论文标题
生物模型选择的最佳无可能方法
An Optimal Likelihood Free Method for Biological Model Selection
论文作者
论文摘要
Systems Biology试图创建生物系统的数学模型，以减少固有的生物学复杂性，并为治疗性开发等应用提供预测。但是，确定哪种数学模型正确以及如何最佳地到达答案仍然是一个挑战。我们提出了一种使用系统生物学和可能性无推理方法的数学模型选择自动生物学模型选择的算法。我们的算法在未经实验生物学和随机搜索中使用的常规启发式方法的情况下，表现出正确的模型的性能提高了。该方法显示有望加速生物基础科学和药物发现。
Systems biology seeks to create math models of biological systems to reduce inherent biological complexity and provide predictions for applications such as therapeutic development. However, it remains a challenge to determine which math model is correct and how to arrive optimally at the answer. We present an algorithm for automated biological model selection using mathematical models of systems biology and likelihood free inference methods. Our algorithm shows improved performance in arriving at correct models without a priori information over conventional heuristics used in experimental biology and random search. This method shows promise to accelerate biological basic science and drug discovery.
