Edgar F Black, Luigi Marini, Ashwini Vaidya, Dora Berman, Melissa Willman, Dan Salomon, Amelia Bartholomew, Norma Kenyon, Kenton McHenry
{"title":"利用隐马尔可夫模型确定受试者数据随时间的变化,研究间充质干细胞的免疫调节作用。","authors":"Edgar F Black, Luigi Marini, Ashwini Vaidya, Dora Berman, Melissa Willman, Dan Salomon, Amelia Bartholomew, Norma Kenyon, Kenton McHenry","doi":"10.1109/eScience.2014.29","DOIUrl":null,"url":null,"abstract":"<p><p>A novel application of Hidden Markov Models is used to help research intended to test the immunuregulatory effects of mesenchymal stem cells in a cynomolgus monkey model of islet transplantation. The Hidden Markov Model, an <i>unsupervised learning</i> data mining technique, is used to automatically determine the postoperative day (POD) corresponding to a decrease of graft function, a possible sign of transplant rejection, on nonhuman primates after isolated islet cell transplant. Currently, decrease of graft function is being determined solely on experts' judgment. Further, information gathered from the evaluation of construted Hidden Markov Models is used as part of a clustering method to aggregate the nonhuman subjects into groups or clusters with the objective of finding similarities that could potentially help predict the health outcome of subjects undergoing postoperative care. Results on expert labeled data show the HMM to be accurate 60% of the time. Clusters based on the HMMs further suggest a possible correspondence between donor haplotypes matching and loss of function outcomes.</p>","PeriodicalId":90293,"journal":{"name":"Proceedings ... IEEE International Conference on eScience. IEEE International Conference on eScience","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/eScience.2014.29","citationCount":"3","resultStr":"{\"title\":\"Using Hidden Markov Models to Determine Changes in Subject Data over Time, Studying the Immunoregulatory effect of Mesenchymal Stem Cells.\",\"authors\":\"Edgar F Black, Luigi Marini, Ashwini Vaidya, Dora Berman, Melissa Willman, Dan Salomon, Amelia Bartholomew, Norma Kenyon, Kenton McHenry\",\"doi\":\"10.1109/eScience.2014.29\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>A novel application of Hidden Markov Models is used to help research intended to test the immunuregulatory effects of mesenchymal stem cells in a cynomolgus monkey model of islet transplantation. The Hidden Markov Model, an <i>unsupervised learning</i> data mining technique, is used to automatically determine the postoperative day (POD) corresponding to a decrease of graft function, a possible sign of transplant rejection, on nonhuman primates after isolated islet cell transplant. Currently, decrease of graft function is being determined solely on experts' judgment. Further, information gathered from the evaluation of construted Hidden Markov Models is used as part of a clustering method to aggregate the nonhuman subjects into groups or clusters with the objective of finding similarities that could potentially help predict the health outcome of subjects undergoing postoperative care. Results on expert labeled data show the HMM to be accurate 60% of the time. Clusters based on the HMMs further suggest a possible correspondence between donor haplotypes matching and loss of function outcomes.</p>\",\"PeriodicalId\":90293,\"journal\":{\"name\":\"Proceedings ... IEEE International Conference on eScience. IEEE International Conference on eScience\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/eScience.2014.29\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings ... IEEE International Conference on eScience. IEEE International Conference on eScience\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/eScience.2014.29\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings ... IEEE International Conference on eScience. IEEE International Conference on eScience","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/eScience.2014.29","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using Hidden Markov Models to Determine Changes in Subject Data over Time, Studying the Immunoregulatory effect of Mesenchymal Stem Cells.
A novel application of Hidden Markov Models is used to help research intended to test the immunuregulatory effects of mesenchymal stem cells in a cynomolgus monkey model of islet transplantation. The Hidden Markov Model, an unsupervised learning data mining technique, is used to automatically determine the postoperative day (POD) corresponding to a decrease of graft function, a possible sign of transplant rejection, on nonhuman primates after isolated islet cell transplant. Currently, decrease of graft function is being determined solely on experts' judgment. Further, information gathered from the evaluation of construted Hidden Markov Models is used as part of a clustering method to aggregate the nonhuman subjects into groups or clusters with the objective of finding similarities that could potentially help predict the health outcome of subjects undergoing postoperative care. Results on expert labeled data show the HMM to be accurate 60% of the time. Clusters based on the HMMs further suggest a possible correspondence between donor haplotypes matching and loss of function outcomes.