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LitCovid-PubTator

{"project":"LitCovid-PubTator","denotations":[{"id":"1129","span":{"begin":548,"end":561},"obj":"Gene"},{"id":"1130","span":{"begin":347,"end":351},"obj":"Species"},{"id":"1131","span":{"begin":388,"end":392},"obj":"Species"}],"attributes":[{"id":"A1130","pred":"tao:has_database_id","subj":"1130","obj":"Tax:694448"},{"id":"A1131","pred":"tao:has_database_id","subj":"1131","obj":"Tax:694448"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"Network-based rational prediction of drug combinations\nFor this network-based approach for drug combinations to be effective, we need to establish if the topological relationship between two drug–target modules reflects biological and pharmacological relationships, while also quantifying their network-based relationship between drug targets and HCoV-associated host proteins (drug–drug–HCoV combinations). To identify potential drug combinations, we combined the top lists of drugs. Then, “separation” measure SAB was calculated for each pair of drugs A and B using the following method:3 \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$S_{AB} = \\left\\langle {d_{AB}} \\right\\rangle - \\frac{{\\left\\langle {d_{AA}} \\right\\rangle + \\left\\langle {d_{BB}} \\right\\rangle }}{2},$$\\end{document}SAB=dAB−dAA+dBB2,where \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$\\left\\langle {d_ \\cdot } \\right\\rangle$$\\end{document}d⋅ was calculated based on the “closest” method. Our key methodology is that a drug combination is therapeutically effective only if it follows a specific relationship to the disease module, as captured by Complementary Exposure patterns in targets’ modules of both drugs without overlapping toxic mechanisms28."}

LitCovid-PD-FMA-UBERON

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T210","span":{"begin":368,"end":376},"obj":"Body_part"}],"attributes":[{"id":"A210","pred":"fma_id","subj":"T210","obj":"http://purl.org/sig/ont/fma/fma67257"}],"text":"Network-based rational prediction of drug combinations\nFor this network-based approach for drug combinations to be effective, we need to establish if the topological relationship between two drug–target modules reflects biological and pharmacological relationships, while also quantifying their network-based relationship between drug targets and HCoV-associated host proteins (drug–drug–HCoV combinations). To identify potential drug combinations, we combined the top lists of drugs. Then, “separation” measure SAB was calculated for each pair of drugs A and B using the following method:3 \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$S_{AB} = \\left\\langle {d_{AB}} \\right\\rangle - \\frac{{\\left\\langle {d_{AA}} \\right\\rangle + \\left\\langle {d_{BB}} \\right\\rangle }}{2},$$\\end{document}SAB=dAB−dAA+dBB2,where \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$\\left\\langle {d_ \\cdot } \\right\\rangle$$\\end{document}d⋅ was calculated based on the “closest” method. Our key methodology is that a drug combination is therapeutically effective only if it follows a specific relationship to the disease module, as captured by Complementary Exposure patterns in targets’ modules of both drugs without overlapping toxic mechanisms28."}

LitCovid-PD-CLO

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T307","span":{"begin":554,"end":555},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T308","span":{"begin":560,"end":561},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T309","span":{"begin":928,"end":930},"obj":"http://purl.obolibrary.org/obo/CLO_0001627"},{"id":"T310","span":{"begin":966,"end":968},"obj":"http://purl.obolibrary.org/obo/CLO_0001869"},{"id":"T311","span":{"begin":1427,"end":1428},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T312","span":{"begin":1494,"end":1495},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"}],"text":"Network-based rational prediction of drug combinations\nFor this network-based approach for drug combinations to be effective, we need to establish if the topological relationship between two drug–target modules reflects biological and pharmacological relationships, while also quantifying their network-based relationship between drug targets and HCoV-associated host proteins (drug–drug–HCoV combinations). To identify potential drug combinations, we combined the top lists of drugs. Then, “separation” measure SAB was calculated for each pair of drugs A and B using the following method:3 \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$S_{AB} = \\left\\langle {d_{AB}} \\right\\rangle - \\frac{{\\left\\langle {d_{AA}} \\right\\rangle + \\left\\langle {d_{BB}} \\right\\rangle }}{2},$$\\end{document}SAB=dAB−dAA+dBB2,where \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$\\left\\langle {d_ \\cdot } \\right\\rangle$$\\end{document}d⋅ was calculated based on the “closest” method. Our key methodology is that a drug combination is therapeutically effective only if it follows a specific relationship to the disease module, as captured by Complementary Exposure patterns in targets’ modules of both drugs without overlapping toxic mechanisms28."}

LitCovid-PD-CHEBI

LitCovid-sentences

{"project":"LitCovid-sentences","denotations":[{"id":"T266","span":{"begin":0,"end":54},"obj":"Sentence"},{"id":"T267","span":{"begin":55,"end":407},"obj":"Sentence"},{"id":"T268","span":{"begin":408,"end":484},"obj":"Sentence"},{"id":"T269","span":{"begin":485,"end":1398},"obj":"Sentence"},{"id":"T270","span":{"begin":1399,"end":1661},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Network-based rational prediction of drug combinations\nFor this network-based approach for drug combinations to be effective, we need to establish if the topological relationship between two drug–target modules reflects biological and pharmacological relationships, while also quantifying their network-based relationship between drug targets and HCoV-associated host proteins (drug–drug–HCoV combinations). To identify potential drug combinations, we combined the top lists of drugs. Then, “separation” measure SAB was calculated for each pair of drugs A and B using the following method:3 \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$S_{AB} = \\left\\langle {d_{AB}} \\right\\rangle - \\frac{{\\left\\langle {d_{AA}} \\right\\rangle + \\left\\langle {d_{BB}} \\right\\rangle }}{2},$$\\end{document}SAB=dAB−dAA+dBB2,where \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$\\left\\langle {d_ \\cdot } \\right\\rangle$$\\end{document}d⋅ was calculated based on the “closest” method. Our key methodology is that a drug combination is therapeutically effective only if it follows a specific relationship to the disease module, as captured by Complementary Exposure patterns in targets’ modules of both drugs without overlapping toxic mechanisms28."}

2_test

{"project":"2_test","denotations":[{"id":"32194980-30867426-19614266","span":{"begin":1658,"end":1660},"obj":"30867426"}],"text":"Network-based rational prediction of drug combinations\nFor this network-based approach for drug combinations to be effective, we need to establish if the topological relationship between two drug–target modules reflects biological and pharmacological relationships, while also quantifying their network-based relationship between drug targets and HCoV-associated host proteins (drug–drug–HCoV combinations). To identify potential drug combinations, we combined the top lists of drugs. Then, “separation” measure SAB was calculated for each pair of drugs A and B using the following method:3 \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$S_{AB} = \\left\\langle {d_{AB}} \\right\\rangle - \\frac{{\\left\\langle {d_{AA}} \\right\\rangle + \\left\\langle {d_{BB}} \\right\\rangle }}{2},$$\\end{document}SAB=dAB−dAA+dBB2,where \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$\\left\\langle {d_ \\cdot } \\right\\rangle$$\\end{document}d⋅ was calculated based on the “closest” method. Our key methodology is that a drug combination is therapeutically effective only if it follows a specific relationship to the disease module, as captured by Complementary Exposure patterns in targets’ modules of both drugs without overlapping toxic mechanisms28."}