Toward a comprehensive drug ontology_ extraction of drug-indication relations from diverse information sources _ journal of biomedical semantics _ full text

Biomedical information overload and the potential of formal ontologies to help overcome it are well recognized [ 1– 3]. Database 11g Information overload is but one threat to the viability of the traditional pharmaceutical industry. Data recovery thumb drive Others include the rising costs of laboratory research, clinical trials, litigation over anomalous harmful side effects, and increasing times to market [ 4]. Data recovery hardware tools The success of the Gene Ontology (GO) as an in silico molecular biology research tool [ 5] suggests that drug ontologies could have a similar impact on drug research. Os x data recovery The advance of practical ontologies into the pharmaceutical domain has been much anticipated [ 6– 8], and is becoming evident [ 9, 10]. Pioneering reports on ontology-based, in silico drug discovery have emerged [ 11– 13]. R studio data recovery download The basic goal is ontology-assisted inference of surprising and/or more-likely-to-succeed new drug candidate compounds for known uses, thus cutting costs and time to market.


Data recovery western digital Drug ontology-assisted inference could also be applied to finding new uses for known compounds (drug repurposing) [ 14], or “personalized” genome-dependent safety/efficacy profiling (pharmacogenomics) [ 15– 18]. Top 5 data recovery software These ontologies include drug relations to chemically similar compounds, diseases (therapeutic classifications, indications, side effects), and biological pathways (mechanisms of action, molecular target proteins or their genes, secondary disease-gene and protein-protein interactions). Data recovery mac In principle, such ontologies could be expanded to encompass many more dimensions of drug information [ 19, 20]; that is, they can be made more comprehensive. For further progress in building comprehensive drug ontologies, rich and well-structured knowledge (content) about biological pathways and chemically similar compounds is readily available from resources such as GO, GenBank [ 21], DrugBank [ 22], PubChem [ 23], and ChemIDplus [ 24]. Database website Rich drug-disease knowledge also is readily available, but usually as unstructured (“free”) text; e.g., DailyMed [ 25]. Data recovery denver Thus the well-structured but relatively shallow WHO-ATC drug classification [ 26] has been utilized as a source for drug-disease knowledge [ 12, 13]. It is important to distinguish between diseases, indications, contraindications, side effects, and other such dimensions of drug information. Data recovery galaxy s4 A drug indication can be a disease 1 that the drug is “used for” (i.e., to treat, prevent, manage, diagnose, etc.). R studio data recovery free full version An important subset are approved indications which have been through a formal, country-specific regulatory vetting process. Data recovery equipment But drugs can also be indicated for medical conditions which may not be considered diseases, such as pregnancy. Database primary key Drugs can also be indicated for procedures, such as contrast media for radiology. Database link oracle In ontological terms, medical conditions (of which diseases are a subclass) and medical procedures constitute the range of drug indications. 7 data recovery key They also constitute the range of very different, even orthogonal, drug relations such as contraindications, precautions, and warnings. Database manager salary The range for side effects, on the other hand, is arguably limited to diseases. Database processing Thus it is important to specify which of these relations is being addressed. Database xcode This paper addresses indications, but much of it is extensible to other drug-disease relations. Database administrator salary Methods The Merck in-house version of the DID (January 2015 release) contains 198,415 rows of data representing unique quadruplets of source, raw drug/chemical name, raw indication “target” term, and indication UMLS CUI. Data recovery iphone 4s Across sources, there are 29,964 unique raw drug/chemical names, 10,938 raw indication target terms, and 192,008 unique raw drug/indication pairs. Fundamentals of database systems Additional file 1 is a copy of this spreadsheet minus 5,557 rows (3%) containing Merck proprietary information. Database er diagram Therefore reproducing these counts and the following analyses on Additional file 1 would yield slightly different quantitative results, but not substantially alter our qualitative conclusions. Data recovery tools linux Additional file 1’s “schema” worksheet shows the DID schema and two example records. Data recovery wizard for mac Drug name normalization Drug name mapping to CAS numbers is encoded in DID columns E-H. Database etl CAS numbers were assigned to 87% of the DID rows and 71% of the unique raw drug names, providing TR of the unique names to 91%. Database lock The preferred authority ChemIDplus alone covered 84% of the rows and 68% of the unique raw drug names. Data recovery reviews Almost all (98%) of these CAS number mappings are based on exact (case-insensitive) matches to the ChemIDplus’ or other standard’s PT for that CAS number, or to a source-specified synonym (“”). Database d b The synonym matches were manually curated and obvious broader term (BT) and narrower term (NT) matches were reclassified as such. Ads b database For BT and NT matches the directionality is raw-to-standard; e.g., raw “arformoterol fumarate” is a NT (a salt, derivative, analog, or formulation of) the closest ChemIDplus term which has a CAS number, “Arformoterol”. Database denormalization Also distinguished are quasi-synonym matches such as “cidofovir anhydrous”: “Cidofovir”. Pokemon y database The intent is to offer users multiple match quality levels as options for filtering. Data recovery icon The individual drug name mappings to ChEBI, ChemIDplus, and CTD are encoded in DID columns I-AC. Drug name mapping to UMLS is encoded in DID columns AD-AM. Fda 510 k database UMLS CUI mapping, compared to CAS number mapping, produced superior coverage of DID rows (96% vs. Google hacking database 87%) and unique raw DB drug names (89% vs. Database concepts 6th edition pdf 71%), and superior TR (85% vs 91%). Data recovery utility The difference is at least partly due to the higher numbers of synonym and narrower UMLS matches, which may be an artefact of unequal curation effort or UMLS’ coverage of broad classes (e.g.,“antiseptics”) which by nature do not have CAS numbers. Data recovery from hard drive Indication normalization Ninety-nine percent of DID rows represent unique triplets of raw data source (column B), drug name (column D), and indication target/substring (column AR), the other 1% representing compound matches where more than one UMLS term was needed to cover the indication concept completely. Database objects There are 10,938 unique values of the target/substring, of which 28 (0.3%) could not be mapped to UMLS. Data recovery raid 5 The rest mapped to 7,522 UMLS entry terms and thence to 6,227 UMLS PT/CUIs of the preferred semantic type (columns AT-BC), yielding a TR of 57%. Database architect Indication semantic type normalization Unlike the drug name normalization mappings, the indication UMLS mappings have a sizable prevalence of quasi-synonym match types (column AT; 46% of rows, 30% of unique target/substrings). Data recovery options This is attributable to our preference for indication normalization to phenotypic-type UMLS terms, operationalized in the semantic type normalization step. Database jobs Non-phenotypic-type terms were thus reduced from 29% of DID rows among initial UMLS mappings (columns BD-BL) to 3% among final (AT-BC), primarily terms of type “Pharmacologic Substance”/A1.4.1.1.1 (25% initial, 1% final). H2 database file The prevalence rank of “Pharmacologic Substance”/A1.4.1.1.1 changed from first to 13th, reflecting the large contributions from ChEBI, CTD, MeSH, PDR, USAN, and WHO-ATC consisting or raw therapeutic/pharmacologic class terms (e.g., “Analgesics”; “Antineoplas
tics”; “Carcinogens”). R studio data recovery serial key Indication subtypes Indication subtype data are contained in DID columns AN-AP. Database query languages These data are very preliminary and incomplete. P d database Supplementing and refining it is one of our ongoing extensions of this work. Database 101 Comparison of sources Coverage

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