@inproceedings{085efb71b88e435ea35f557cbe2ef9ee,
title = "exML: An Explainable Maximum Likelihood Tool for Proportion Estimation in DNA Data",
abstract = "Estimating proportions of elements in a given set is a key problem in multiple scenarios. A particular use case of interest is the analysis of ancient DNA, where the goal is to estimate the proportion of species in a set of DNA reads extracted from sediments in archaeological sites. While there is a plethora of existing solutions for this type of problem, they lack explainability, which leads to challenges in their debugging and deployment as well as in downstream analysis tasks. To this end, we have developed exML, a Maximum Likelihood Estimator equipped with novel explanation methods. We propose to demonstrate exML in the context of analyzing ancient DNA samples. We will show use cases where the explanations generated by exML provide insights on otherwise ambiguous classification results.",
keywords = "Shapley value, ancient DNA, computational genomics, explainability, maximum likelihood",
author = "Amit Bergman and Viviane Slon and Daniel Deutch",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 31st ACM International Conference on Information and Knowledge Management, CIKM 2022 ; Conference date: 17-10-2022 Through 21-10-2022",
year = "2022",
month = oct,
day = "17",
doi = "10.1145/3511808.3557156",
language = "אנגלית",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",
pages = "4818--4822",
booktitle = "CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management",
}