Biclustering Gene Expressions Using Factor Graphs and the Max-Sum Algorithm
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 925-930.
Abstract
Biclustering is an intrinsically challenging and highly complex problem, particularly studied in the biology field, where the goal is to simultaneously cluster genes and samples of an expression data matrix. In this paper we present a novel approach to gene expression biclustering by providing a binary Factor Graph formulation to such problem. In more detail, we reformulate biclustering as a sequential search for single biclusters and use an efficient optimization procedure based on the Max Sum algorithm. Such approach, drastically alleviates the scaling issues of previous approaches for biclustering based on Factor Graphs obtaining significantly more accurate results on synthetic datasets. A further analysis on two real-world datasets confirms the potentials of the proposed methodology when compared to alternative state of the art methods.
@inproceedings{ijcai2015p135,
title = {Biclustering Gene Expressions Using Factor Graphs and the Max-Sum Algorithm},
author = {Denitto, Matteo and Farinelli, Alessandro and Bicego, Manuele},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {925--930},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/135},
}
TY - CPAPER TI - Biclustering Gene Expressions Using Factor Graphs and the Max-Sum Algorithm AU - Denitto, Matteo AU - Farinelli, Alessandro AU - Bicego, Manuele T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 925 EP - 930 UR - https://ijcai.org/proceedings/2015/135 ER -Copied
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