基于蝙蝠算法的基因调控网络循环神经网络建模
Recurrent Neural Network Based Modeling of Gene Regulatory Network Using Bat Algorithm
摘要：
正确推断细胞内的遗传调控是后基因组时代生物学家和研究人员面临的最大挑战之一。已经提出了一些智能技术和模型来识别基因之间的调控关系，从生物数据库，如时间序列微阵列数据。循环神经网络（Recurrent Neural Network，RNN）是一种最流行和最简单的方法来建模动态以及推断基因之间的正确依赖关系。将蝙蝠算法（BA）应用于基因调控网络（GRN）的RNN模型参数优化。首先，所提出的方法进行了测试，对小人工网络没有任何噪音和效率的迭代次数，人口和BA优化参数的数量观察。该模型还在存在不同水平的随机噪声的情况下对小型人工网络进行了验证，并证明了其在存在噪声的情况下推断正确推断的能力，如真实世界数据集。在本研究的下一阶段，基于BA的RNN被应用于真实世界的基准时间序列微阵列数据集E。杆菌实验结果表明，该方法能够识别出最大数量的真阳性规则，但也包含一些假阳性规则。因此，BA非常适合于在RNN模型的帮助下识别生物似然GRN。
英文摘要：
Correct inference of genetic regulations inside a cell is one of the greatest challenges in post genomic era for the biologist and researchers. Several intelligent techniques and models were already proposed to identify the regulatory relations among genes from the biological database like time series microarray data. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. In this paper, Bat Algorithm (BA) is applied to optimize the model parameters of RNN model of Gene Regulatory Network (GRN). Initially the proposed method is tested against small artificial network without any noise and the efficiency is observed in term of number of iteration, number of population and BA optimization parameters. The model is also validated in presence of different level of random noise for the small artificial network and that proved its ability to infer the correct inferences in presence of noise like real world dataset. In the next phase of this research, BA based RNN is applied to real world benchmark time series microarray dataset of E. coli. The results prove that it can able to identify the maximum number of true positive regulation but also include some false positive regulations. Therefore, BA is very suitable for identifying biological plausible GRN with the help RNN model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
