面向微阵列数据分析的结合深度学习的大象搜索算法
Elephant Search with Deep Learning for Microarray Data Analysis
AI总结：
针对微阵列基因表达数据规模大、分析难的问题，提出基于大象搜索的特征选择结合带softmax的随机梯度下降深度学习的分类方法，在9个UCI癌症数据集上验证了其优于最新方法的性能，适用于生物信息学研究。
摘要：
尽管微阵列基因表达数据分析领域已有大量研究，但研究人员仍面临挑战，难以有效且高效地分析规模庞大且复杂的基因表达数据。特征（基因）选择方法对于理解样本间生物学与非生物学变异的差异至关重要。为解决这一问题，本文提出一种基于新型大象搜索（ES）的优化方法，用于从海量微阵列数据中筛选最优基因表达特征。此外，研究设想采用一种性能优异的机器学习方法，利用这类高维复杂微阵列数据集挖掘其中的隐藏模式，以实现有意义的预测和最高精度的分类。具体而言，针对降维后的特征（基因），采用基于随机梯度下降、带有softmax激活函数的深度学习（DL），根据基因表达水平实现不同样本的更优分类。实验在9个最常用的癌症微阵列基因选择数据集上开展，数据集取自UCI机器学习库。本文将所提出的基于大象搜索的深度学习（ESDL）方法所得实证结果与最新发表的研究进行了对比，以验证其在未来生物信息学研究中的适用性。
英文摘要：
Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes. The feature (gene) selection method is of paramount importance for understanding the differences in biological and non-biological variation between samples. In order to address this problem, a novel elephant search (ES) based optimization is proposed to select best gene expressions from the large volume of microarray data. Further, a promising machine learning method is envisioned to leverage such high dimensional and complex microarray dataset for extracting hidden patterns inside to make a meaningful prediction and most accurate classification. In particular, stochastic gradient descent based Deep learning (DL) with softmax activation function is then used on the reduced features (genes) for better classification of different samples according to their gene expression levels. The experiments are carried out on nine most popular Cancer microarray gene selection datasets, obtained from UCI machine learning repository. The empirical results obtained by the proposed elephant search based deep learning (ESDL) approach are compared with most recent published article for its suitability in future Bioinformatics research.
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。
