression-based clustering.
Briefings Bioinform., 2022

2020
Improving Survival Prediction Using a Novel Feature Selection and Feature Reduction FrameworkBased on the Integration of Clinical and Molecular Data.
Proceedings of the Pacific Symposium on Biocomputing 2020, 2020

2019
pwrEWAS: a user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS).
BMC Bioinform., 2019

2018
Relevant Word Order Vectorization for Improved Natural Language Processing in Electronic Healthcare Records.
CoRR, 2018

Subject level clustering using a negative binomial model for small transcriptomic studies.
BMC Bioinform., 2018

2016
An ensemble-based Cox proportional hazards regression framework for predicting survival in metastatic castration-resistant prostate cancer (mCRPC) patients.
F1000Research, 2016

Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL).
BMC Bioinform., 2016

2012
DNA methylation arrays as surrogate measures of cell mixture distribution.
BMC Bioinform., 2012

2010
Semi-supervised recursively partitioned mixture models for identifying cancer subtypes.
Bioinform., 2010


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