Using association rule mining to jointly detect clinical features and differentially expressed genes related to chronic inflammatory diseases
It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM), dyslipidemia (DLP) and periodontitis (PD), which are chronic inflammatory diseases. More studies able to capture unknown relationships among these diseases will contribute to raise biological and...
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| creator | Veroneze, Rosana Cruz Tfaile Corbi, Sâmia Roque da Silva, Bárbara de S Rocha, Cristiane V Maurer-Morelli, Cláudia Perez Orrico, Silvana Regina Cirelli, Joni A Von Zuben, Fernando J Mantuaneli Scarel-Caminaga, Raquel |
| description | It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM), dyslipidemia (DLP) and periodontitis (PD), which are chronic inflammatory diseases. More studies able to capture unknown relationships among these diseases will contribute to raise biological and clinical evidence. The aim of this study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features (CFs) and differentially expressed genes (DEGs) relevant to these diseases. We intend to reinforce the evidence of the T2DM-DLP-PD-interplay and demonstrate the ARM ability to provide new insights into multivariate pattern discovery.
We utilized 29 clinical glycemic, lipid and periodontal parameters from 143 patients divided into five groups based upon diabetic, dyslipidemic and periodontal conditions (including a healthy-control group). At least 5 patients from each group were selected to assess the transcriptome by microarray. ARM was utilized to assess relevant association rules considering: (i) only CFs; and (ii) CFs+DEGs, such that the identified DEGs, specific to each group of patients, were submitted to gene expression validation by quantitative polymerase chain reaction (qPCR).
We obtained 78 CF-rules and 161 CF+DEG-rules. Based on their clinical significance, Periodontists and Geneticist experts selected 11 CF-rules, and 5 CF+DEG-rules. From the five DEGs prospected by the rules, four of them were validated by qPCR as significantly different from the control group; and two of them validated the previous microarray findings.
ARM was a powerful data analysis technique to identify multivariate patterns involving clinical and molecular profiles of patients affected by specific pathological panels. ARM proved to be an effective mining approach to analyze gene expression with the advantage of including patient's CFs. A combination of CFs and DEGs might be employed in modeling the patient's chance to develop complex diseases, such as those studied here. |
| doi_str_mv | 10.1371/journal.pone.0240269 |
| format | Article |
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We utilized 29 clinical glycemic, lipid and periodontal parameters from 143 patients divided into five groups based upon diabetic, dyslipidemic and periodontal conditions (including a healthy-control group). At least 5 patients from each group were selected to assess the transcriptome by microarray. ARM was utilized to assess relevant association rules considering: (i) only CFs; and (ii) CFs+DEGs, such that the identified DEGs, specific to each group of patients, were submitted to gene expression validation by quantitative polymerase chain reaction (qPCR).
We obtained 78 CF-rules and 161 CF+DEG-rules. Based on their clinical significance, Periodontists and Geneticist experts selected 11 CF-rules, and 5 CF+DEG-rules. From the five DEGs prospected by the rules, four of them were validated by qPCR as significantly different from the control group; and two of them validated the previous microarray findings.
ARM was a powerful data analysis technique to identify multivariate patterns involving clinical and molecular profiles of patients affected by specific pathological panels. ARM proved to be an effective mining approach to analyze gene expression with the advantage of including patient's CFs. A combination of CFs and DEGs might be employed in modeling the patient's chance to develop complex diseases, such as those studied here.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0240269</identifier><identifier>PMID: 33007040</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Automation ; Biology and Life Sciences ; Computational Biology - methods ; Computer and Information Sciences ; Computer engineering ; Cytokines ; Data analysis ; Data Mining ; Dentistry ; Diabetes ; Diabetes mellitus ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus, Type 2 - genetics ; Diabetes Mellitus, Type 2 - pathology ; Diagnosis ; Disease ; DNA microarrays ; Dyslipidemia ; Dyslipidemias ; Female ; Gene expression ; Gene Expression Profiling - methods ; Genes ; Genetic aspects ; Genetics ; Genotypes ; Gum disease ; Health aspects ; Humans ; Identification and classification ; Inflammation - genetics ; Inflammation - pathology ; Inflammatory diseases ; Insulin ; Leukocytes, Mononuclear - metabolism ; Leukocytes, Mononuclear - pathology ; Lipids ; Male ; Medicine and Health Sciences ; Metabolic disorders ; Metabolism ; Middle Aged ; Morphology ; Multivariate Analysis ; Obesity ; Orthodontics ; Patients ; Pediatrics ; Periodontitis ; Periodontium ; Polymerase chain reaction ; Real-Time Polymerase Chain Reaction ; Research and Analysis Methods ; Software ; Surgery ; Teeth ; Type 2 diabetes</subject><ispartof>PloS one, 2020-10, Vol.15 (10), p.e0240269-e0240269</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Veroneze et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Veroneze et al 2020 Veroneze et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-5afc00bb9d0587247cf76190c0b7b27ee92495aed8ab5e8b8ad42fa07b1ccbe03</citedby><cites>FETCH-LOGICAL-c692t-5afc00bb9d0587247cf76190c0b7b27ee92495aed8ab5e8b8ad42fa07b1ccbe03</cites><orcidid>0000-0002-7082-9290 ; 0000-0001-5678-2070 ; 0000-0003-4007-9350</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531780/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531780/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33007040$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Veroneze, Rosana</creatorcontrib><creatorcontrib>Cruz Tfaile Corbi, Sâmia</creatorcontrib><creatorcontrib>Roque da Silva, Bárbara</creatorcontrib><creatorcontrib>de S Rocha, Cristiane</creatorcontrib><creatorcontrib>V Maurer-Morelli, Cláudia</creatorcontrib><creatorcontrib>Perez Orrico, Silvana Regina</creatorcontrib><creatorcontrib>Cirelli, Joni A</creatorcontrib><creatorcontrib>Von Zuben, Fernando J</creatorcontrib><creatorcontrib>Mantuaneli Scarel-Caminaga, Raquel</creatorcontrib><title>Using association rule mining to jointly detect clinical features and differentially expressed genes related to chronic inflammatory diseases</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM), dyslipidemia (DLP) and periodontitis (PD), which are chronic inflammatory diseases. More studies able to capture unknown relationships among these diseases will contribute to raise biological and clinical evidence. The aim of this study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features (CFs) and differentially expressed genes (DEGs) relevant to these diseases. We intend to reinforce the evidence of the T2DM-DLP-PD-interplay and demonstrate the ARM ability to provide new insights into multivariate pattern discovery.
We utilized 29 clinical glycemic, lipid and periodontal parameters from 143 patients divided into five groups based upon diabetic, dyslipidemic and periodontal conditions (including a healthy-control group). At least 5 patients from each group were selected to assess the transcriptome by microarray. ARM was utilized to assess relevant association rules considering: (i) only CFs; and (ii) CFs+DEGs, such that the identified DEGs, specific to each group of patients, were submitted to gene expression validation by quantitative polymerase chain reaction (qPCR).
We obtained 78 CF-rules and 161 CF+DEG-rules. Based on their clinical significance, Periodontists and Geneticist experts selected 11 CF-rules, and 5 CF+DEG-rules. From the five DEGs prospected by the rules, four of them were validated by qPCR as significantly different from the control group; and two of them validated the previous microarray findings.
ARM was a powerful data analysis technique to identify multivariate patterns involving clinical and molecular profiles of patients affected by specific pathological panels. ARM proved to be an effective mining approach to analyze gene expression with the advantage of including patient's CFs. A combination of CFs and DEGs might be employed in modeling the patient's chance to develop complex diseases, such as those studied here.</description><subject>Adult</subject><subject>Automation</subject><subject>Biology and Life Sciences</subject><subject>Computational Biology - methods</subject><subject>Computer and Information Sciences</subject><subject>Computer engineering</subject><subject>Cytokines</subject><subject>Data analysis</subject><subject>Data Mining</subject><subject>Dentistry</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diabetes Mellitus, Type 2 - genetics</subject><subject>Diabetes Mellitus, Type 2 - pathology</subject><subject>Diagnosis</subject><subject>Disease</subject><subject>DNA microarrays</subject><subject>Dyslipidemia</subject><subject>Dyslipidemias</subject><subject>Female</subject><subject>Gene expression</subject><subject>Gene Expression Profiling - methods</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Genetics</subject><subject>Genotypes</subject><subject>Gum disease</subject><subject>Health aspects</subject><subject>Humans</subject><subject>Identification and classification</subject><subject>Inflammation - 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metabolism</topic><topic>Leukocytes, Mononuclear - pathology</topic><topic>Lipids</topic><topic>Male</topic><topic>Medicine and Health Sciences</topic><topic>Metabolic disorders</topic><topic>Metabolism</topic><topic>Middle Aged</topic><topic>Morphology</topic><topic>Multivariate Analysis</topic><topic>Obesity</topic><topic>Orthodontics</topic><topic>Patients</topic><topic>Pediatrics</topic><topic>Periodontitis</topic><topic>Periodontium</topic><topic>Polymerase chain reaction</topic><topic>Real-Time Polymerase Chain Reaction</topic><topic>Research and Analysis Methods</topic><topic>Software</topic><topic>Surgery</topic><topic>Teeth</topic><topic>Type 2 diabetes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Veroneze, Rosana</creatorcontrib><creatorcontrib>Cruz Tfaile Corbi, Sâmia</creatorcontrib><creatorcontrib>Roque da Silva, Bárbara</creatorcontrib><creatorcontrib>de S Rocha, Cristiane</creatorcontrib><creatorcontrib>V Maurer-Morelli, Cláudia</creatorcontrib><creatorcontrib>Perez Orrico, Silvana Regina</creatorcontrib><creatorcontrib>Cirelli, Joni A</creatorcontrib><creatorcontrib>Von Zuben, Fernando J</creatorcontrib><creatorcontrib>Mantuaneli Scarel-Caminaga, Raquel</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Veroneze, Rosana</au><au>Cruz Tfaile Corbi, Sâmia</au><au>Roque da Silva, Bárbara</au><au>de S Rocha, Cristiane</au><au>V Maurer-Morelli, Cláudia</au><au>Perez Orrico, Silvana Regina</au><au>Cirelli, Joni A</au><au>Von Zuben, Fernando J</au><au>Mantuaneli Scarel-Caminaga, Raquel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using association rule mining to jointly detect clinical features and differentially expressed genes related to chronic inflammatory diseases</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020-10-02</date><risdate>2020</risdate><volume>15</volume><issue>10</issue><spage>e0240269</spage><epage>e0240269</epage><pages>e0240269-e0240269</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM), dyslipidemia (DLP) and periodontitis (PD), which are chronic inflammatory diseases. More studies able to capture unknown relationships among these diseases will contribute to raise biological and clinical evidence. The aim of this study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features (CFs) and differentially expressed genes (DEGs) relevant to these diseases. We intend to reinforce the evidence of the T2DM-DLP-PD-interplay and demonstrate the ARM ability to provide new insights into multivariate pattern discovery.
We utilized 29 clinical glycemic, lipid and periodontal parameters from 143 patients divided into five groups based upon diabetic, dyslipidemic and periodontal conditions (including a healthy-control group). At least 5 patients from each group were selected to assess the transcriptome by microarray. ARM was utilized to assess relevant association rules considering: (i) only CFs; and (ii) CFs+DEGs, such that the identified DEGs, specific to each group of patients, were submitted to gene expression validation by quantitative polymerase chain reaction (qPCR).
We obtained 78 CF-rules and 161 CF+DEG-rules. Based on their clinical significance, Periodontists and Geneticist experts selected 11 CF-rules, and 5 CF+DEG-rules. From the five DEGs prospected by the rules, four of them were validated by qPCR as significantly different from the control group; and two of them validated the previous microarray findings.
ARM was a powerful data analysis technique to identify multivariate patterns involving clinical and molecular profiles of patients affected by specific pathological panels. ARM proved to be an effective mining approach to analyze gene expression with the advantage of including patient's CFs. A combination of CFs and DEGs might be employed in modeling the patient's chance to develop complex diseases, such as those studied here.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33007040</pmid><doi>10.1371/journal.pone.0240269</doi><tpages>e0240269</tpages><orcidid>https://orcid.org/0000-0002-7082-9290</orcidid><orcidid>https://orcid.org/0000-0001-5678-2070</orcidid><orcidid>https://orcid.org/0000-0003-4007-9350</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2020-10, Vol.15 (10), p.e0240269-e0240269 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2448112493 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Adult Automation Biology and Life Sciences Computational Biology - methods Computer and Information Sciences Computer engineering Cytokines Data analysis Data Mining Dentistry Diabetes Diabetes mellitus Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - genetics Diabetes Mellitus, Type 2 - pathology Diagnosis Disease DNA microarrays Dyslipidemia Dyslipidemias Female Gene expression Gene Expression Profiling - methods Genes Genetic aspects Genetics Genotypes Gum disease Health aspects Humans Identification and classification Inflammation - genetics Inflammation - pathology Inflammatory diseases Insulin Leukocytes, Mononuclear - metabolism Leukocytes, Mononuclear - pathology Lipids Male Medicine and Health Sciences Metabolic disorders Metabolism Middle Aged Morphology Multivariate Analysis Obesity Orthodontics Patients Pediatrics Periodontitis Periodontium Polymerase chain reaction Real-Time Polymerase Chain Reaction Research and Analysis Methods Software Surgery Teeth Type 2 diabetes |
| title | Using association rule mining to jointly detect clinical features and differentially expressed genes related to chronic inflammatory diseases |
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