Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity.
Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity.
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DOI：
10.1038/s42003-022-03068-7
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发表时间：
2022-02-11
影响因子：
5.9
通讯作者：
Oprea TI
中科院分区：
文献类型：
--
作者：
Binder J;Ursu O;Bologa C;Jiang S;Maphis N;Dadras S;Chisholm D;Weick J;Myers O;Kumar P;Yang JJ;Bhaskar K;Oprea TI
随着地球仪对阿尔茨海默病（AD）和相关疾病的研究资金的增加，正在产生大量数据。一些研究采用机器学习方法来理解不断增长的组学数据，以加强早期诊断，绘制复杂的疾病网络或发现潜在的药物靶点。我们描述的结果的基础上的目标中央资源数据库蛋白质知识图和证据路径转化为向量的元路径匹配。我们提取了特定基因和疾病之间的特征，然后使用XGBoost训练和优化我们的模型，称为MPxgb（AD）。为了确定我们的MPxgb（AD）预测性能，我们通过实验筛选管道检查了前20个预测基因。我们的分析确定了潜在的AD风险基因：FRRS 1，CTRAM，SCGB 3A 1，FAM 92 B/CIBAR 2和TMEFF 2。FRRS 1和FAM 92 B被认为是暗基因，而CTRAM、SCGB 3A 1和TMEFF 2与TREM 2-TYROBP、IL-1β-TNFα和MTOR-APP AD风险节点相关，表明与AD发病机制相关。Jessica Binder等人开发了一种机器学习模型来发现阿尔茨海默病的潜在药物靶点。他们在几个体外模型中验证了他们的20个最佳候选基因，并强调FRRS 1，CTRAM，SCGB 3A 1，FAM 92 B/CIBAR 2和TMEFF 2是潜在的AD风险基因。
With increased research funding for Alzheimer’s disease (AD) and related disorders across the globe, large amounts of data are being generated. Several studies employed machine learning methods to understand the ever-growing omics data to enhance early diagnosis, map complex disease networks, or uncover potential drug targets. We describe results based on a Target Central Resource Database protein knowledge graph and evidence paths transformed into vectors by metapath matching. We extracted features between specific genes and diseases, then trained and optimized our model using XGBoost, termed MPxgb(AD). To determine our MPxgb(AD) prediction performance, we examined the top twenty predicted genes through an experimental screening pipeline. Our analysis identified potential AD risk genes: FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2. FRRS1 and FAM92B are considered dark genes, while CTRAM, SCGB3A1, and TMEFF2 are connected to TREM2-TYROBP, IL-1β-TNFα, and MTOR-APP AD-risk nodes, suggesting relevance to the pathogenesis of AD. Jessica Binder et al. developed a machine learning model to discover potential drug targets for Alzheimer’s disease. They validated their 20 top candidates in several in vitro models, and highlight FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2 as potential AD risk genes.
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影响因子：
14.9
作者：
Avram S;Bologa CG;Holmes J;Bocci G;Wilson TB;Nguyen DT;Curpan R;Halip L;Bora A;Yang JJ;Knockel J;Sirimulla S;Ursu O;Oprea TI
通讯作者：
Oprea TI
影响因子：
14.9
作者：
The Gene Ontology Consortium
通讯作者：
The Gene Ontology Consortium
影响因子：
16.2
作者：
Bhaskar, Kiran;Konerth, Megan;Kokiko-Cochran, Olga N.;Cardona, Astrid;Ransohoff, Richard M.;Lamb, Bruce T.
通讯作者：
Lamb, Bruce T.
DOI：
10.1056/nejmoa1211851
发表时间：
2013-01-10
期刊：
The New England journal of medicine
影响因子：
--
作者：
Guerreiro R;Wojtas A;Bras J;Carrasquillo M;Rogaeva E;Majounie E;Cruchaga C;Sassi C;Kauwe JS;Younkin S;Hazrati L;Collinge J;Pocock J;Lashley T;Williams J;Lambert JC;Amouyel P;Goate A;Rademakers R;Morgan K;Powell J;St George-Hyslop P;Singleton A;Hardy J;Alzheimer Genetic Analysis Group
通讯作者：
Alzheimer Genetic Analysis Group
影响因子：
14.9
作者：
Nguyen DT;Mathias S;Bologa C;Brunak S;Fernandez N;Gaulton A;Hersey A;Holmes J;Jensen LJ;Karlsson A;Liu G;Ma'ayan A;Mandava G;Mani S;Mehta S;Overington J;Patel J;Rouillard AD;Schürer S;Sheils T;Simeonov A;Sklar LA;Southall N;Ursu O;Vidovic D;Waller A;Yang J;Jadhav A;Oprea TI;Guha R
通讯作者：
Guha R