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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Optimal cancer prognosis under network uncertainty.

Mohammadmahdi R Yousefi1, Lori A Dalton1,2

  • 1Department of Electrical and Computer Engineering, The Ohio State University, Columbus, 43210 OH USA.

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|February 15, 2017
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Predicting cancer prognosis is challenging due to evolving cancer networks. This study shows that even with ideal conditions, network uncertainty can make cancer prognosis prediction unreliable.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Cancer Research

Background:

  • Accurate cancer prognosis requires understanding cell dynamics, healthy cell regulation, and cancer progression.
  • Existing models often overlook the uncertainty and evolving nature of cancer networks.
  • Network uncertainty significantly impacts the reliability of predicting disease progression and treatment outcomes.

Purpose of the Study:

  • To investigate the impact of gene regulatory network uncertainty on cancer prognosis prediction.
  • To analyze cancer prognosis under conditions of unknown aberrant gene relationships within a defined class of mutations.
  • To assess the reliability of prognosis prediction given patient gene activity profiles and probabilistic network information.

Main Methods:

  • Utilizing optimal control strategies for probabilistic Boolean networks.
  • Applying optimal Bayesian classification techniques.
  • Analyzing patient gene activity profiles against a class of possible mutated networks.

Main Results:

  • Demonstrated that prognosis prediction can be highly unreliable.
  • Showcased unreliability even under optimistic assumptions of known healthy processes and ideal treatments.
  • Highlighted the significant challenge posed by network uncertainty in cancer prognosis.

Conclusions:

  • Gene regulatory network uncertainty poses a fundamental challenge to accurate cancer prognosis.
  • Even with complete knowledge of healthy systems and optimal treatments, unreliable predictions can occur.
  • Further research is needed to address network uncertainty for improved cancer outcome prediction.