IEEE/ACM Trans Comput Biol Bioinform. 2020 Nov-Dec;17(6):2005-2016. doi: 10.1109/TCBB.2019.2918523. Epub 2020 Dec 8.
Cluster ensemble techniques aim to combine the outputs of multiple clustering algorithms to obtain a single consensus partitioning. The current paper reports about the development of a cluster ensemble based technique combining the concepts of multiobjective optimization and deep-learning models for gene clustering where some additional protein-protein interaction information are utilized for generating the consensus partitioning. The proposed ensemble based framework works in four phases: (i) filtering out the irrelevant genes from the microarray dataset: only the statistically significant genes are considered for further data analysis; (ii) generation of diverse base partitionings: a multi-objective optimization-based clustering technique is proposed which simultaneously optimizes three different cluster quality measures and generates a set of partitioning solutions on the Pareto optimal front; (iii) generation of a consensus partitioning: mentha scores, calculated by accessing a highly enriched protein-protein interaction archive named mentha, of different clustering solutions are considered for generating a weighted incidence matrix; (iv) finally, two approaches are used to generate a consensus partitioning from the obtained incidence matrix. The first approach is based on a traditional machine learning method, and another approach exploits the graph partitioning algorithm and two deep neural models to generate the final clustering. To validate the efficacy of the proposed ensemble framework, it is applied on five gene expression datasets. We present a comparative analysis of the proposed technique over different clustering algorithms in terms of biological homogeneity index (BHI) and biological stability index (BSI). The traditional approach attains an average 3 and 2 percent improvements over the best non-dominated solution with respect to BHI and BSI, respectively, whereas deep learning models illustrate an average 6.8 and 1.5 percent improvements over the proposed traditional approach with respect to BHI and BSI, respectively. Subsequently, Welch's t-test is executed to prove that the results obtained by the proposed methods are statistically significant. Availability of data and materials: https://github.com/sduttap16/DeepEnsm.
聚类集成技术旨在结合多个聚类算法的输出，以获得单一的共识划分。本文报告了一种基于聚类集成的技术的发展，该技术结合了多目标优化和深度学习模型的概念，用于基因聚类，其中利用一些额外的蛋白质-蛋白质相互作用信息来生成共识划分。所提出的基于集成的框架分四个阶段工作：（i）从微阵列数据集中过滤掉不相关的基因：仅考虑统计上显著的基因进行进一步数据分析；（ii）生成多样化的基础划分：提出了一种基于多目标优化的聚类技术，该技术同时优化了三个不同的聚类质量度量，并在帕累托最优前沿上生成一组划分解决方案；（iii）生成共识划分：考虑不同聚类解决方案的 mentha 分数，通过访问名为 mentha 的高度富集蛋白质-蛋白质相互作用档案来计算，以生成加权关联矩阵；（iv）最后，从获得的关联矩阵中使用两种方法生成共识划分。第一种方法基于传统的机器学习方法，另一种方法利用图划分算法和两种深度神经网络模型生成最终聚类。为了验证所提出的集成框架的有效性，将其应用于五个基因表达数据集。我们根据生物同质性指数（BHI）和生物稳定性指数（BSI），在不同聚类算法的基础上，对所提出的技术进行了比较分析。传统方法在 BHI 和 BSI 方面分别比最佳非支配解平均提高了 3%和 2%，而深度学习模型在 BHI 和 BSI 方面分别比所提出的传统方法平均提高了 6.8%和 1.5%。随后，进行了 Welch 检验以证明所提出方法获得的结果在统计上是显著的。数据和材料的可用性：https://github.com/sduttap16/DeepEnsm。
