Radiation Oncology, Massachusetts General Hospital, Boston 02114, MA, USA.
Bioinformatics. 2017 Nov 15;33(22):3610-3618. doi: 10.1093/bioinformatics/btx438.
Our overall goal is to develop machine-learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available.
The simulations are based on Boolean networks-directed graphs with 0/1 node states and logical node update rules-which are the simplest computational systems that can mimic the dynamic behavior of cellular systems. Boolean networks can be generated and simulated at scale, have complex yet cyclical dynamics and as such provide a useful framework for developing machine-learning algorithms for modular and hierarchical networks such as biological systems in general and cancer in particular. We demonstrate that utilizing prior knowledge (in the form of network connectivity information), without detailed state equations, greatly increases the power of machine-learning algorithms to predict network steady-state node values ('phenotypes') and perturbation responses ('drug effects').
Links to codes and datasets here: https://gray.mgh.harvard.edu/people-directory/71-david-craft-phd.
Supplementary data are available at Bioinformatics online.
我们的总体目标是开发基于基因组学和其他相关可访问信息的机器学习方法，用于预测患者对给定建议药物或治疗的反应。鉴于此问题的复杂性，我们首先使用来自模拟系统的数据来开发、测试和分析学习方法，这使我们能够访问已知的真实情况。我们研究了使用先前系统知识的好处，并调查了学习准确性如何取决于各种系统参数以及可用的训练数据量。
模拟基于布尔网络-有向图，节点状态为 0/1，逻辑节点更新规则-这是可以模拟细胞系统动态行为的最简单计算系统。布尔网络可以大规模生成和模拟，具有复杂但周期性的动态，因此为开发用于模块化和分层网络（例如一般生物系统和特别是癌症）的机器学习算法提供了有用的框架。我们证明，在没有详细状态方程的情况下，利用先验知识（以网络连通性信息的形式）可以大大提高机器学习算法预测网络稳态节点值（“表型”）和扰动响应（“药物作用”）的能力。
此处提供了代码和数据集的链接：https://gray.mgh.harvard.edu/people-directory/71-david-craft-phd。
补充数据可在“Bioinformatics”在线获取。
