来自杂志 Genome Research 的文献。
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1.
半面阳光
(2026-03-31 18:15):
#paper doi: 10.1101/gr.278413.123. Genome Res. 2025. Artificial intelligence and machine learning in cell-free-DNA-based diagnostics. 这篇综述文章不是提出某个全新算法，而是系统总结了 AI/机器学习怎样用于 cfDNA（cell-free DNA）诊断，尤其是 NIPT 和 肿瘤液体活检 两大场景。作者先回顾了 cfDNA 的生物学特征，再介绍常见的 ML/AI 方法，最后重点讲这些方法如何处理 cfDNA 这类高维、多特征数据。
Genome Research,
2025-1.
DOI: 10.1101/gr.278413.123
Abstract:
The discovery of circulating fetal and tumor cell-free DNA (cfDNA) molecules in plasma has opened up tremendous opportunities in noninvasive diagnostics such as the detection of fetal chromosomal aneuploidies and …
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The discovery of circulating fetal and tumor cell-free DNA (cfDNA) molecules in plasma has opened up tremendous opportunities in noninvasive diagnostics such as the detection of fetal chromosomal aneuploidies and cancers and in posttransplantation monitoring. The advent of high-throughput sequencing technologies makes it possible to scrutinize the characteristics of cfDNA molecules, opening up the fields of cfDNA genetics, epigenetics, transcriptomics, and fragmentomics, providing a plethora of biomarkers. Machine learning (ML) and/or artificial intelligence (AI) technologies that are known for their ability to integrate high-dimensional features have recently been applied to the field of liquid biopsy. In this review, we highlight various AI and ML approaches in cfDNA-based diagnostics. We first introduce the biology of cell-free DNA and basic concepts of ML and AI technologies. We then discuss selected examples of ML- or AI-based applications in noninvasive prenatal testing and cancer liquid biopsy. These applications include the deduction of fetal DNA fraction, plasma DNA tissue mapping, and cancer detection and localization. Finally, we offer perspectives on the future direction of using ML and AI technologies to leverage cfDNA fragmentation patterns in terms of methylomic and transcriptional investigations.
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2.
徐炳祥
(2025-10-31 16:38):
#paper doi: 10.1101/gr.212241.116 Genome Research, 2018, FIND: difFerential chromatin INteractions Detection using a spatial Poisson process。差异分析是Hi-C数据分析中一个尚未被完全解决的重要课题。早期研究主要基于广义线性模型，考虑系统偏差的基础上移植基因差异表达分析中所用模型。然而这些模型忽略了Hi-C数据与基因表达数据间一个本质的差别，即Hi-C数据中相邻位点相互作用频率间存在强烈的相关关系，此相关关系的利用将有助于减少差异分析的假阳性并提高结果的稳定性。本文是第一篇系统性证明此相关关系存在的文献。本文指出，具有生物学意义的差异位点不仅本身应具有相互作用频率的显著差异，其一定半径的邻域内也应呈现此种差异。为此作者引入空间Poison过程对邻域内相互作用频率差异建模，并据此开发了一种Hi-C数据一致性度量和差异分析工具FIND。虽然FIND的性能并不十分优越，但其思想为后续三维基因组生物信息学提供了重要启示。
Genome Research,
2018-3.
DOI: 10.1101/gr.212241.116
Abstract:
Polymer-based simulations and experimental studies indicate the existence of a spatial dependency between the adjacent DNA fibers involved in the formation of chromatin loops. However, the existing strategies for detecting …
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Polymer-based simulations and experimental studies indicate the existence of a spatial dependency between the adjacent DNA fibers involved in the formation of chromatin loops. However, the existing strategies for detecting differential chromatin interactions assume that the interacting segments are spatially independent from the other segments nearby. To resolve this issue, we developed a new computational method, FIND, which considers the local spatial dependency between interacting loci. FIND uses a spatial Poisson process to detect differential chromatin interactions that show a significant difference in their interaction frequency and the interaction frequency of their neighbors. Simulation and biological data analysis show that FIND outperforms the widely used count-based methods and has a better signal-to-noise ratio.
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