2024-09-10-eliades24a.md

title	booktitle	year	volume	series	month	publisher	pdf	url	abstract	layout	issn	id	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	address	container-title	genre	issued	extras
Reliable Change Point Detection for ACGH data	Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications	2024	230	Proceedings of Machine Learning Research	0	PMLR	https://raw.githubusercontent.com/mlresearch/v230/main/assets/eliades24a/eliades24a.pdf	https://proceedings.mlr.press/v230/eliades24a.html	This study introduces two algorithms based on the Inductive Conformal Martingale (ICM) approach to address the change point (CP) detection problem in array-based Comparative Genomic Hybridization (aCGH) data. The ICM, a distribution-free approach with minimal assumptions, is particularly suitable for this application. We have implemented two ICM-based algorithms; the first utilizes nonconformities from preprocessed data, while the second incorporates the label conditional distribution and the labels’ distribution to enhance detection accuracy. This approach significantly improves our results, demonstrating the potential of ICM in complex genomic data analysis.	inproceedings	2640-3498	eliades24a	Reliable Change Point Detection for ACGH data	387	405	387-405	387	false	Vantini, Simone and Fontana, Matteo and Solari, Aldo and Bostr\"{o}m, Henrik and Carlsson, Lars	given family Simone Vantini given family Matteo Fontana given family Aldo Solari given family Henrik Boström given family Lars Carlsson	Eliades, Charalambos and Papadopoulos, Harris	given family Charalambos Eliades given family Harris Papadopoulos	2024-09-10		Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications	inproceedings	date-parts 2024 9 10