PubMed:22156918 JSON TXT

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sentences

{"project":"sentences","denotations":[{"id":"TextSentencer_T1","span":{"begin":0,"end":139},"obj":"Sentence"},{"id":"TextSentencer_T2","span":{"begin":140,"end":306},"obj":"Sentence"},{"id":"TextSentencer_T3","span":{"begin":307,"end":374},"obj":"Sentence"},{"id":"TextSentencer_T4","span":{"begin":375,"end":499},"obj":"Sentence"},{"id":"TextSentencer_T5","span":{"begin":500,"end":584},"obj":"Sentence"},{"id":"TextSentencer_T6","span":{"begin":585,"end":681},"obj":"Sentence"},{"id":"TextSentencer_T7","span":{"begin":682,"end":792},"obj":"Sentence"},{"id":"TextSentencer_T8","span":{"begin":793,"end":1039},"obj":"Sentence"},{"id":"TextSentencer_T9","span":{"begin":1040,"end":1199},"obj":"Sentence"},{"id":"TextSentencer_T10","span":{"begin":1200,"end":1292},"obj":"Sentence"},{"id":"TextSentencer_T11","span":{"begin":1293,"end":1395},"obj":"Sentence"},{"id":"T1","span":{"begin":0,"end":139},"obj":"Sentence"},{"id":"T2","span":{"begin":140,"end":306},"obj":"Sentence"},{"id":"T3","span":{"begin":307,"end":374},"obj":"Sentence"},{"id":"T4","span":{"begin":375,"end":499},"obj":"Sentence"},{"id":"T5","span":{"begin":500,"end":584},"obj":"Sentence"},{"id":"T6","span":{"begin":585,"end":681},"obj":"Sentence"},{"id":"T7","span":{"begin":682,"end":792},"obj":"Sentence"},{"id":"T8","span":{"begin":793,"end":1039},"obj":"Sentence"},{"id":"T9","span":{"begin":1040,"end":1199},"obj":"Sentence"},{"id":"T10","span":{"begin":1200,"end":1292},"obj":"Sentence"},{"id":"T11","span":{"begin":1293,"end":1395},"obj":"Sentence"},{"id":"T1","span":{"begin":0,"end":139},"obj":"Sentence"},{"id":"T2","span":{"begin":140,"end":306},"obj":"Sentence"},{"id":"T3","span":{"begin":307,"end":374},"obj":"Sentence"},{"id":"T4","span":{"begin":375,"end":499},"obj":"Sentence"},{"id":"T5","span":{"begin":500,"end":584},"obj":"Sentence"},{"id":"T6","span":{"begin":585,"end":681},"obj":"Sentence"},{"id":"T7","span":{"begin":682,"end":792},"obj":"Sentence"},{"id":"T8","span":{"begin":793,"end":1039},"obj":"Sentence"},{"id":"T9","span":{"begin":1040,"end":1199},"obj":"Sentence"},{"id":"T10","span":{"begin":1200,"end":1292},"obj":"Sentence"},{"id":"T11","span":{"begin":1293,"end":1395},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"A quantitative structure-activity relationship (QSAR) study on glycan array data to determine the specificities of glycan-binding proteins.\nAdvances in glycan array technology have provided opportunities to automatically and systematically characterize the binding specificities of glycan-binding proteins. However, there is still a lack of robust methods for such analyses. In this study, we developed a novel quantitative structure-activity relationship (QSAR) method to analyze glycan array data. We first decomposed glycan chains into mono-, di-, tri- or tetrasaccharide subtrees. The bond information was incorporated into subtrees to help distinguish glycan chain structures. Then, we performed partial least-squares (PLS) regression on glycan array data using the subtrees as features. The application of QSAR to the glycan array data of different glycan-binding proteins demonstrated that PLS regression using subtree features can obtain higher R(2) values and a higher percentage of variance explained in glycan array intensities. Based on the regression coefficients of PLS, we were able to effectively identify subtrees that indicate the binding specificities of a glycan-binding protein. Our approach will facilitate the glycan-binding specificity analysis using the glycan array. A user-friendly web tool of the QSAR method is available at http://bci.clemson.edu/tools/glycan_array."}

GlycoBiology-FMA

{"project":"GlycoBiology-FMA","denotations":[{"id":"_T1","span":{"begin":130,"end":138},"obj":"FMAID:67257"},{"id":"_T2","span":{"begin":130,"end":138},"obj":"FMAID:165447"},{"id":"_T3","span":{"begin":297,"end":305},"obj":"FMAID:67257"},{"id":"_T4","span":{"begin":297,"end":305},"obj":"FMAID:165447"},{"id":"_T5","span":{"begin":870,"end":878},"obj":"FMAID:67257"},{"id":"_T6","span":{"begin":870,"end":878},"obj":"FMAID:165447"},{"id":"_T7","span":{"begin":1191,"end":1198},"obj":"FMAID:67257"},{"id":"_T8","span":{"begin":1191,"end":1198},"obj":"FMAID:165447"}],"namespaces":[{"prefix":"FMAID","uri":"http://purl.org/sig/ont/fma/fma"}],"text":"A quantitative structure-activity relationship (QSAR) study on glycan array data to determine the specificities of glycan-binding proteins.\nAdvances in glycan array technology have provided opportunities to automatically and systematically characterize the binding specificities of glycan-binding proteins. However, there is still a lack of robust methods for such analyses. In this study, we developed a novel quantitative structure-activity relationship (QSAR) method to analyze glycan array data. We first decomposed glycan chains into mono-, di-, tri- or tetrasaccharide subtrees. The bond information was incorporated into subtrees to help distinguish glycan chain structures. Then, we performed partial least-squares (PLS) regression on glycan array data using the subtrees as features. The application of QSAR to the glycan array data of different glycan-binding proteins demonstrated that PLS regression using subtree features can obtain higher R(2) values and a higher percentage of variance explained in glycan array intensities. Based on the regression coefficients of PLS, we were able to effectively identify subtrees that indicate the binding specificities of a glycan-binding protein. Our approach will facilitate the glycan-binding specificity analysis using the glycan array. A user-friendly web tool of the QSAR method is available at http://bci.clemson.edu/tools/glycan_array."}

GO-BP

{"project":"GO-BP","denotations":[{"id":"T1","span":{"begin":393,"end":402},"obj":"http://purl.obolibrary.org/obo/GO_0032502"}],"text":"A quantitative structure-activity relationship (QSAR) study on glycan array data to determine the specificities of glycan-binding proteins.\nAdvances in glycan array technology have provided opportunities to automatically and systematically characterize the binding specificities of glycan-binding proteins. However, there is still a lack of robust methods for such analyses. In this study, we developed a novel quantitative structure-activity relationship (QSAR) method to analyze glycan array data. We first decomposed glycan chains into mono-, di-, tri- or tetrasaccharide subtrees. The bond information was incorporated into subtrees to help distinguish glycan chain structures. Then, we performed partial least-squares (PLS) regression on glycan array data using the subtrees as features. The application of QSAR to the glycan array data of different glycan-binding proteins demonstrated that PLS regression using subtree features can obtain higher R(2) values and a higher percentage of variance explained in glycan array intensities. Based on the regression coefficients of PLS, we were able to effectively identify subtrees that indicate the binding specificities of a glycan-binding protein. Our approach will facilitate the glycan-binding specificity analysis using the glycan array. A user-friendly web tool of the QSAR method is available at http://bci.clemson.edu/tools/glycan_array."}

GO-MF

Allie

{"project":"Allie","denotations":[{"id":"SS1_22156918_0_0","span":{"begin":2,"end":46},"obj":"expanded"},{"id":"SS2_22156918_0_0","span":{"begin":48,"end":52},"obj":"abbr"},{"id":"SS1_22156918_3_0","span":{"begin":411,"end":455},"obj":"expanded"},{"id":"SS2_22156918_3_0","span":{"begin":457,"end":461},"obj":"abbr"},{"id":"SS1_22156918_6_0","span":{"begin":701,"end":722},"obj":"expanded"},{"id":"SS2_22156918_6_0","span":{"begin":724,"end":727},"obj":"abbr"}],"relations":[{"id":"AE1_22156918_0_0","pred":"abbreviatedTo","subj":"SS1_22156918_0_0","obj":"SS2_22156918_0_0"},{"id":"AE1_22156918_3_0","pred":"abbreviatedTo","subj":"SS1_22156918_3_0","obj":"SS2_22156918_3_0"},{"id":"AE1_22156918_6_0","pred":"abbreviatedTo","subj":"SS1_22156918_6_0","obj":"SS2_22156918_6_0"}],"text":"A quantitative structure-activity relationship (QSAR) study on glycan array data to determine the specificities of glycan-binding proteins.\nAdvances in glycan array technology have provided opportunities to automatically and systematically characterize the binding specificities of glycan-binding proteins. However, there is still a lack of robust methods for such analyses. In this study, we developed a novel quantitative structure-activity relationship (QSAR) method to analyze glycan array data. We first decomposed glycan chains into mono-, di-, tri- or tetrasaccharide subtrees. The bond information was incorporated into subtrees to help distinguish glycan chain structures. Then, we performed partial least-squares (PLS) regression on glycan array data using the subtrees as features. The application of QSAR to the glycan array data of different glycan-binding proteins demonstrated that PLS regression using subtree features can obtain higher R(2) values and a higher percentage of variance explained in glycan array intensities. Based on the regression coefficients of PLS, we were able to effectively identify subtrees that indicate the binding specificities of a glycan-binding protein. Our approach will facilitate the glycan-binding specificity analysis using the glycan array. A user-friendly web tool of the QSAR method is available at http://bci.clemson.edu/tools/glycan_array."}

GlyTouCan-IUPAC

Lectin-Jamboree-Sentence

{"project":"Lectin-Jamboree-Sentence","blocks":[{"id":"T1","span":{"begin":0,"end":139},"obj":"Sentence"},{"id":"T2","span":{"begin":140,"end":306},"obj":"Sentence"},{"id":"T3","span":{"begin":307,"end":374},"obj":"Sentence"},{"id":"T4","span":{"begin":375,"end":499},"obj":"Sentence"},{"id":"T5","span":{"begin":500,"end":584},"obj":"Sentence"},{"id":"T6","span":{"begin":585,"end":681},"obj":"Sentence"},{"id":"T7","span":{"begin":682,"end":792},"obj":"Sentence"},{"id":"T8","span":{"begin":793,"end":1039},"obj":"Sentence"},{"id":"T9","span":{"begin":1040,"end":1199},"obj":"Sentence"},{"id":"T10","span":{"begin":1200,"end":1292},"obj":"Sentence"},{"id":"T11","span":{"begin":1293,"end":1395},"obj":"Sentence"}],"text":"A quantitative structure-activity relationship (QSAR) study on glycan array data to determine the specificities of glycan-binding proteins.\nAdvances in glycan array technology have provided opportunities to automatically and systematically characterize the binding specificities of glycan-binding proteins. However, there is still a lack of robust methods for such analyses. In this study, we developed a novel quantitative structure-activity relationship (QSAR) method to analyze glycan array data. We first decomposed glycan chains into mono-, di-, tri- or tetrasaccharide subtrees. The bond information was incorporated into subtrees to help distinguish glycan chain structures. Then, we performed partial least-squares (PLS) regression on glycan array data using the subtrees as features. The application of QSAR to the glycan array data of different glycan-binding proteins demonstrated that PLS regression using subtree features can obtain higher R(2) values and a higher percentage of variance explained in glycan array intensities. Based on the regression coefficients of PLS, we were able to effectively identify subtrees that indicate the binding specificities of a glycan-binding protein. Our approach will facilitate the glycan-binding specificity analysis using the glycan array. A user-friendly web tool of the QSAR method is available at http://bci.clemson.edu/tools/glycan_array."}

Anatomy-UBERON

{"project":"Anatomy-UBERON","denotations":[{"id":"T1","span":{"begin":1309,"end":1312},"obj":"Body_part"}],"attributes":[{"id":"A1","pred":"uberon_id","subj":"T1","obj":"http://purl.obolibrary.org/obo/UBERON_0006015"}],"text":"A quantitative structure-activity relationship (QSAR) study on glycan array data to determine the specificities of glycan-binding proteins.\nAdvances in glycan array technology have provided opportunities to automatically and systematically characterize the binding specificities of glycan-binding proteins. However, there is still a lack of robust methods for such analyses. In this study, we developed a novel quantitative structure-activity relationship (QSAR) method to analyze glycan array data. We first decomposed glycan chains into mono-, di-, tri- or tetrasaccharide subtrees. The bond information was incorporated into subtrees to help distinguish glycan chain structures. Then, we performed partial least-squares (PLS) regression on glycan array data using the subtrees as features. The application of QSAR to the glycan array data of different glycan-binding proteins demonstrated that PLS regression using subtree features can obtain higher R(2) values and a higher percentage of variance explained in glycan array intensities. Based on the regression coefficients of PLS, we were able to effectively identify subtrees that indicate the binding specificities of a glycan-binding protein. Our approach will facilitate the glycan-binding specificity analysis using the glycan array. A user-friendly web tool of the QSAR method is available at http://bci.clemson.edu/tools/glycan_array."}