Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB): characterizing clusters of differentiation within a compendium of systemic lupus erythematosus patients
Clusters of differentiation ( ) are cell surface biomarkers that denote key biological differences between cell types and disease state. CD-targeting therapeutic monoclonal antibodies ( ) afford rich trans-disease repositioning opportunities. Within a compendium of systemic lupus erythematous ( ) pa...
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| creator | Le, Trang T Blackwood, Nigel O Taroni, Jaclyn N Fu, Weixuan Breitenstein, Matthew K |
| description | Clusters of differentiation (
) are cell surface biomarkers that denote key biological differences between cell types and disease state. CD-targeting therapeutic monoclonal antibodies (
) afford rich trans-disease repositioning opportunities. Within a compendium of systemic lupus erythematous (
) patients, we applied the Integrated machine learning pipeline for aberrant biomarker enrichment (
) to profile
gene expression features affecting CD20, CD22 and CD30 gene aberrance. First, a novel Relief-based algorithm identified interdependent features(p=681) predicting treatment-naïve SLE patients (balanced accuracy=0.822). We then compiled CD-associated expression profiles using regularized logistic regression and pathway enrichment analyses. On an independent general cell line model system data, we replicated associations (
) of
(p
=1.69e-9) and
(p
=4.63e-8) with CD22;
(p
=7.00e-4),
(p
=1.71e-2), and
(p
=3.34e-2) with CD30; and
, a phosphatase linked to bone mineralization, with both CD22(p
=4.37e-2) and CD30(p
=7.40e-3). Utilizing carefully aggregated secondary data and leveraging
hypotheses, i-mAB fostered robust biomarker profiling among interdependent biological features. |
| format | Article |
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) are cell surface biomarkers that denote key biological differences between cell types and disease state. CD-targeting therapeutic monoclonal antibodies (
) afford rich trans-disease repositioning opportunities. Within a compendium of systemic lupus erythematous (
) patients, we applied the Integrated machine learning pipeline for aberrant biomarker enrichment (
) to profile
gene expression features affecting CD20, CD22 and CD30 gene aberrance. First, a novel Relief-based algorithm identified interdependent features(p=681) predicting treatment-naïve SLE patients (balanced accuracy=0.822). We then compiled CD-associated expression profiles using regularized logistic regression and pathway enrichment analyses. On an independent general cell line model system data, we replicated associations (
) of
(p
=1.69e-9) and
(p
=4.63e-8) with CD22;
(p
=7.00e-4),
(p
=1.71e-2), and
(p
=3.34e-2) with CD30; and
, a phosphatase linked to bone mineralization, with both CD22(p
=4.37e-2) and CD30(p
=7.40e-3). Utilizing carefully aggregated secondary data and leveraging
hypotheses, i-mAB fostered robust biomarker profiling among interdependent biological features.</description><identifier>EISSN: 1559-4076</identifier><identifier>PMID: 30815180</identifier><language>eng</language><publisher>United States: American Medical Informatics Association</publisher><subject>Adolescent ; Adult ; Aged ; Antigens, CD20 - metabolism ; Biomarkers - metabolism ; Case-Control Studies ; Cell Adhesion Molecules - genetics ; Cell Adhesion Molecules - metabolism ; Cell Differentiation ; Child ; Female ; Humans ; Ki-1 Antigen - metabolism ; Lupus Erythematosus, Systemic - genetics ; Lupus Erythematosus, Systemic - metabolism ; Machine Learning ; Male ; Middle Aged ; Reference Values ; Sialic Acid Binding Ig-like Lectin 2 - metabolism ; Young Adult</subject><ispartof>AMIA ... Annual Symposium proceedings, 2018, Vol.2018, p.1358-1367</ispartof><rights>2018 AMIA - All rights reserved. 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371296/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371296/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,725,778,782,883,4012,53778,53780</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30815180$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Le, Trang T</creatorcontrib><creatorcontrib>Blackwood, Nigel O</creatorcontrib><creatorcontrib>Taroni, Jaclyn N</creatorcontrib><creatorcontrib>Fu, Weixuan</creatorcontrib><creatorcontrib>Breitenstein, Matthew K</creatorcontrib><title>Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB): characterizing clusters of differentiation within a compendium of systemic lupus erythematosus patients</title><title>AMIA ... Annual Symposium proceedings</title><addtitle>AMIA Annu Symp Proc</addtitle><description>Clusters of differentiation (
) are cell surface biomarkers that denote key biological differences between cell types and disease state. CD-targeting therapeutic monoclonal antibodies (
) afford rich trans-disease repositioning opportunities. Within a compendium of systemic lupus erythematous (
) patients, we applied the Integrated machine learning pipeline for aberrant biomarker enrichment (
) to profile
gene expression features affecting CD20, CD22 and CD30 gene aberrance. First, a novel Relief-based algorithm identified interdependent features(p=681) predicting treatment-naïve SLE patients (balanced accuracy=0.822). We then compiled CD-associated expression profiles using regularized logistic regression and pathway enrichment analyses. On an independent general cell line model system data, we replicated associations (
) of
(p
=1.69e-9) and
(p
=4.63e-8) with CD22;
(p
=7.00e-4),
(p
=1.71e-2), and
(p
=3.34e-2) with CD30; and
, a phosphatase linked to bone mineralization, with both CD22(p
=4.37e-2) and CD30(p
=7.40e-3). Utilizing carefully aggregated secondary data and leveraging
hypotheses, i-mAB fostered robust biomarker profiling among interdependent biological features.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Antigens, CD20 - metabolism</subject><subject>Biomarkers - metabolism</subject><subject>Case-Control Studies</subject><subject>Cell Adhesion Molecules - genetics</subject><subject>Cell Adhesion Molecules - metabolism</subject><subject>Cell Differentiation</subject><subject>Child</subject><subject>Female</subject><subject>Humans</subject><subject>Ki-1 Antigen - metabolism</subject><subject>Lupus Erythematosus, Systemic - genetics</subject><subject>Lupus Erythematosus, Systemic - metabolism</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Reference Values</subject><subject>Sialic Acid Binding Ig-like Lectin 2 - metabolism</subject><subject>Young Adult</subject><issn>1559-4076</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkcFq3TAQRU2hNGnaXyhapguDZFm23EUhDUkTCGSTrs1IHj2rtSRXkhNef6y_Vz2ShmQ13Jk758LMm-qYCTHULe27o-p9Sj8pbXshu3fVEaeSCSbpcfX32mfcRcg4EQd6th7JghC99Tuy2hWXQ8eESEBhjOAzUTY4iL8wEvTR6tlhaZ7a2p19-_yF6Bki6IzR_jkg9LKlIhIJhkzWGIzFbSHb4MmDzSWPANHBregnu7mDLe3LhrOaLNu6JYJxn2d0kEMqai2rhZA-VG8NLAk_PtWT6sflxd35VX1z-_36_OymXplkue7UoBC07HlvFFVy4ExKqjROkwJgQhveatNPUnIm6EBb5ENj1MRZL4yQLT-pvj5y1005nHTJjrCMa7TlBvsxgB1fT7ydx124Hzves2boCuD0CRDD7w1THp1NGpcFPIYtjQ2TPeW0EU2xfnqZ9Rzy_1v8H2j0l5o</recordid><startdate>2018</startdate><enddate>2018</enddate><creator>Le, Trang T</creator><creator>Blackwood, Nigel O</creator><creator>Taroni, Jaclyn N</creator><creator>Fu, Weixuan</creator><creator>Breitenstein, Matthew K</creator><general>American Medical Informatics Association</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>2018</creationdate><title>Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB): characterizing clusters of differentiation within a compendium of systemic lupus erythematosus patients</title><author>Le, Trang T ; Blackwood, Nigel O ; Taroni, Jaclyn N ; Fu, Weixuan ; Breitenstein, Matthew K</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p181t-6b9beac8737fb0b8931880bceddbaa15cf34cf7d883150904e392fbd3175f5843</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Aged</topic><topic>Antigens, CD20 - metabolism</topic><topic>Biomarkers - metabolism</topic><topic>Case-Control Studies</topic><topic>Cell Adhesion Molecules - genetics</topic><topic>Cell Adhesion Molecules - metabolism</topic><topic>Cell Differentiation</topic><topic>Child</topic><topic>Female</topic><topic>Humans</topic><topic>Ki-1 Antigen - metabolism</topic><topic>Lupus Erythematosus, Systemic - genetics</topic><topic>Lupus Erythematosus, Systemic - metabolism</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Reference Values</topic><topic>Sialic Acid Binding Ig-like Lectin 2 - metabolism</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Le, Trang T</creatorcontrib><creatorcontrib>Blackwood, Nigel O</creatorcontrib><creatorcontrib>Taroni, Jaclyn N</creatorcontrib><creatorcontrib>Fu, Weixuan</creatorcontrib><creatorcontrib>Breitenstein, Matthew K</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>AMIA ... Annual Symposium proceedings</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Le, Trang T</au><au>Blackwood, Nigel O</au><au>Taroni, Jaclyn N</au><au>Fu, Weixuan</au><au>Breitenstein, Matthew K</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB): characterizing clusters of differentiation within a compendium of systemic lupus erythematosus patients</atitle><jtitle>AMIA ... Annual Symposium proceedings</jtitle><addtitle>AMIA Annu Symp Proc</addtitle><date>2018</date><risdate>2018</risdate><volume>2018</volume><spage>1358</spage><epage>1367</epage><pages>1358-1367</pages><eissn>1559-4076</eissn><abstract>Clusters of differentiation (
) are cell surface biomarkers that denote key biological differences between cell types and disease state. CD-targeting therapeutic monoclonal antibodies (
) afford rich trans-disease repositioning opportunities. Within a compendium of systemic lupus erythematous (
) patients, we applied the Integrated machine learning pipeline for aberrant biomarker enrichment (
) to profile
gene expression features affecting CD20, CD22 and CD30 gene aberrance. First, a novel Relief-based algorithm identified interdependent features(p=681) predicting treatment-naïve SLE patients (balanced accuracy=0.822). We then compiled CD-associated expression profiles using regularized logistic regression and pathway enrichment analyses. On an independent general cell line model system data, we replicated associations (
) of
(p
=1.69e-9) and
(p
=4.63e-8) with CD22;
(p
=7.00e-4),
(p
=1.71e-2), and
(p
=3.34e-2) with CD30; and
, a phosphatase linked to bone mineralization, with both CD22(p
=4.37e-2) and CD30(p
=7.40e-3). Utilizing carefully aggregated secondary data and leveraging
hypotheses, i-mAB fostered robust biomarker profiling among interdependent biological features.</abstract><cop>United States</cop><pub>American Medical Informatics Association</pub><pmid>30815180</pmid><tpages>10</tpages></addata></record> |
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| ispartof | AMIA ... Annual Symposium proceedings, 2018, Vol.2018, p.1358-1367 |
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| source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central |
| subjects | Adolescent Adult Aged Antigens, CD20 - metabolism Biomarkers - metabolism Case-Control Studies Cell Adhesion Molecules - genetics Cell Adhesion Molecules - metabolism Cell Differentiation Child Female Humans Ki-1 Antigen - metabolism Lupus Erythematosus, Systemic - genetics Lupus Erythematosus, Systemic - metabolism Machine Learning Male Middle Aged Reference Values Sialic Acid Binding Ig-like Lectin 2 - metabolism Young Adult |
| title | Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB): characterizing clusters of differentiation within a compendium of systemic lupus erythematosus patients |
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