Optimizing the Prognostic Model of Cervical Cancer Based on Artificial Intelligence Algorithm and Data Mining Technology
With the accumulation and development of medical multimodal data as well as the breakthrough in the theory and practice of artificial neural network and deep learning algorithm, the deep integration of multimodal data and artificial intelligence based on the Internet has become an important goal of...
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| Veröffentlicht in: | Wireless communications and mobile computing 2022-08, Vol.2022, p.1-10 |
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| description | With the accumulation and development of medical multimodal data as well as the breakthrough in the theory and practice of artificial neural network and deep learning algorithm, the deep integration of multimodal data and artificial intelligence based on the Internet has become an important goal of the Internet of Medical Things. The deep application of the latest technologies in the medical field, such as artificial intelligence, machine learning, multimodal data, and advanced sensors, has a profound impact on the development of medical research. Artificial intelligence can achieve low-consumption and high-efficiency screening of specific markers due to its powerful data integration and processing capabilities, and its advantages are fully demonstrated in the construction of disease-related risk prediction models. In this study, multi-type cloud data were used as research objects to explore the potential of alternative CpG sites and establish a high-quality prognosis model of cervical cancer DNA methylation big data. 14,419 strict differentially methylated CpG sites (DMCs) were identified by ChAMP methylation analysis and presented these distributions based on different genomic regions and relation to island. Further, rbsurv and Cox regression analyses were performed to construct a prognostic model integrating these four methylated CpG sites that could adequately predict the survival of patients (AUC=0.833, P |
| doi_str_mv | 10.1155/2022/5908686 |
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The deep application of the latest technologies in the medical field, such as artificial intelligence, machine learning, multimodal data, and advanced sensors, has a profound impact on the development of medical research. Artificial intelligence can achieve low-consumption and high-efficiency screening of specific markers due to its powerful data integration and processing capabilities, and its advantages are fully demonstrated in the construction of disease-related risk prediction models. In this study, multi-type cloud data were used as research objects to explore the potential of alternative CpG sites and establish a high-quality prognosis model of cervical cancer DNA methylation big data. 14,419 strict differentially methylated CpG sites (DMCs) were identified by ChAMP methylation analysis and presented these distributions based on different genomic regions and relation to island. Further, rbsurv and Cox regression analyses were performed to construct a prognostic model integrating these four methylated CpG sites that could adequately predict the survival of patients (AUC=0.833, P<0.001). The low- and high-risk patient groups, divided by risk score, showed significantly different overall survival (OS) in both the training (P<0.001) and validation datasets (P<0.005). Moreover, the model has an independent predictive value for FIGO stage and age and is more suitable for predicting survival time in patients with histological type (SCC) and histologic grade (G2/G3). Finally, the model exhibited much higher predictive accuracy compared to other known models and the corresponding expression of genes. The proposed model provides a novel signature to predict the prognosis, which can serve as a useful guide for increasing the accuracy of predicting overall survival of cervical cancer patients.</description><identifier>ISSN: 1530-8669</identifier><identifier>EISSN: 1530-8677</identifier><identifier>DOI: 10.1155/2022/5908686</identifier><language>eng</language><publisher>Oxford: Hindawi</publisher><subject>Algorithms ; Artificial intelligence ; Artificial neural networks ; Big Data ; Biomarkers ; Cancer ; Cervical cancer ; Data integration ; Data mining ; Datasets ; Deep learning ; DNA methylation ; Gene expression ; Internet of medical things ; Machine learning ; Medical research ; Prediction models ; Prognosis ; Regression analysis ; Risk ; Survival</subject><ispartof>Wireless communications and mobile computing, 2022-08, Vol.2022, p.1-10</ispartof><rights>Copyright © 2022 Yue Ma et al.</rights><rights>Copyright © 2022 Yue Ma et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c294t-97aca3f98014885010e9b8399caf982494eab824b552abcd200ae6d051ec40233</cites><orcidid>0000-0002-8925-364X ; 0000-0001-8115-5204</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><contributor>Wang, Pengfei</contributor><contributor>Pengfei Wang</contributor><creatorcontrib>Ma, Yue</creatorcontrib><creatorcontrib>Zhu, Hongbo</creatorcontrib><creatorcontrib>Yang, Zhuo</creatorcontrib><creatorcontrib>Wang, Danbo</creatorcontrib><title>Optimizing the Prognostic Model of Cervical Cancer Based on Artificial Intelligence Algorithm and Data Mining Technology</title><title>Wireless communications and mobile computing</title><description>With the accumulation and development of medical multimodal data as well as the breakthrough in the theory and practice of artificial neural network and deep learning algorithm, the deep integration of multimodal data and artificial intelligence based on the Internet has become an important goal of the Internet of Medical Things. 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Further, rbsurv and Cox regression analyses were performed to construct a prognostic model integrating these four methylated CpG sites that could adequately predict the survival of patients (AUC=0.833, P<0.001). The low- and high-risk patient groups, divided by risk score, showed significantly different overall survival (OS) in both the training (P<0.001) and validation datasets (P<0.005). Moreover, the model has an independent predictive value for FIGO stage and age and is more suitable for predicting survival time in patients with histological type (SCC) and histologic grade (G2/G3). Finally, the model exhibited much higher predictive accuracy compared to other known models and the corresponding expression of genes. The proposed model provides a novel signature to predict the prognosis, which can serve as a useful guide for increasing the accuracy of predicting overall survival of cervical cancer patients.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Big Data</subject><subject>Biomarkers</subject><subject>Cancer</subject><subject>Cervical cancer</subject><subject>Data integration</subject><subject>Data mining</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>DNA methylation</subject><subject>Gene expression</subject><subject>Internet of medical things</subject><subject>Machine learning</subject><subject>Medical research</subject><subject>Prediction models</subject><subject>Prognosis</subject><subject>Regression analysis</subject><subject>Risk</subject><subject>Survival</subject><issn>1530-8669</issn><issn>1530-8677</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kMtOwzAQRS0EEqWw4wMssYTQsRMn8bKUV6VWZVHWkeM4iavULrYLlK8nVSuWrO5o5miudBC6JnBPCGMjCpSOGIc8zdMTNCAshihPs-z0b075ObrwfgUAMVAyQN-LTdBr_aNNg0Or8JuzjbE-aInntlIdtjWeKPeppejwRBipHH4QXlXYGjx2Qdda6v40NUF1nW5UT-Bx11inQ7vGwlT4UQSB59rsK5ZKtsZ2ttldorNadF5dHXOI3p-flpPXaLZ4mU7Gs0hSnoSIZ0KKuOY5kCTPGRBQvMxjzqXolzThiRJlnyVjVJSyogBCpRUwomQCNI6H6Obwd-Psx1b5UKzs1pm-sqAZcMYpS7KeujtQ0lnvnaqLjdNr4XYFgWLvtti7LY5ue_z2gLfaVOJL_0__AiDWePI</recordid><startdate>20220825</startdate><enddate>20220825</enddate><creator>Ma, Yue</creator><creator>Zhu, Hongbo</creator><creator>Yang, Zhuo</creator><creator>Wang, Danbo</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7XB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-8925-364X</orcidid><orcidid>https://orcid.org/0000-0001-8115-5204</orcidid></search><sort><creationdate>20220825</creationdate><title>Optimizing the Prognostic Model of Cervical Cancer Based on Artificial Intelligence Algorithm and Data Mining Technology</title><author>Ma, Yue ; Zhu, Hongbo ; Yang, Zhuo ; Wang, Danbo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c294t-97aca3f98014885010e9b8399caf982494eab824b552abcd200ae6d051ec40233</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Big Data</topic><topic>Biomarkers</topic><topic>Cancer</topic><topic>Cervical cancer</topic><topic>Data integration</topic><topic>Data mining</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>DNA methylation</topic><topic>Gene expression</topic><topic>Internet of medical things</topic><topic>Machine learning</topic><topic>Medical research</topic><topic>Prediction models</topic><topic>Prognosis</topic><topic>Regression analysis</topic><topic>Risk</topic><topic>Survival</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ma, Yue</creatorcontrib><creatorcontrib>Zhu, Hongbo</creatorcontrib><creatorcontrib>Yang, Zhuo</creatorcontrib><creatorcontrib>Wang, Danbo</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>Wireless communications and mobile computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ma, Yue</au><au>Zhu, Hongbo</au><au>Yang, Zhuo</au><au>Wang, Danbo</au><au>Wang, Pengfei</au><au>Pengfei Wang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimizing the Prognostic Model of Cervical Cancer Based on Artificial Intelligence Algorithm and Data Mining Technology</atitle><jtitle>Wireless communications and mobile computing</jtitle><date>2022-08-25</date><risdate>2022</risdate><volume>2022</volume><spage>1</spage><epage>10</epage><pages>1-10</pages><issn>1530-8669</issn><eissn>1530-8677</eissn><abstract>With the accumulation and development of medical multimodal data as well as the breakthrough in the theory and practice of artificial neural network and deep learning algorithm, the deep integration of multimodal data and artificial intelligence based on the Internet has become an important goal of the Internet of Medical Things. The deep application of the latest technologies in the medical field, such as artificial intelligence, machine learning, multimodal data, and advanced sensors, has a profound impact on the development of medical research. Artificial intelligence can achieve low-consumption and high-efficiency screening of specific markers due to its powerful data integration and processing capabilities, and its advantages are fully demonstrated in the construction of disease-related risk prediction models. In this study, multi-type cloud data were used as research objects to explore the potential of alternative CpG sites and establish a high-quality prognosis model of cervical cancer DNA methylation big data. 14,419 strict differentially methylated CpG sites (DMCs) were identified by ChAMP methylation analysis and presented these distributions based on different genomic regions and relation to island. Further, rbsurv and Cox regression analyses were performed to construct a prognostic model integrating these four methylated CpG sites that could adequately predict the survival of patients (AUC=0.833, P<0.001). The low- and high-risk patient groups, divided by risk score, showed significantly different overall survival (OS) in both the training (P<0.001) and validation datasets (P<0.005). Moreover, the model has an independent predictive value for FIGO stage and age and is more suitable for predicting survival time in patients with histological type (SCC) and histologic grade (G2/G3). Finally, the model exhibited much higher predictive accuracy compared to other known models and the corresponding expression of genes. The proposed model provides a novel signature to predict the prognosis, which can serve as a useful guide for increasing the accuracy of predicting overall survival of cervical cancer patients.</abstract><cop>Oxford</cop><pub>Hindawi</pub><doi>10.1155/2022/5908686</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-8925-364X</orcidid><orcidid>https://orcid.org/0000-0001-8115-5204</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Artificial intelligence Artificial neural networks Big Data Biomarkers Cancer Cervical cancer Data integration Data mining Datasets Deep learning DNA methylation Gene expression Internet of medical things Machine learning Medical research Prediction models Prognosis Regression analysis Risk Survival |
| title | Optimizing the Prognostic Model of Cervical Cancer Based on Artificial Intelligence Algorithm and Data Mining Technology |
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