Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia
Genome-wide DNA methylation and gene expression are commonly altered in pediatric acute lymphoblastic leukemia (PALL). Integrated network analysis of cytosine methylation and expression datasets has the potential to provide deeper insights into the complex disease states and their causes than indivi...
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| Veröffentlicht in: | Scientific reports 2020-02, Vol.10 (1), p.2123-2123, Article 2123 |
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| description | Genome-wide DNA methylation and gene expression are commonly altered in pediatric acute lymphoblastic leukemia (PALL). Integrated network analysis of cytosine methylation and expression datasets has the potential to provide deeper insights into the complex disease states and their causes than individual disconnected analyses. With the purpose of identifying reliable cancer-associated methylation signal in gene regions from leukemia patients, we present an integrative network analysis of differentially methylated (DMGs) and differentially expressed genes (DEGs). The application of a novel signal detection-machine learning approach to methylation analysis of whole genome bisulfite sequencing (WGBS) data permitted a high level of methylation signal resolution in cancer-associated genes and pathways. This integrative network analysis approach revealed that gene expression and methylation consistently targeted the same gene pathways relevant to cancer:
Pathways in cancer, Ras signaling pathway
,
PI3K-Akt signaling pathway
, and
Rap1 signaling pathway
, among others. Detected gene hubs and hub sub-networks were integrated by signature loci associated with cancer that include, for example,
NOTCH1, RAC1, PIK3CD, BCL2
, and
EGFR
. Statistical analysis disclosed a stochastic deterministic relationship between methylation and gene expression within the set of genes simultaneously identified as DEGs and DMGs, where larger values of gene expression changes were probabilistically associated with larger values of methylation changes. Concordance analysis of the overlap between enriched pathways in DEG and DMG datasets revealed statistically significant agreement between gene expression and methylation changes. These results support the potential identification of reliable and stable methylation biomarkers at genes for cancer diagnosis and prognosis. |
| doi_str_mv | 10.1038/s41598-020-58123-2 |
| format | Article |
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Pathways in cancer, Ras signaling pathway
,
PI3K-Akt signaling pathway
, and
Rap1 signaling pathway
, among others. Detected gene hubs and hub sub-networks were integrated by signature loci associated with cancer that include, for example,
NOTCH1, RAC1, PIK3CD, BCL2
, and
EGFR
. Statistical analysis disclosed a stochastic deterministic relationship between methylation and gene expression within the set of genes simultaneously identified as DEGs and DMGs, where larger values of gene expression changes were probabilistically associated with larger values of methylation changes. Concordance analysis of the overlap between enriched pathways in DEG and DMG datasets revealed statistically significant agreement between gene expression and methylation changes. These results support the potential identification of reliable and stable methylation biomarkers at genes for cancer diagnosis and prognosis.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-020-58123-2</identifier><identifier>PMID: 32034170</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>1-Phosphatidylinositol 3-kinase ; 45 ; 45/23 ; 631/67/69 ; 692/53/2421 ; Acute lymphoblastic leukemia ; AKT protein ; Biomarkers ; Biomarkers, Tumor - genetics ; Bisulfite ; Cancer ; Computational Biology ; Cytosine ; Databases, Genetic ; DNA methylation ; DNA Methylation - genetics ; Epidermal growth factor receptors ; Gene expression ; Gene Expression Profiling - methods ; Gene Expression Regulation, Neoplastic - genetics ; Gene Regulatory Networks - genetics ; Genomes ; Humanities and Social Sciences ; Humans ; Learning algorithms ; Leukemia ; Leukemia - genetics ; Lymphatic leukemia ; Machine learning ; multidisciplinary ; Notch1 protein ; Pediatrics ; Phosphatidylinositol 3-Kinases - genetics ; Prognosis ; Protein Interaction Maps - genetics ; Proto-Oncogene Proteins c-akt - genetics ; Rac1 protein ; Rap1 protein ; Science ; Science (multidisciplinary) ; Signal transduction ; Signal Transduction - genetics ; Statistical analysis ; Stochasticity</subject><ispartof>Scientific reports, 2020-02, Vol.10 (1), p.2123-2123, Article 2123</ispartof><rights>The Author(s) 2020. corrected publication 2021</rights><rights>This work is published 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><rights>The Author(s) 2020. corrected publication 2021. This work is published 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><rights>The Author(s) 2020, corrected publication 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c568t-d5680ab728800df624d8d9b5311eded7fb169eb934dcbf6cdccac3764e7350903</citedby><cites>FETCH-LOGICAL-c568t-d5680ab728800df624d8d9b5311eded7fb169eb934dcbf6cdccac3764e7350903</cites><orcidid>0000-0002-5246-1453</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/PMC7005804/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7005804/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27924,27925,41120,42189,51576,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32034170$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sanchez, Robersy</creatorcontrib><creatorcontrib>Mackenzie, Sally A.</creatorcontrib><title>Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><description>Genome-wide DNA methylation and gene expression are commonly altered in pediatric acute lymphoblastic leukemia (PALL). Integrated network analysis of cytosine methylation and expression datasets has the potential to provide deeper insights into the complex disease states and their causes than individual disconnected analyses. With the purpose of identifying reliable cancer-associated methylation signal in gene regions from leukemia patients, we present an integrative network analysis of differentially methylated (DMGs) and differentially expressed genes (DEGs). The application of a novel signal detection-machine learning approach to methylation analysis of whole genome bisulfite sequencing (WGBS) data permitted a high level of methylation signal resolution in cancer-associated genes and pathways. This integrative network analysis approach revealed that gene expression and methylation consistently targeted the same gene pathways relevant to cancer:
Pathways in cancer, Ras signaling pathway
,
PI3K-Akt signaling pathway
, and
Rap1 signaling pathway
, among others. Detected gene hubs and hub sub-networks were integrated by signature loci associated with cancer that include, for example,
NOTCH1, RAC1, PIK3CD, BCL2
, and
EGFR
. Statistical analysis disclosed a stochastic deterministic relationship between methylation and gene expression within the set of genes simultaneously identified as DEGs and DMGs, where larger values of gene expression changes were probabilistically associated with larger values of methylation changes. Concordance analysis of the overlap between enriched pathways in DEG and DMG datasets revealed statistically significant agreement between gene expression and methylation changes. These results support the potential identification of reliable and stable methylation biomarkers at genes for cancer diagnosis and prognosis.</description><subject>1-Phosphatidylinositol 3-kinase</subject><subject>45</subject><subject>45/23</subject><subject>631/67/69</subject><subject>692/53/2421</subject><subject>Acute lymphoblastic leukemia</subject><subject>AKT protein</subject><subject>Biomarkers</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Bisulfite</subject><subject>Cancer</subject><subject>Computational Biology</subject><subject>Cytosine</subject><subject>Databases, Genetic</subject><subject>DNA methylation</subject><subject>DNA Methylation - genetics</subject><subject>Epidermal growth factor receptors</subject><subject>Gene expression</subject><subject>Gene Expression Profiling - methods</subject><subject>Gene Expression Regulation, Neoplastic - genetics</subject><subject>Gene Regulatory Networks - genetics</subject><subject>Genomes</subject><subject>Humanities and Social Sciences</subject><subject>Humans</subject><subject>Learning algorithms</subject><subject>Leukemia</subject><subject>Leukemia - genetics</subject><subject>Lymphatic leukemia</subject><subject>Machine learning</subject><subject>multidisciplinary</subject><subject>Notch1 protein</subject><subject>Pediatrics</subject><subject>Phosphatidylinositol 3-Kinases - genetics</subject><subject>Prognosis</subject><subject>Protein Interaction Maps - genetics</subject><subject>Proto-Oncogene Proteins c-akt - genetics</subject><subject>Rac1 protein</subject><subject>Rap1 protein</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Signal transduction</subject><subject>Signal Transduction - genetics</subject><subject>Statistical analysis</subject><subject>Stochasticity</subject><issn>2045-2322</issn><issn>2045-2322</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kU1vEzEQhi0EolXoH-CALHHhsuCPtdd7QSqllEgBLnC2vPY4dbOxg70J5N_jkFIKh_rgD80z74znReg5Ja8p4epNaanoVUMYaYSijDfsETplpBUN44w9vnc_QWel3JC6BOtb2j9FJ5wR3tKOnKLdPE6wzGYKO8CfYfqR8gqfRzPuSyg4efw-eA8Z4hTMOO7xJ5iu96OZwGETHb78uclQSn1dQYSCfcr4XUhrk1eQ8dwd8nywVT5FHCJewHYF62CeoSfejAXObs8Z-vbh8uvFx2bx5Wp-cb5orJBqalzdiRk6phQhzkvWOuX6QXBKwYHr_EBlD0PPW2cHL62z1ljeyRY6LkhP-Ay9PeputsManK39ZDPqTQ61xb1OJuh_IzFc62Xa6a5OS5G2Cry6Fcjp-xbKpNehWBhHEyFti2ZcMCl6Ig61Xv6H3qRtrqOslFCSKipl_yBVtTijUvBKsSNlcyolg79rmRJ98F8f_dfVf_3b_5o9Qy_uf_Yu5Y_bFeBHoNRQXEL-W_sB2V-Am7zA</recordid><startdate>20200207</startdate><enddate>20200207</enddate><creator>Sanchez, Robersy</creator><creator>Mackenzie, Sally A.</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-5246-1453</orcidid></search><sort><creationdate>20200207</creationdate><title>Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia</title><author>Sanchez, Robersy ; Mackenzie, Sally A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c568t-d5680ab728800df624d8d9b5311eded7fb169eb934dcbf6cdccac3764e7350903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>1-Phosphatidylinositol 3-kinase</topic><topic>45</topic><topic>45/23</topic><topic>631/67/69</topic><topic>692/53/2421</topic><topic>Acute lymphoblastic leukemia</topic><topic>AKT protein</topic><topic>Biomarkers</topic><topic>Biomarkers, Tumor - genetics</topic><topic>Bisulfite</topic><topic>Cancer</topic><topic>Computational Biology</topic><topic>Cytosine</topic><topic>Databases, Genetic</topic><topic>DNA methylation</topic><topic>DNA Methylation - genetics</topic><topic>Epidermal growth factor receptors</topic><topic>Gene expression</topic><topic>Gene Expression Profiling - methods</topic><topic>Gene Expression Regulation, Neoplastic - genetics</topic><topic>Gene Regulatory Networks - genetics</topic><topic>Genomes</topic><topic>Humanities and Social Sciences</topic><topic>Humans</topic><topic>Learning algorithms</topic><topic>Leukemia</topic><topic>Leukemia - genetics</topic><topic>Lymphatic leukemia</topic><topic>Machine learning</topic><topic>multidisciplinary</topic><topic>Notch1 protein</topic><topic>Pediatrics</topic><topic>Phosphatidylinositol 3-Kinases - genetics</topic><topic>Prognosis</topic><topic>Protein Interaction Maps - genetics</topic><topic>Proto-Oncogene Proteins c-akt - genetics</topic><topic>Rac1 protein</topic><topic>Rap1 protein</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><topic>Signal transduction</topic><topic>Signal Transduction - genetics</topic><topic>Statistical analysis</topic><topic>Stochasticity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sanchez, Robersy</creatorcontrib><creatorcontrib>Mackenzie, Sally A.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech 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>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</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 Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sanchez, Robersy</au><au>Mackenzie, Sally A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2020-02-07</date><risdate>2020</risdate><volume>10</volume><issue>1</issue><spage>2123</spage><epage>2123</epage><pages>2123-2123</pages><artnum>2123</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>Genome-wide DNA methylation and gene expression are commonly altered in pediatric acute lymphoblastic leukemia (PALL). Integrated network analysis of cytosine methylation and expression datasets has the potential to provide deeper insights into the complex disease states and their causes than individual disconnected analyses. With the purpose of identifying reliable cancer-associated methylation signal in gene regions from leukemia patients, we present an integrative network analysis of differentially methylated (DMGs) and differentially expressed genes (DEGs). The application of a novel signal detection-machine learning approach to methylation analysis of whole genome bisulfite sequencing (WGBS) data permitted a high level of methylation signal resolution in cancer-associated genes and pathways. This integrative network analysis approach revealed that gene expression and methylation consistently targeted the same gene pathways relevant to cancer:
Pathways in cancer, Ras signaling pathway
,
PI3K-Akt signaling pathway
, and
Rap1 signaling pathway
, among others. Detected gene hubs and hub sub-networks were integrated by signature loci associated with cancer that include, for example,
NOTCH1, RAC1, PIK3CD, BCL2
, and
EGFR
. Statistical analysis disclosed a stochastic deterministic relationship between methylation and gene expression within the set of genes simultaneously identified as DEGs and DMGs, where larger values of gene expression changes were probabilistically associated with larger values of methylation changes. Concordance analysis of the overlap between enriched pathways in DEG and DMG datasets revealed statistically significant agreement between gene expression and methylation changes. These results support the potential identification of reliable and stable methylation biomarkers at genes for cancer diagnosis and prognosis.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>32034170</pmid><doi>10.1038/s41598-020-58123-2</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-5246-1453</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | 1-Phosphatidylinositol 3-kinase 45 45/23 631/67/69 692/53/2421 Acute lymphoblastic leukemia AKT protein Biomarkers Biomarkers, Tumor - genetics Bisulfite Cancer Computational Biology Cytosine Databases, Genetic DNA methylation DNA Methylation - genetics Epidermal growth factor receptors Gene expression Gene Expression Profiling - methods Gene Expression Regulation, Neoplastic - genetics Gene Regulatory Networks - genetics Genomes Humanities and Social Sciences Humans Learning algorithms Leukemia Leukemia - genetics Lymphatic leukemia Machine learning multidisciplinary Notch1 protein Pediatrics Phosphatidylinositol 3-Kinases - genetics Prognosis Protein Interaction Maps - genetics Proto-Oncogene Proteins c-akt - genetics Rac1 protein Rap1 protein Science Science (multidisciplinary) Signal transduction Signal Transduction - genetics Statistical analysis Stochasticity |
| title | Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia |
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