Mandal Sudip, Saha Goutam, Pal Rajat Kumar
* Electronics and Communication Engineering Department, Global Institute of Management and Technology, Krishnanagar, West Bengal 741102, India.
† Information Technology Department, North Eastern Hill University, Umshing, Mawkynroh, Shillong 793 022, Meghalaya, India.
J Bioinform Comput Biol. 2017 Aug;15(4):1750016. doi: 10.1142/S0219720017500160. Epub 2017 Jun 13.
Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. 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. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.
从诸如时间序列微阵列数据这样的生物数据库中正确推断细胞内的基因调控，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。循环神经网络（RNN）是对动态过程进行建模以及推断基因间正确依赖关系的最流行且简单的方法之一。受社会大象行为的启发，我们提出了一种新的元启发式算法，即大象群水搜索算法（ESWSA）来推断基因调控网络（GRN）。该算法主要基于干旱期间智能且群居的大象的水搜索策略，并利用了不同类型的通信技术。最初，该算法针对基准中小规模人工遗传网络进行测试，测试有无不同噪声水平的情况，并从参数误差、最小适应度值、执行时间、真实调控预测准确性等方面观察其效率。接下来，将所提出的算法应用于大肠杆菌SOS网络的实时基因表达数据进行测试，并将结果与其他现有优化方法进行比较。实验结果表明，ESWSA对于基因调控网络推断问题非常有效，并且在许多方面比其他方法表现更好。
