Optimizing Peripheral Nerve Regeneration using Computational Intelligence based T

描述（由申请人提供）：周围神经损伤是每年影响大量患者的常见疾病。 组织工程已经成为一种强有力的方法来开发替代神经移植周围神经再生。 由于周围神经再生中的组织工程策略涉及各种可能的变量组合，因此有必要开发有效的工具来确定最佳的组织工程策略并预测基于这些周围神经再生的组织工程策略的实验结果。 一些研究小组已经应用人工神经网络和决策树来获得用于预测组织工程策略的最佳模型配置。 对于基于决策树的方法，很难区分哪一个分类树比另一个更好。 此外，使用决策树算法的预测系统缺乏随着时间积累学习经验的能力。 另一方面，人工神经网络（ANN）表现出一些显着的性质，但只有连接权是用固定的拓扑结构训练的。 对于每种特定的组织工程策略，很难提前找到最佳的固定拓扑结构。 在这个建议中，群智能（SI）的进化人工神经网络技术提出了解决这一挑战。 两种基于群体智能的方法，蚁群优化（ACO）和粒子群优化（PSO），将在这个项目中应用到训练人工神经网络模型。 更具体地说，蚁群算法将被用来优化的神经网络模型的拓扑结构，而粒子群算法被用来调整的神经网络模型的连接权重的基础上优化的拓扑结构。 对于这种基于SWarm智能的人工神经网络强化学习方法（SWIRL-ANN）系统，人工神经网络的拓扑结构和连接权重可以同时自动进化，从而可以实现周围神经再生中组织工程策略的最佳分类器。 该研究项目将包括以下几个阶段：目标1：预测周围神经再生的组织工程策略，使用SWARM智能强化学习方法的人工神经网络（SWIRL-ANN）分析和预测系统。 目标二：通过SWIRL-ANN分析和预测系统预测新型未知组织工程神经移植物在大鼠坐骨神经损伤模型中桥接周围神经缺损的有效性。 公共卫生相关性：组织工程已经成为一种强大的方法，用于开发周围神经再生的神经移植物。 由于周围神经再生中的组织工程策略涉及各种可能的变量组合，因此有必要开发有效的工具来确定最佳的组织工程策略并预测基于这些周围神经再生的组织工程策略的实验结果。 在这个建议中，群智能（SI）的进化人工神经网络（ANN）技术提出来应对这一挑战。 本研究将有助于有效开发组织工程产品，用于组织和器官替代。

DESCRIPTION (provided by applicant): Peripheral nerve injuries are common diseases that affect a large amount of patients every year. Tissue engineering has emerged as a powerful approach for developing alternative nerve grafts for peripheral nerve regeneration. Since tissue engineering strategies in peripheral nerve regeneration involve various possible combinations of variables, it is necessary to develop efficient tools to identify optimal tissue engineering strategies and predict the experimental results based on these tissue engineering strategies for peripheral nerve regeneration. Some research groups have applied artificial neural networks and decision trees to obtain the best model configuration for the prediction of the tissue engineering strategies. For the decision trees based methods, it is hard to tell which classification tree is better than the other. Furthermore, the prediction system using the decision tree algorithm lacks the capability of accumulating the learning experience over time. On the other hand, Artificial Neural Networks (ANNs) exhibit some remarkable properties, but only the connection weights are trained with fixed topology. It is hard to find the best fixed topology in advance for each specific tissue engineering strategy. In this proposal, swarm intelligence (SI) based evolving ANNs technique is proposed to tackle this challenge. Two swarm intelligence based methods, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), will be applied in this project to train the ANN model. More specifically, ACO will be used to optimize the topology structure of the ANN models, while the PSO is used to adjust the connection weights of the ANN models based on the optimized topology structure. For this SWarm Intelligence based Reinforcement Learning method for ANNs (SWIRL-ANN) system, both topology and connection weight of artificial neural networks can be evolved automatically and simultaneously so that an optimal classifier for tissue engineering strategies in peripheral nerve regeneration can be achieved. The research project will include the following phases: Aim 1: Predict tissue engineering strategies in peripheral nerve regeneration using SWarm Intelligence based Reinforcement Learning method for ANNs (SWIRL-ANN) analytical and prediction system. Aim 2: Validate the efficacy of novel unknown tissue engineered nerve grafts as predicted by using SWIRL-ANN based analytical and prediction system for bridging peripheral nerve gaps in rat sciatic nerve injury model in vivo. PUBLIC HEALTH RELEVANCE: Tissue engineering has emerged as a powerful approach for developing nerve grafts for peripheral nerve regeneration. Since tissue engineering strategies in peripheral nerve regeneration involve various possible combinations of variables, it is necessary to develop efficient tools to identify optimal tissue engineering strategies and predict the experimental results based on these tissue engineering strategies for peripheral nerve regeneration. In this proposal, swarm intelligence (SI) based evolving artificial neural networks (ANNs) technique is proposed to tackle this challenge. The proposed research will be helpful to efficiently develop tissue engineered products for tissue and organ replacement.