Classification of multicolor fluorescence in-situ hybridization (M-FISH) image using regularized multinomial logistic regression

Authors:
Jingyao Li;Hongbao Cao;Yu-Ping Wang
Affiliations:
Tulane University, New Orleans, LA;Tulane University, New Orleans, LA;Tulane University, New Orleans, LA
Venue:
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Year:
2012

Citing 3
Cited 0

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Abstract

In this paper, we applied a regularized multinomial logistic regression (RMLR) for multicolor fluorescence in-situ hybridization (M-FISH) image analysis, in order to better classify chromosomes. The RMLR integrates complementary information from multi-channel M-FISH images and considers the relationship of these data between different channels. We compared the model with two other regression models, e.g., multinomial logistic regression (MLR) and sparse multinomial logistic regression (SMLR). We show that the correct classification ratio of chromosomal region by the RMLR model is almost 93%, compared with 90% and 76% by the MLR and SMLR model when tested in a comprehensive M-FISH image database that we established and the p-value of these three models indicating that the RMLR model can significantly improve the accuracy of M-FISH image analysis.