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Increased peak detection accuracy in over-dispersed ChIP-seq data with
  supervised segmentation models
v1v2 (latest)

Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models

BMC Bioinformatics (BB), 2020
12 December 2020
Arnaud Liehrmann
G. Rigaill
T. Hocking
ArXiv (abs)PDFHTML

Papers citing "Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models"

5 / 5 papers shown
Online Multivariate Changepoint Detection: Leveraging Links With
  Computational Geometry
Online Multivariate Changepoint Detection: Leveraging Links With Computational Geometry
Liudmila Pishchagina
Gaetano Romano
Paul Fearnhead
Vincent Runge
G. Rigaill
315
5
0
02 Nov 2023
Geometric-Based Pruning Rules For Change Point Detection in Multiple
  Independent Time Series
Geometric-Based Pruning Rules For Change Point Detection in Multiple Independent Time Series
Liudmila Pishchagina
G. Rigaill
Vincent Runge
225
3
0
15 Jun 2023
Automatic Change-Point Detection in Time Series via Deep Learning
Automatic Change-Point Detection in Time Series via Deep Learning
Jie Li
Paul Fearnhead
Piotr Fryzlewicz
Teng Wang
AI4TS
263
37
0
07 Nov 2022
Functional Labeled Optimal Partitioning
Functional Labeled Optimal PartitioningJournal of Computational And Graphical Statistics (JCGS), 2022
T. Hocking
Jacob M. Kaufman
Alyssa J. Stenberg
198
0
0
05 Oct 2022
Optimizing ROC Curves with a Sort-Based Surrogate Loss Function for
  Binary Classification and Changepoint Detection
Optimizing ROC Curves with a Sort-Based Surrogate Loss Function for Binary Classification and Changepoint Detection
J. Hillman
T. Hocking
210
4
0
02 Jul 2021
1
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