PMC:3990764 / 8869-10985 JSONTXT

{"project":"2_test","denotations":[{"id":"24748859-19597789-44838021","span":{"begin":159,"end":160},"obj":"19597789"},{"id":"24748859-18782832-44838022","span":{"begin":1226,"end":1228},"obj":"18782832"},{"id":"24748859-20864448-44838023","span":{"begin":1230,"end":1232},"obj":"20864448"},{"id":"24748859-16381955-44838024","span":{"begin":1411,"end":1413},"obj":"16381955"},{"id":"24748859-18048412-44838025","span":{"begin":1415,"end":1417},"obj":"18048412"}],"text":"Semantic modeling for ethnicity-specific SNPs\nSemantic modeling is an emerging method for comprehensively understanding complicated BPs and spacious networks [7]. The continuous production of increasingly large-scale data in biology needs better visualization of complex and huge biological data. We constructed a semantic network model in order to analyze biologically functional implications for ethnicity-specific SNPs. Overall, network entities were used, such as \"Gene\" (records, 46,354), \"Pathway\" (records, 362), \"Disease\" (records, 9,647), \"Chemical\" (records, 153,021), \"Drug\" (records, 6,712), \"ClinicalTrials\" (records, 1,273), and \"SNP\" (records, 379), and pairwise relationships between entity-entity were curated as \"Gene-Pathway\" (records, 46,354), \"Gene-Disease\" (records, 18,391,755), \"Gene-Chemical\" (records, 308,405), \"Disease-Chemical\" (records, 401,145), \"Disease-Pathway\" (records, 43,139), \"Chemical-Pathway\" (records, 196,073), \"Chemical-Drug\" (records, 1,702), \"SNP-Gene\" (records, 379), \"ClinicalTrials-Drug\" (records, 1,419), and \"ClinicalTrials-Disease\" (records, 1,210). Entities, including \"Pathway,\" \"Chemical,\" and \"Disease,\" were collected from the Comparative Toxicogenomics Database (CTD) [14, 21], which is a public database to promote the understanding of the interaction of genes, chemical compounds, and disease networks in human health. Drugs were mapped from DrugBank [15, 22], which provides detailed drug action information. We linked a novel relationship for \"Chemical-Drug\" and \"Gene-SNP\" by curating the relationship of entities using Python ver. 2.6, and the remaining relations were collected from the CTD. Fig. 3 shows that semantic modeling of ethnicity-specific SNPs is dynamic and flexible. Hierarchy structure is where the parent can have one child, while in Directed Acyclic Graph (DAG) networks, like BioXM, the parent can have more than one child. For example, Gene A is associated with Chemical B or Pathway C. Also, Gene A is associated with Drug C, because Gene A is a curated interaction with Disease B, and Disease B is a curated association with Drug C."}

{"project":"NEUROSES","denotations":[{"id":"T336","span":{"begin":167,"end":177},"obj":"PATO_0000689"},{"id":"T337","span":{"begin":205,"end":210},"obj":"PATO_0000586"},{"id":"T338","span":{"begin":263,"end":270},"obj":"PATO_0001504"},{"id":"T339","span":{"begin":370,"end":380},"obj":"PATO_0001510"},{"id":"T340","span":{"begin":580,"end":584},"obj":"CHEBI_23888"},{"id":"T341","span":{"begin":963,"end":967},"obj":"CHEBI_23888"},{"id":"T342","span":{"begin":1030,"end":1034},"obj":"CHEBI_23888"},{"id":"T343","span":{"begin":1378,"end":1383},"obj":"CHEBI_23888"},{"id":"T344","span":{"begin":1444,"end":1448},"obj":"CHEBI_23888"},{"id":"T345","span":{"begin":1514,"end":1518},"obj":"CHEBI_23888"},{"id":"T346","span":{"begin":2001,"end":2005},"obj":"CHEBI_23888"},{"id":"T347","span":{"begin":2109,"end":2113},"obj":"CHEBI_23888"},{"id":"T348","span":{"begin":1734,"end":1742},"obj":"PATO_0001544"},{"id":"T349","span":{"begin":1754,"end":1763},"obj":"PATO_0000141"}],"text":"Semantic modeling for ethnicity-specific SNPs\nSemantic modeling is an emerging method for comprehensively understanding complicated BPs and spacious networks [7]. The continuous production of increasingly large-scale data in biology needs better visualization of complex and huge biological data. We constructed a semantic network model in order to analyze biologically functional implications for ethnicity-specific SNPs. Overall, network entities were used, such as \"Gene\" (records, 46,354), \"Pathway\" (records, 362), \"Disease\" (records, 9,647), \"Chemical\" (records, 153,021), \"Drug\" (records, 6,712), \"ClinicalTrials\" (records, 1,273), and \"SNP\" (records, 379), and pairwise relationships between entity-entity were curated as \"Gene-Pathway\" (records, 46,354), \"Gene-Disease\" (records, 18,391,755), \"Gene-Chemical\" (records, 308,405), \"Disease-Chemical\" (records, 401,145), \"Disease-Pathway\" (records, 43,139), \"Chemical-Pathway\" (records, 196,073), \"Chemical-Drug\" (records, 1,702), \"SNP-Gene\" (records, 379), \"ClinicalTrials-Drug\" (records, 1,419), and \"ClinicalTrials-Disease\" (records, 1,210). Entities, including \"Pathway,\" \"Chemical,\" and \"Disease,\" were collected from the Comparative Toxicogenomics Database (CTD) [14, 21], which is a public database to promote the understanding of the interaction of genes, chemical compounds, and disease networks in human health. Drugs were mapped from DrugBank [15, 22], which provides detailed drug action information. We linked a novel relationship for \"Chemical-Drug\" and \"Gene-SNP\" by curating the relationship of entities using Python ver. 2.6, and the remaining relations were collected from the CTD. Fig. 3 shows that semantic modeling of ethnicity-specific SNPs is dynamic and flexible. Hierarchy structure is where the parent can have one child, while in Directed Acyclic Graph (DAG) networks, like BioXM, the parent can have more than one child. For example, Gene A is associated with Chemical B or Pathway C. Also, Gene A is associated with Drug C, because Gene A is a curated interaction with Disease B, and Disease B is a curated association with Drug C."}