Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction
Microbes are closely associated with the formation and development of diseases. The identification of the potential associations between microbes and diseases can boost the understanding of various complex diseases. Wet experiments applied to microbe–disease association (MDA) identification are cost...
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doaj-330878f185114431b05907aa373272a32021-03-16T05:45:36ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2021-03-011210.3389/fmicb.2021.650056650056Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association PredictionYu Chen0Hongjian Sun1Mengzhe Sun2Changguo Shi3Hongmei Sun4Xiaoli Shi5Xiaoli Shi6Binbin Ji7Binbin Ji8Jinpeng Cui9The Cancer Hospital of Jia Mu Si, Jiamusi, ChinaOncological Surgery, The Central Hospital of Jia Mu Si, Jiamusi, ChinaOncological Surgery, The Central Hospital of Jia Mu Si, Jiamusi, ChinaDepartment of Thoracic Surgery, The Cancer Hospital of Jia Mu Si, Jiamusi, ChinaMedical Oncology, The Cancer Hospital of Jia Mu Si, Jiamusi, ChinaGeneis Beijing Co., Ltd., Beijing, ChinaQingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, ChinaGeneis Beijing Co., Ltd., Beijing, ChinaQingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, ChinaDepartment of Laboratory Medicine, Yantaishan Hospital of Yantai City, Yantai, ChinaMicrobes are closely associated with the formation and development of diseases. The identification of the potential associations between microbes and diseases can boost the understanding of various complex diseases. Wet experiments applied to microbe–disease association (MDA) identification are costly and time-consuming. In this manuscript, we developed a novel computational model, NLLMDA, to find unobserved MDAs, especially for colon cancer and colorectal carcinoma. NLLMDA integrated negative MDA selection, linear neighborhood similarity, label propagation, information integration, and known biological data. The Gaussian association profile (GAP) similarity of microbes and GAPs similarity and symptom similarity of diseases were firstly computed. Secondly, linear neighborhood method was then applied to the above computed similarity matrices to obtain more stable performance. Thirdly, negative MDA samples were selected, and the label propagation algorithm was used to score for microbe–disease pairs. The final association probabilities can be computed based on the information integration method. NLLMDA was compared with the other five classical MDA methods and obtained the highest area under the curve (AUC) value of 0.9031 and 0.9335 on cross-validations of diseases and microbe–disease pairs. The results suggest that NLLMDA was an effective prediction method. More importantly, we found that Acidobacteriaceae may have a close link with colon cancer and Tannerella may densely associate with colorectal carcinoma.https://www.frontiersin.org/articles/10.3389/fmicb.2021.650056/fullmicrobe–disease associationnegative sample selectionlinear neighborhood similaritylabel propagationinformation integrationcolon cancer |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yu Chen Hongjian Sun Mengzhe Sun Changguo Shi Hongmei Sun Xiaoli Shi Xiaoli Shi Binbin Ji Binbin Ji Jinpeng Cui |
spellingShingle |
Yu Chen Hongjian Sun Mengzhe Sun Changguo Shi Hongmei Sun Xiaoli Shi Xiaoli Shi Binbin Ji Binbin Ji Jinpeng Cui Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction Frontiers in Microbiology microbe–disease association negative sample selection linear neighborhood similarity label propagation information integration colon cancer |
author_facet |
Yu Chen Hongjian Sun Mengzhe Sun Changguo Shi Hongmei Sun Xiaoli Shi Xiaoli Shi Binbin Ji Binbin Ji Jinpeng Cui |
author_sort |
Yu Chen |
title |
Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction |
title_short |
Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction |
title_full |
Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction |
title_fullStr |
Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction |
title_full_unstemmed |
Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction |
title_sort |
finding colon cancer- and colorectal cancer-related microbes based on microbe–disease association prediction |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Microbiology |
issn |
1664-302X |
publishDate |
2021-03-01 |
description |
Microbes are closely associated with the formation and development of diseases. The identification of the potential associations between microbes and diseases can boost the understanding of various complex diseases. Wet experiments applied to microbe–disease association (MDA) identification are costly and time-consuming. In this manuscript, we developed a novel computational model, NLLMDA, to find unobserved MDAs, especially for colon cancer and colorectal carcinoma. NLLMDA integrated negative MDA selection, linear neighborhood similarity, label propagation, information integration, and known biological data. The Gaussian association profile (GAP) similarity of microbes and GAPs similarity and symptom similarity of diseases were firstly computed. Secondly, linear neighborhood method was then applied to the above computed similarity matrices to obtain more stable performance. Thirdly, negative MDA samples were selected, and the label propagation algorithm was used to score for microbe–disease pairs. The final association probabilities can be computed based on the information integration method. NLLMDA was compared with the other five classical MDA methods and obtained the highest area under the curve (AUC) value of 0.9031 and 0.9335 on cross-validations of diseases and microbe–disease pairs. The results suggest that NLLMDA was an effective prediction method. More importantly, we found that Acidobacteriaceae may have a close link with colon cancer and Tannerella may densely associate with colorectal carcinoma. |
topic |
microbe–disease association negative sample selection linear neighborhood similarity label propagation information integration colon cancer |
url |
https://www.frontiersin.org/articles/10.3389/fmicb.2021.650056/full |
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