Petri网模型与非线性遗传疾病

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) Pub Date : 2010-11-29 DOI:10.1109/BICTA.2010.5645278

P. Glory, N. David, J. D. Emerald

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引用次数: 2

摘要

了解个体基因构成如何影响其患病风险，是一个至关重要的问题。虽然机器学习技术无法揭示基因型和疾病之间的关系，但我们仍然可以在识别导致遗传疾病的人群中DNA序列变异的方法的帮助下自动构建最佳的生化模型。在本文中，我们研究的Petri网模型在一定程度上是生化合理的，它可能揭示人类实际生化途径的特征，有助于了解疾病。

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Petri net models and non linear genetic diseases

Understanding how an individual genetic make-up influences their risk of diseases, is a problem of paramount importance. Although machine-learning techniques are unable to uncover the relationships between genotype and disease, we can still build the best biochemical model automatically with the help of methods that identify the DNA sequence variations in human populations that cause genetic diseases. In this paper, we study Petri net model that is bio chemically plausible to a certain degree, that it may reveal characteristics of the actual biochemical pathways in humans that can aid understanding of the disease.

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2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)

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