A machine learning approach to predict pancreatic islet grafts rejection versus tolerance.
A machine learning approach to predict pancreatic islet grafts rejection versus tolerance.
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DOI：
10.1371/journal.pone.0241925
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发表时间：
2020
期刊：
影响因子：
3.7
通讯作者：
Abdulreda MH
中科院分区：
文献类型：
--
作者：
Ceballos GA;Hernandez LF;Paredes D;Betancourt LR;Abdulreda MH
人工智能（AI）和机器学习（ML）在生物医学研究中的应用有望从医疗保健和不断扩大的高通量研究应用中产生的大量数据中释放新信息。这些信息可以帮助医学诊断，并揭示可作为早期疾病生物标志物的各种独特的生化和免疫特征模式。在这份报告中，我们证明了在相对较小的数据集中使用AI/ML方法来区分从小鼠获得的三类样本的可行性，这些小鼠在眼前房移植后拒绝或耐受胰岛同种异体移植物，以及来自幼稚对照组。我们创建了一个锁定的软件的基础上支持向量机（SVM）技术的模式识别胶束电动色谱和激光诱导荧光检测（MEKC-LIFD）产生的电泳图（EPG）。仅根据代表胰岛同种异体移植物直接局部微环境的微升大小的房水样本中获得的对齐的EPG进行预测。该分析在三种样品类别的EPG中识别出区别峰。我们的分类器软件用靶向和非靶向峰进行了测试。使用非目标峰的图案（即，基于EPG的整个模式），它能够实现22个阳性分类得分中的21个，在三个样本类别中的相应预测准确率为95.45%，并且在拒绝和耐受受体之间的准确率为100%。这些发现证明了AI/ML方法对少量样品进行分类的可行性，并且它们保证了进一步的研究，以鉴定对应于区别性特征的分析物/生化物质作为胰岛同种异体移植免疫排斥和耐受的潜在生物标志物。
The application of artificial intelligence (AI) and machine learning (ML) in biomedical research promises to unlock new information from the vast amounts of data being generated through the delivery of healthcare and the expanding high-throughput research applications. Such information can aid medical diagnoses and reveal various unique patterns of biochemical and immune features that can serve as early disease biomarkers. In this report, we demonstrate the feasibility of using an AI/ML approach in a relatively small dataset to discriminate among three categories of samples obtained from mice that either rejected or tolerated their pancreatic islet allografts following transplant in the anterior chamber of the eye, and from naïve controls. We created a locked software based on a support vector machine (SVM) technique for pattern recognition in electropherograms (EPGs) generated by micellar electrokinetic chromatography and laser induced fluorescence detection (MEKC-LIFD). Predictions were made based only on the aligned EPGs obtained in microliter-size aqueous humor samples representative of the immediate local microenvironment of the islet allografts. The analysis identified discriminative peaks in the EPGs of the three sample categories. Our classifier software was tested with targeted and untargeted peaks. Working with the patterns of untargeted peaks (i.e., based on the whole pattern of EPGs), it was able to achieve a 21 out of 22 positive classification score with a corresponding 95.45% prediction accuracy among the three sample categories, and 100% accuracy between the rejecting and tolerant recipients. These findings demonstrate the feasibility of AI/ML approaches to classify small numbers of samples and they warrant further studies to identify the analytes/biochemicals corresponding to discriminative features as potential biomarkers of islet allograft immune rejection and tolerance.
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影响因子：
1.2
作者：
Abdulreda, Midhat H.;Caicedo, Alejandro;Berggren, Per-Olof
通讯作者：
Berggren, Per-Olof
DOI：
10.1073/pnas.1105002108
发表时间：
2011-08-02
影响因子：
11.1
作者：
Abdulreda, Midhat H.;Faleo, Gaetano;Berggren, Per-Olof
通讯作者：
Berggren, Per-Olof
DOI：
10.1016/j.jchromb.2011.11.016
发表时间：
2012-01-01
影响因子：
3
作者：
Betancourt, Luis;Rada, Pedro;Hernandez, Luis
通讯作者：
Hernandez, Luis
DOI：
10.1084/jem.20130785
发表时间：
2014-03-10
期刊：
The Journal of experimental medicine
影响因子：
--
作者：
Miska J;Abdulreda MH;Devarajan P;Lui JB;Suzuki J;Pileggi A;Berggren PO;Chen Z
通讯作者：
Chen Z
DOI：
10.1016/j.jchromb.2018.02.015
发表时间：
2018-04-01
影响因子：
3
作者：
Betancourt, L.;Rada, P.;Paredes, D. A.
通讯作者：
Paredes, D. A.