PMC:7371427 / 7793-8699 JSONTXT

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T43","span":{"begin":191,"end":195},"obj":"Body_part"},{"id":"T44","span":{"begin":200,"end":207},"obj":"Body_part"},{"id":"T45","span":{"begin":306,"end":310},"obj":"Body_part"},{"id":"T46","span":{"begin":311,"end":314},"obj":"Body_part"},{"id":"T47","span":{"begin":557,"end":562},"obj":"Body_part"},{"id":"T48","span":{"begin":592,"end":598},"obj":"Body_part"},{"id":"T49","span":{"begin":728,"end":735},"obj":"Body_part"},{"id":"T50","span":{"begin":740,"end":744},"obj":"Body_part"}],"attributes":[{"id":"A43","pred":"fma_id","subj":"T43","obj":"http://purl.org/sig/ont/fma/fma74402"},{"id":"A44","pred":"fma_id","subj":"T44","obj":"http://purl.org/sig/ont/fma/fma67257"},{"id":"A45","pred":"fma_id","subj":"T45","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A46","pred":"fma_id","subj":"T46","obj":"http://purl.org/sig/ont/fma/fma67095"},{"id":"A47","pred":"fma_id","subj":"T47","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A48","pred":"fma_id","subj":"T48","obj":"http://purl.org/sig/ont/fma/fma9637"},{"id":"A49","pred":"fma_id","subj":"T49","obj":"http://purl.org/sig/ont/fma/fma9637"},{"id":"A50","pred":"fma_id","subj":"T50","obj":"http://purl.org/sig/ont/fma/fma68646"}],"text":"In order to assess the veracity of these conceptual associations derived from biomedical literature, it is absolutely essential to enable triangulation with structured data sources including gene and protein expression datasets. To address this need and empower the scientific community, we built a Single Cell RNA-seq (scRNAseq) resource (https://academia.nferx.com/) which harnesses these local and global score metrics to enable seamless integration of literature-derived associations with the analysis of transcriptomes from over 2.2 million individual cells from over 50 human and mouse tissue-types (Figure 1B). Here, we use this first-in-class resource to conduct a comprehensive expression profiling of ACE2 across host tissues and cell types and discuss how the observed expression patterns correlate with the pathogenicity and viral transmission shaping the ongoing COVID-19 pandemic (Figure 1C)."}

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T16","span":{"begin":592,"end":598},"obj":"Body_part"}],"attributes":[{"id":"A16","pred":"uberon_id","subj":"T16","obj":"http://purl.obolibrary.org/obo/UBERON_0000479"}],"text":"In order to assess the veracity of these conceptual associations derived from biomedical literature, it is absolutely essential to enable triangulation with structured data sources including gene and protein expression datasets. To address this need and empower the scientific community, we built a Single Cell RNA-seq (scRNAseq) resource (https://academia.nferx.com/) which harnesses these local and global score metrics to enable seamless integration of literature-derived associations with the analysis of transcriptomes from over 2.2 million individual cells from over 50 human and mouse tissue-types (Figure 1B). Here, we use this first-in-class resource to conduct a comprehensive expression profiling of ACE2 across host tissues and cell types and discuss how the observed expression patterns correlate with the pathogenicity and viral transmission shaping the ongoing COVID-19 pandemic (Figure 1C)."}

{"project":"LitCovid-PubTator","denotations":[{"id":"37","span":{"begin":876,"end":884},"obj":"Disease"}],"attributes":[{"id":"A37","pred":"tao:has_database_id","subj":"37","obj":"MESH:C000657245"}],"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":"In order to assess the veracity of these conceptual associations derived from biomedical literature, it is absolutely essential to enable triangulation with structured data sources including gene and protein expression datasets. To address this need and empower the scientific community, we built a Single Cell RNA-seq (scRNAseq) resource (https://academia.nferx.com/) which harnesses these local and global score metrics to enable seamless integration of literature-derived associations with the analysis of transcriptomes from over 2.2 million individual cells from over 50 human and mouse tissue-types (Figure 1B). Here, we use this first-in-class resource to conduct a comprehensive expression profiling of ACE2 across host tissues and cell types and discuss how the observed expression patterns correlate with the pathogenicity and viral transmission shaping the ongoing COVID-19 pandemic (Figure 1C)."}

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T33","span":{"begin":876,"end":884},"obj":"Disease"}],"attributes":[{"id":"A33","pred":"mondo_id","subj":"T33","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"}],"text":"In order to assess the veracity of these conceptual associations derived from biomedical literature, it is absolutely essential to enable triangulation with structured data sources including gene and protein expression datasets. To address this need and empower the scientific community, we built a Single Cell RNA-seq (scRNAseq) resource (https://academia.nferx.com/) which harnesses these local and global score metrics to enable seamless integration of literature-derived associations with the analysis of transcriptomes from over 2.2 million individual cells from over 50 human and mouse tissue-types (Figure 1B). Here, we use this first-in-class resource to conduct a comprehensive expression profiling of ACE2 across host tissues and cell types and discuss how the observed expression patterns correlate with the pathogenicity and viral transmission shaping the ongoing COVID-19 pandemic (Figure 1C)."}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T73","span":{"begin":191,"end":195},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T74","span":{"begin":297,"end":298},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T75","span":{"begin":306,"end":310},"obj":"http://purl.obolibrary.org/obo/GO_0005623"},{"id":"T76","span":{"begin":557,"end":562},"obj":"http://purl.obolibrary.org/obo/GO_0005623"},{"id":"T77","span":{"begin":576,"end":581},"obj":"http://purl.obolibrary.org/obo/NCBITaxon_9606"},{"id":"T78","span":{"begin":586,"end":591},"obj":"http://purl.obolibrary.org/obo/CLO_0007836"},{"id":"T79","span":{"begin":671,"end":672},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T80","span":{"begin":740,"end":750},"obj":"http://purl.obolibrary.org/obo/CL_0000000"}],"text":"In order to assess the veracity of these conceptual associations derived from biomedical literature, it is absolutely essential to enable triangulation with structured data sources including gene and protein expression datasets. To address this need and empower the scientific community, we built a Single Cell RNA-seq (scRNAseq) resource (https://academia.nferx.com/) which harnesses these local and global score metrics to enable seamless integration of literature-derived associations with the analysis of transcriptomes from over 2.2 million individual cells from over 50 human and mouse tissue-types (Figure 1B). Here, we use this first-in-class resource to conduct a comprehensive expression profiling of ACE2 across host tissues and cell types and discuss how the observed expression patterns correlate with the pathogenicity and viral transmission shaping the ongoing COVID-19 pandemic (Figure 1C)."}

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T10","span":{"begin":200,"end":207},"obj":"Chemical"}],"attributes":[{"id":"A10","pred":"chebi_id","subj":"T10","obj":"http://purl.obolibrary.org/obo/CHEBI_36080"}],"text":"In order to assess the veracity of these conceptual associations derived from biomedical literature, it is absolutely essential to enable triangulation with structured data sources including gene and protein expression datasets. To address this need and empower the scientific community, we built a Single Cell RNA-seq (scRNAseq) resource (https://academia.nferx.com/) which harnesses these local and global score metrics to enable seamless integration of literature-derived associations with the analysis of transcriptomes from over 2.2 million individual cells from over 50 human and mouse tissue-types (Figure 1B). Here, we use this first-in-class resource to conduct a comprehensive expression profiling of ACE2 across host tissues and cell types and discuss how the observed expression patterns correlate with the pathogenicity and viral transmission shaping the ongoing COVID-19 pandemic (Figure 1C)."}

{"project":"LitCovid-sentences","denotations":[{"id":"T44","span":{"begin":0,"end":228},"obj":"Sentence"},{"id":"T45","span":{"begin":229,"end":617},"obj":"Sentence"},{"id":"T46","span":{"begin":618,"end":906},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"In order to assess the veracity of these conceptual associations derived from biomedical literature, it is absolutely essential to enable triangulation with structured data sources including gene and protein expression datasets. To address this need and empower the scientific community, we built a Single Cell RNA-seq (scRNAseq) resource (https://academia.nferx.com/) which harnesses these local and global score metrics to enable seamless integration of literature-derived associations with the analysis of transcriptomes from over 2.2 million individual cells from over 50 human and mouse tissue-types (Figure 1B). Here, we use this first-in-class resource to conduct a comprehensive expression profiling of ACE2 across host tissues and cell types and discuss how the observed expression patterns correlate with the pathogenicity and viral transmission shaping the ongoing COVID-19 pandemic (Figure 1C)."}