组学技术的进步通过产生海量数据彻底改变了癌症研究。通常，解读这些复杂数据的方法是嵌入分子交互网络算法。这些算法在低维空间中找到最能保留节点相似性的方式。目前可用的嵌入方法直接挖掘基因嵌入以发现新的与癌症相关的知识。然而，这些基因中心的方法产生的知识不完整，因为它们没有考虑基因组改变的功能影响。我们提出了一个新的、功能中心的视角和方法，来补充从组学数据中获得的知识。我们引入了我们的功能映射矩阵来探索由非负矩阵三因式分解算法生成的不同组织特异性和物种特异性嵌入空间的功能组织。此外，我们使用我们的FMM来定义这些分子交互网络嵌入空间的最佳维数。对于这种最佳维数，我们将人类最普遍的癌症的FMM与其相应的对照组织的FMM进行比较。我们发现，癌症通过改变癌相关功能在嵌入空间的位置，保持非癌症相关功能的位置。我们利用这种空间“移动”来预测新的癌症相关功能。最后，我们预测目前可用的基因中心分析方法无法识别的新的与癌症相关的基因；我们通过文献整理和患者生存数据的回顾性分析来验证这些预测。数据和源代码可以在https://github.com/gaiac/FMM访问。补充数据可在生物信息学在线获取。©作者（们） 2023。由牛津大学出版社发表。

Advances in omics technologies have revolutionized cancer research by producing massive datasets. Common approaches to deciphering these complex data are by embedding algorithms of molecular interaction networks. These algorithms find a low-dimensional space in which similarities between the network nodes are best preserved. Currently available embedding approaches mine the gene embeddings directly to uncover new cancer-related knowledge. However, these gene-centric approaches produce incomplete knowledge, since they do not account for the functional implications of genomic alterations. We propose a new, function-centric perspective and approach, to complement the knowledge obtained from omic data.We introduce our Functional Mapping Matrix to explore the functional organization of different tissue-specific and species-specific embedding spaces generated by a Non-negative Matrix Tri-Factorization algorithm. Also, we use our FMM to define the optimal dimensionality of these molecular interaction network embedding spaces. For this optimal dimensionality, we compare the FMMs of the most prevalent cancers in human to FMMs of their corresponding control tissues. We find that cancer alters the positions in the embedding space of cancer-related functions, while it keeps the positions of the non-cancer-related ones. We exploit this spacial "movement" to predict novel cancer-related functions. Finally, we predict novel cancer-related genes that the currently available methods for gene-centric analyses cannot identify; we validate these predictions by literature curation and retrospective analyses of patient survival data.Data and source code can be accessed at https://github.com/gaiac/FMM.Supplementary data are available at Bioinformatics online.© The Author(s) 2023. Published by Oxford University Press.