NDLI: A Bayesian Approach to Determine the Composition of Heterogeneous Cancer Tissue

Content Provider	ACM Digital Library
Author	Lopes, Rosana Mohanty, Anwoy Sima, Chao Bittner, Michael L. Hua, Jianping Katiyar, Ashish Datta, Aniruddha
Abstract	Cancer Tissue Heterogeneity is an important consideration in cancer research as it can give insights into the causes and progression of cancer. It is known to play a significant role in cancer cell survival, growth and metastasis. Determining the compositional breakup of a heterogeneous cancer tissue can also help address the therapeutic challenges posed by heterogeneity. This necessitates a low cost, scalable algorithm to address the challenge of accurate estimation of the composition of a heterogeneous cancer tissue. In this paper, we propose an algorithm to tackle this problem by utilizing the data of accurate, but high cost, single cell line cell-by-cell observation methods in low cost ensemble observation method for heterogeneous cancer cell mixtures to obtain their composition in a Bayesian framework. The algorithm is analyzed and validated using synthetic data and experimental data obtained from mixtures of cancer cell lines.
Starting Page	773
Ending Page	774
Page Count	2
File Format	PDF
ISBN	9781450347228
DOI	10.1145/3107411.3110410
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Metropolis Bayesian modeling Cancer tissue heterogeneity Kernel density estimation
Content Type	Text
Resource Type	Article

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A Bayesian Approach to Determine the Composition of Heterogeneous Cancer Tissue