{"title":"应用精密套索进行弥漫大B细胞淋巴瘤的基因选择。","authors":"Rashed Pourhamidi, Azam Moslemi","doi":"10.1186/s43046-023-00172-5","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Gene selection from gene expression profiles is the appropriate tool for diagnosing and predicting cancers. The aim of this study is to perform a Precision Lasso regression model on gene expression of diffuse large B cell lymphoma patients and to find marker genes related to DLBCL.</p><p><strong>Methods: </strong>In the present case-control study, the dataset included 180 gene expressions from 14 healthy individuals and 17 DLBCL patients. The marker genes were selected by fitting Ridge, Lasso, Elastic Net, and Precision Lasso regression models.</p><p><strong>Results: </strong>Based on our findings, the Precision Lasso, the Ridge, the Elastic Net, and the Lasso models choose the most marker genes, respectively. In addition, the top 20 genes are based on models compared with the results of clinical studies. The Precision Lasso and the Ridge models selected the most common genes with the clinical results, respectively.</p><p><strong>Conclusions: </strong>The performance of the Precision Lasso model in selecting related genes could be considered more acceptable rather than other models.</p>","PeriodicalId":17301,"journal":{"name":"Journal of the Egyptian National Cancer Institute","volume":"35 1","pages":"19"},"PeriodicalIF":2.1000,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using the Precision Lasso for gene selection in diffuse large B cell lymphoma cancer.\",\"authors\":\"Rashed Pourhamidi, Azam Moslemi\",\"doi\":\"10.1186/s43046-023-00172-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Gene selection from gene expression profiles is the appropriate tool for diagnosing and predicting cancers. The aim of this study is to perform a Precision Lasso regression model on gene expression of diffuse large B cell lymphoma patients and to find marker genes related to DLBCL.</p><p><strong>Methods: </strong>In the present case-control study, the dataset included 180 gene expressions from 14 healthy individuals and 17 DLBCL patients. The marker genes were selected by fitting Ridge, Lasso, Elastic Net, and Precision Lasso regression models.</p><p><strong>Results: </strong>Based on our findings, the Precision Lasso, the Ridge, the Elastic Net, and the Lasso models choose the most marker genes, respectively. In addition, the top 20 genes are based on models compared with the results of clinical studies. The Precision Lasso and the Ridge models selected the most common genes with the clinical results, respectively.</p><p><strong>Conclusions: </strong>The performance of the Precision Lasso model in selecting related genes could be considered more acceptable rather than other models.</p>\",\"PeriodicalId\":17301,\"journal\":{\"name\":\"Journal of the Egyptian National Cancer Institute\",\"volume\":\"35 1\",\"pages\":\"19\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Egyptian National Cancer Institute\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s43046-023-00172-5\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Egyptian National Cancer Institute","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s43046-023-00172-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
Using the Precision Lasso for gene selection in diffuse large B cell lymphoma cancer.
Background: Gene selection from gene expression profiles is the appropriate tool for diagnosing and predicting cancers. The aim of this study is to perform a Precision Lasso regression model on gene expression of diffuse large B cell lymphoma patients and to find marker genes related to DLBCL.
Methods: In the present case-control study, the dataset included 180 gene expressions from 14 healthy individuals and 17 DLBCL patients. The marker genes were selected by fitting Ridge, Lasso, Elastic Net, and Precision Lasso regression models.
Results: Based on our findings, the Precision Lasso, the Ridge, the Elastic Net, and the Lasso models choose the most marker genes, respectively. In addition, the top 20 genes are based on models compared with the results of clinical studies. The Precision Lasso and the Ridge models selected the most common genes with the clinical results, respectively.
Conclusions: The performance of the Precision Lasso model in selecting related genes could be considered more acceptable rather than other models.
期刊介绍：
As the official publication of the National Cancer Institute, Cairo University, the Journal of the Egyptian National Cancer Institute (JENCI) is an open access peer-reviewed journal that publishes on the latest innovations in oncology and thereby, providing academics and clinicians a leading research platform. JENCI welcomes submissions pertaining to all fields of basic, applied and clinical cancer research. Main topics of interest include: local and systemic anticancer therapy (with specific interest on applied cancer research from developing countries); experimental oncology; early cancer detection; randomized trials (including negatives ones); and key emerging fields of personalized medicine, such as molecular pathology, bioinformatics, and biotechnologies.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
