@inproceedings{d014e2433b404501a8e4ed2bc88e0106,
title = "Clusters identification in binary genomic data: The alternative offered by scan statistics approach",
abstract = "In many different research area, identification of clusters or regions showing an increment in event rate over a given study area is an important and interesting problem. Nowadays literature concerning scan statistics is quite broad and methods can be subdivided based on dimensional complexity of the study area, assumption on distribution generating the data under the null hypothesis and shape-dimension of the scanning window. The aim of this study is to adapt and apply this methodology to the genomics field taking into account for some peculiarities of these data and to compare its performance to existing method based on DBSCAN algorithm.",
keywords = "Binary genomic event, Hotspot, Scan statistics",
author = "Danilo Pellin and {Di Serio}, Clelia",
year = "2014",
doi = "10.1007/978-3-319-09042-9_11",
language = "English",
isbn = "9783319090412",
volume = "8452 LNBI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "149--158",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
note = "10th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2013 ; Conference date: 20-06-2013 Through 22-06-2013",
}