Large scale multi-omics data analysis and signature prediction have been a topic of interest in the last two decades. While various traditional clustering/correlation-based methods have been proposed, but the overall prediction is not always satisfactory. To solve these challenges, in this article, we propose a new approach by leveraging the Gene Ontology (GO)similarity combined with multiomics data. In this article, a new GO similarity measure, $ModSchlicker$ , is proposed and the effectiveness of the proposed measure along with other standardized measures are reviewed while using various graph topology-based Information Content (IC)values of GO-term. The proposed measure is deployed to PPI prediction. Furthermore, by involving GO similarity, we propose a new framework for stronger disease-based gene signature detection from the multi-omics data. For the first objective, we predict interaction from various benchmark PPI datasets of Yeast and Human species. For the latter, the gene expression and methylation profiles are used to identify Differentially Expressed and Methylated (DEM)genes. Thereafter, the GO similarity score along with a statistical method are used to determine the potential gene signature. Interestingly, the proposed method produces a better performance ( $>$ 0.9 avg. accuracy and $>$ 0.95 AUC)as compared to the other existing related methods during the classification of the participating features (genes)of the signature. Moreover, the proposed method is highly useful in other prediction/classification problems for any kind of large scale omics data.

中文翻译：

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一种新的基于图拓扑的 GO-Similarity 度量，用于从多组学数据中进行签名检测及其在其他问题中的应用

在过去的二十年里，大规模多组学数据分析和特征预测一直是人们感兴趣的话题。虽然已经提出了各种传统的基于聚类/相关的方法，但总体预测并不总是令人满意。为了解决这些挑战，在本文中，我们提出了一种利用基因本体（GO）相似性与多组学数据相结合的新方法。在本文中，一种新的 GO 相似性度量，$ModSchlicker$ , 被提出，并在使用 GO-term 的各种基于图拓扑的信息内容 (IC) 值时审查了所提出的度量以及其他标准化度量的有效性。建议的措施被部署到 PPI 预测。此外，通过涉及 GO 相似性，我们提出了一个新框架，用于从多组学数据中进行更强的基于疾病的基因特征检测。对于第一个目标，我们预测酵母和人类物种的各种基准 PPI 数据集的相互作用。对于后者，基因表达和甲基化谱用于识别差异表达和甲基化 (DEM) 基因。此后，使用 GO 相似性评分以及统计方法来确定潜在的基因特征。有趣的是，所提出的方法产生了更好的性能（ $>$0.9 平均 准确性和$>$ 0.95 AUC）与其他现有的相关方法相比，在签名的参与特征（基因）的分类过程中。此外，所提出的方法在任何类型的大规模组学数据的其他预测/分类问题中都非常有用。