Reproducibility Of Parameter Learning With Missing Observations in Naive Wnt Bayesian Network Trained on Normal/Adenomas Samples and Doxycycline Treated LS174T Cell Lines

BioRxiv : the Preprint Server for Biology
Shriprakash Sinha

Abstract

Insight, Innovation and Integration Doxycycline, a derivative of tetracycline, induces gene expression via reversible transcriptional activation. Levels of /3-catenin and other intra/extracellular genetic factors have been influenced in colorectal cancer cell lines, which make doxycycline a potential candidate for cancer chemotherapy. With the aim to build better computational models that show good prediction on test datasets, doxycycline treated cell lines might provide best training samples. This work tests the reproducibility of parameter learning and predictions based on the estimated parameters, using the Naive Bayesian Networks for Wnt pathway in case of missing observations for different nodes. The in silico experiments show the efficacy of causal models as one of the emerging diagnostic tools in development of targeted cancer therapy. Recent efforts in predicting Wnt signaling activation via inference methods have helped in developing diagnostic models for therapeutic drug targeting. In this manuscript the reproducibility of parameter learning with missing observations in a Bayesian Network and its effect on prediction results for Wnt signaling activation is tested, while training the networks on doxycycline treated LS...Continue Reading

Related Concepts

Doxycycline
Extracellular
WNT1
Adenoma
Gene Expression
Administration of Antineoplastic Agent
Neural Network Simulation
Wnt Signaling Pathway
Cancer Treatment
Pharmacologic Substance

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