先进的基因组和分子分析技术加速了对癌症发展和进展背后的调控机制以及患者定向治疗的认识。近年来，大量的生物信息研究推动了分子生物标志物的发现。癌症是全球主要死亡原因之一。阐明乳腺癌（BRCA）基因组和表观遗传因子可以提供揭示疾病机制的路线图。因此，揭示-组学数据类型之间可能的系统联系及其对BRCA肿瘤进展的贡献至关重要。在这项研究中，我们开发了一种基于机器学习（ML）的多组学数据分析集成方法。这种集成方法结合了基因表达（mRNA）、微型RNA（miRNA）和甲基化数据的信息。由于癌症的复杂性，这种集成数据有望通过这三组学数据集之间仅可用的模式来改善疾病的预测、诊断和治疗。此外，所提出的方法弥合了驱动疾病发病和进展的疾病机制之间的解释差距。我们的基础贡献是三组学集成工具（3Mint）。该工具旨在使用生物学知识进行分组和打分。另一个重要目标是通过检测新的交叉组学生物标志物组来改善基因选择。 3Mint的性能使用不同的度量进行评估。我们的计算性能评估显示，与使用miRNA和mRNA基因表达谱的miRcorrNet工具相比，3Mint仅使用较少的基因对BRCA分子亚型进行分类，具有类似的性能度量（95%的准确性）。3Mint加入甲基化数据可以得到更加聚焦的分析。 3Mint工具以及所有其他附加文件均可在https://github.com/malikyousef/3Mint/上获得。Copyright © 2023 Unlu Yazici, Marron, Bakir-Gungor, Zou and Yousef.

Advanced genomic and molecular profiling technologies accelerated the enlightenment of the regulatory mechanisms behind cancer development and progression, and the targeted therapies in patients. Along this line, intense studies with immense amounts of biological information have boosted the discovery of molecular biomarkers. Cancer is one of the leading causes of death around the world in recent years. Elucidation of genomic and epigenetic factors in Breast Cancer (BRCA) can provide a roadmap to uncover the disease mechanisms. Accordingly, unraveling the possible systematic connections between-omics data types and their contribution to BRCA tumor progression is crucial. In this study, we have developed a novel machine learning (ML) based integrative approach for multi-omics data analysis. This integrative approach combines information from gene expression (mRNA), microRNA (miRNA) and methylation data. Due to the complexity of cancer, this integrated data is expected to improve the prediction, diagnosis and treatment of disease through patterns only available from the 3-way interactions between these 3-omics datasets. In addition, the proposed method bridges the interpretation gap between the disease mechanisms that drive onset and progression. Our fundamental contribution is the 3 Multi-omics integrative tool (3Mint). This tool aims to perform grouping and scoring of groups using biological knowledge. Another major goal is improved gene selection via detection of novel groups of cross-omics biomarkers. Performance of 3Mint is assessed using different metrics. Our computational performance evaluations showed that the 3Mint classifies the BRCA molecular subtypes with lower number of genes when compared to the miRcorrNet tool which uses miRNA and mRNA gene expression profiles in terms of similar performance metrics (95% Accuracy). The incorporation of methylation data in 3Mint yields a much more focused analysis. The 3Mint tool and all other supplementary files are available at https://github.com/malikyousef/3Mint/.Copyright © 2023 Unlu Yazici, Marron, Bakir-Gungor, Zou and Yousef.