AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning.
AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning.
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DOI：
10.1093/bib/bbad030
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发表时间：
2023-03-19
影响因子：
9.5
通讯作者：
中科院分区：
文献类型：
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阿尔茨海默病（Alzheimer's disease，AD）是最具挑战性的神经退行性疾病之一，其发病机制复杂，具有进行性，危险因素多。越来越多的研究证据表明，遗传可能是导致该病发生的关键因素。虽然以前的报道确定了相当多的AD相关基因，但由于患者样本量和选择偏倚，它们大多是有限的。缺乏旨在系统地识别AD相关风险突变的全面研究。为了应对这一挑战，我们在此构建了一个大规模的AD突变和共突变框架（“AD-Syn-Net”），并提出了名为Deep-SMCI和Deep-CMCI的深度学习模型，这些模型配置有完全连接的层，能够基于基因突变和共突变谱有效地预测受试者的认知障碍。接下来，我们将定制的框架应用于数据集，以评估突变的重要性得分，并确定导致认知障碍的突变效应子和共突变组合漏洞。此外，我们评估了突变对对网络架构的影响，以剖析AD的遗传组织，并识别可能导致痴呆的新的共突变，为提出未来的AD精准医学靶向治疗奠定了坚实的基础。我们的深度学习模型代码可以在这里开放访问：https://github.com/Pan-Bio/AD-mutation-effectors。
Alzheimer’s disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. To address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework (‘AD-Syn-Net’), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors.
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DOI：
10.6061/clinics/2013(02)rc01
发表时间：
2013
期刊：
Clinics (Sao Paulo, Brazil)
影响因子：
--
作者：
Izzo G;Forlenza OV;Santos Bd;Bertolucci PH;Ojopi EB;Gattaz WF;Kerr DS
通讯作者：
Kerr DS
影响因子：
7.5
作者：
Du S;Zheng H
通讯作者：
Zheng H
影响因子：
11
作者：
Bis JC;Jian X;Kunkle BW;Chen Y;Hamilton-Nelson KL;Bush WS;Salerno WJ;Lancour D;Ma Y;Renton AE;Marcora E;Farrell JJ;Zhao Y;Qu L;Ahmad S;Amin N;Amouyel P;Beecham GW;Below JE;Campion D;Cantwell L;Charbonnier C;Chung J;Crane PK;Cruchaga C;Cupples LA;Dartigues JF;Debette S;Deleuze JF;Fulton L;Gabriel SB;Genin E;Gibbs RA;Goate A;Grenier-Boley B;Gupta N;Haines JL;Havulinna AS;Helisalmi S;Hiltunen M;Howrigan DP;Ikram MA;Kaprio J;Konrad J;Kuzma A;Lander ES;Lathrop M;Lehtimäki T;Lin H;Mattila K;Mayeux R;Muzny DM;Nasser W;Neale B;Nho K;Nicolas G;Patel D;Pericak-Vance MA;Perola M;Psaty BM;Quenez O;Rajabli F;Redon R;Reitz C;Remes AM;Salomaa V;Sarnowski C;Schmidt H;Schmidt M;Schmidt R;Soininen H;Thornton TA;Tosto G;Tzourio C;van der Lee SJ;van Duijn CM;Valladares O;Vardarajan B;Wang LS;Wang W;Wijsman E;Wilson RK;Witten D;Worley KC;Zhang X;Alzheimer’s Disease Sequencing Project;Bellenguez C;Lambert JC;Kurki MI;Palotie A;Daly M;Boerwinkle E;Lunetta KL;Destefano AL;Dupuis J;Martin ER;Schellenberg GD;Seshadri S;Naj AC;Fornage M;Farrer LA
通讯作者：
Farrer LA
影响因子：
14
作者：
通讯作者：
--
影响因子：
4
作者：
Cechova, Katerina;Andel, Ross;Hort, Jakub
通讯作者：
Hort, Jakub