Noncoding RNAs improve the predictive power of network medicine.
Noncoding RNAs improve the predictive power of network medicine.
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DOI：
10.1073/pnas.2301342120
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发表时间：
2023-11-07
影响因子：
11.1
通讯作者：
Barabasi, Albert-Laszlo
中科院分区：
文献类型：
--
作者：
Gysi, Deisy Morselli;Barabasi, Albert-Laszlo
网络医学已被用于量化疾病机制，合并症和治疗，但大多数方法都忽略了非编码RNA（ncRNA）介导的相互作用。这项研究系统地结合了实验证实的ncRNA和蛋白质-蛋白质相互作用，构建了人类细胞中所有物理相互作用的综合网络。包含ncRNA增加了相互作用组中的基因和相互作用的数量，并增强了识别疾病模块和预测疾病之间的共病模式的能力。最终，这项研究表明，包括非编码交互提高了网络医学的广度和准确性。网络医学提高了对疾病的机制性理解，提供了对疾病机制、合并症以及新的诊断工具和治疗方法的定量见解。然而，大多数基于网络的方法依赖于蛋白质-蛋白质相互作用（PPI）的综合图谱，忽略了非编码RNA（ncRNA）介导的相互作用。在这里，我们系统地结合联合收割机实验证实的结合相互作用介导的ncRNA与PPI，构建一个全面的网络，在人类细胞中的所有物理相互作用。我们发现，包含ncRNA使相互作用组中的基因数量增加了46%，相互作用的数量增加了107%，显著增强了我们识别疾病模块的能力。事实上，我们发现132种疾病在基于蛋白质的相互作用组中缺乏统计学显著的疾病模块，但在包含ncRNA介导的相互作用后具有统计学显著的疾病模块，使这些疾病可用于网络医学工具。我们表明，包含ncRNA有助于揭示以前无法检测到的疾病-疾病关系，并扩大了我们预测疾病之间共病模式的能力。总之，我们发现，包括非编码交互提高了呼吸和网络医学的预测准确性。
Network medicine has been used to quantify disease mechanisms, comorbidities, and treatments, but most approaches have ignored interactions mediated by noncoding RNAs (ncRNAs). This study systematically combines experimentally confirmed ncRNA and protein–protein interactions to construct a comprehensive network of all physical interactions in the human cell. The inclusion of ncRNA increases the number of genes and interactions in the interactome and enhances the ability to identify disease modules and predict comorbidity patterns between diseases. Ultimately, this study shows that including noncoding interactions improves the breadth and accuracy of network medicine. Network medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein–protein interactions (PPI), ignoring interactions mediated by noncoding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with PPI, constructing a comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases lacked a statistically significant disease module in the protein-based interactome but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease–disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including noncoding interactions improves both the breath and the predictive accuracy of network medicine.
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DOI：
10.4021/jocmr1682w
发表时间：
2014-02
期刊：
Journal of clinical medicine research
影响因子：
--
作者：
Al-Jameil N;Aziz Khan F;Fareed Khan M;Tabassum H
通讯作者：
Tabassum H
影响因子：
14.9
作者：
Chang, Le;Zhou, Guangyan;Xia, Jianguo
通讯作者：
Xia, Jianguo
影响因子：
8.4
作者：
ERIKSSON, M
通讯作者：
ERIKSSON, M
DOI：
10.1093/database/bat034
发表时间：
2013
期刊：
Database : the journal of biological databases and curation
影响因子：
--
作者：
Bhartiya D;Pal K;Ghosh S;Kapoor S;Jalali S;Panwar B;Jain S;Sati S;Sengupta S;Sachidanandan C;Raghava GP;Sivasubbu S;Scaria V
通讯作者：
Scaria V
DOI：
10.1126/science.1262110
发表时间：
2015-05-08
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
GTEx Consortium
通讯作者：
GTEx Consortium