Discovery of biomarker combinations that predict periodontal health or disease with high accuracy from GCF samples based on high-throughput proteomic analysis and mixed-integer linear optimization.
Discovery of biomarker combinations that predict periodontal health or disease with high accuracy from GCF samples based on high-throughput proteomic analysis and mixed-integer linear optimization.
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DOI：
10.1111/jcpe.12037
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发表时间：
2013-02
影响因子：
6.7
通讯作者：
Floudas CA
中科院分区：
文献类型：
--
作者：
Baliban RC;Sakellari D;Li Z;Guzman YA;Garcia BA;Floudas CA
确定慢性牙周炎 (CP) 和牙周健康个体的龈沟液 (GCF) 样本中基于蛋白质组的生物标志物的最佳组合，并通过已知的盲测试集验证预测。 GCF 样本采集自 96 名牙周病和牙周健康受试者，并使用高效液相色谱法、串联质谱法和 PILOT_PROTEIN 算法进行分析。然后开发了混合整数线性优化（MILP）模型来识别生物标志物的最佳组合，该组合可以清楚地区分盲受试者样本为健康或患病。对 55 个样本的训练集进行了 MILP 模型功能的彻底交叉验证，在将测试集样本注释为健康或患病时，始终达到 99% 以上的准确度。然后，该模型在所有 55 个样本上进行训练，并在两个不同的盲测试集上进行测试，并使用 7 种人类蛋白质和 3 种细菌蛋白质的最佳组合，该模型能够正确预测 41 个健康和患病样本中的 40 个。所提出的大规模蛋白质组学分析和 MILP 模型导致识别出新的生物标志物组合，以对牙周状态进行一致诊断，预测准确度超过 95%。
To identify optimal combination(s) of proteomic based biomarkers in gingival crevicular fluid (GCF) samples from chronic periodontitis (CP) and periodontally healthy individuals and validate the predictions through known and blind test sets. GCF samples were collected from 96 CP and periodontally healthy subjects and analyzed using high-performance liquid chromatography, tandem mass spectrometry, and the PILOT_PROTEIN algorithm. A mixed-integer linear optimization (MILP) model was then developed to identify the optimal combination of biomarkers which could clearly distinguish a blind subject sample as healthy or diseased. A thorough cross-validation of the MILP model capability was performed on a training set of 55 samples and greater than 99% accuracy was consistently achieved when annotating the testing set samples as healthy or diseased. The model was then trained on all 55 samples and tested on two different blind test sets, and using an optimal combination of 7 human proteins and 3 bacterial proteins, the model was able to correctly predict 40 out of 41 healthy and diseased samples. The proposed large-scale proteomic analysis and MILP model led to the identification of novel combinations of biomarkers for consistent diagnosis of periodontal status with greater than 95% predictive accuracy.
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影响因子：
4.4
作者：
DiMaggio, Peter A., Jr.;Floudas, Christodoulos A.;Yates, John R., III
通讯作者：
Yates, John R., III
影响因子：
7.6
作者：
Barnes, V. M.;Teles, R.;Guo, L.
通讯作者：
Guo, L.
影响因子：
6.7
作者：
Joensson, Daniel;Ramberg, Per;Papapanou, Panos N.
通讯作者：
Papapanou, Panos N.
影响因子：
6.7
作者：
Teles RP;Gursky LC;Faveri M;Rosa EA;Teles FR;Feres M;Socransky SS;Haffajee AD
通讯作者：
Haffajee AD
影响因子：
18.6
作者：
Champagne, CME;Buchanan, W;Offenbacher, S
通讯作者：
Offenbacher, S