PathMethy: an interpretable AI framework for cancer origin tracing based on DNA methylation.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Jiajing Xie, Yuhang Song, Hailong Zheng, Shijie Luo, Ying Chen, Chen Zhang, Rongshan Yu, Mengsha Tong
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引用次数: 0

Abstract

Despite advanced diagnostics, 3%-5% of cases remain classified as cancer of unknown primary (CUP). DNA methylation, an important epigenetic feature, is essential for determining the origin of metastatic tumors. We presented PathMethy, a novel Transformer model integrated with functional categories and crosstalk of pathways, to accurately trace the origin of tumors in CUP samples based on DNA methylation. PathMethy outperformed seven competing methods in F1-score across nine cancer datasets and predicted accurately the molecular subtypes within nine primary tumor types. It not only excelled at tracing the origins of both primary and metastatic tumors but also demonstrated a high degree of agreement with previously diagnosed sites in cases of CUP. PathMethy provided biological insights by highlighting key pathways, functional categories, and their interactions. Using functional categories of pathways, we gained a global understanding of biological processes. For broader access, a user-friendly web server for researchers and clinicians is available at https://cup.pathmethy.com.

PathMethy:基于 DNA 甲基化的可解释癌症起源追踪人工智能框架。
尽管诊断手段先进,但仍有 3%-5% 的病例被归类为原发灶不明的癌症(CUP)。DNA 甲基化是一种重要的表观遗传特征,对于确定转移性肿瘤的起源至关重要。我们提出了 PathMethy,这是一种新型的 Transformer 模型,集成了功能分类和路径串联,可根据 DNA 甲基化准确追踪 CUP 样本中肿瘤的来源。在九个癌症数据集中,PathMethy 的 F1 分数超过了七种竞争方法,并准确预测了九种原发性肿瘤类型中的分子亚型。它不仅在追踪原发性肿瘤和转移性肿瘤的起源方面表现出色,而且与之前诊断出的 CUP 病例的部位高度吻合。PathMethy 通过突出关键通路、功能类别及其相互作用来提供生物学见解。通过途径的功能类别,我们对生物过程有了全面的了解。为了扩大访问范围,我们还为研究人员和临床医生提供了一个用户友好型网络服务器,网址是 https://cup.pathmethy.com。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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