A machine-learned computational functional genomics-based approach to drug classification.
Objective: The public accessibility of 'big data' about the molecular targets of drugs and the biological functions of genes allows novel data science-based approaches to pharmacology that link drugs directly with their effects on pathophysiologic processes. This provides a phenotypic path to drug d...
| Publicado en: | European Journal of Clinical Pharmacology Vol. 72; no. 12; pp. 1449 - 1462 |
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| Autores principales: | , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=119479061&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 119479061 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00316970 NP9 jtl: European Journal of Clinical Pharmacology issn: 00316970 maglogo: N pubinfo: dt: Dec2016 vid: 72 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 119479061 119479061 144000406 119479061 10.1007/s00228-016-2134-x 119479061 ppf: 1449 ppct: 13 formats: fmt: @attributes: type: P tig: atl: A machine-learned computational functional genomics-based approach to drug classification. aug: au: Lötsch, Jörn Ultsch, Alfred affil: DataBionics Research Group , University of Marburg , Hans - Meerwein - Straße 35032 Marburg Germany sug: subj: Genomics Ontologies Data Analytics Physiopathology Human Prescriptions, Drug Classification Machinery Learning ab: Objective: The public accessibility of 'big data' about the molecular targets of drugs and the biological functions of genes allows novel data science-based approaches to pharmacology that link drugs directly with their effects on pathophysiologic processes. This provides a phenotypic path to drug discovery and repurposing. This paper compares the performance of a functional genomics-based criterion to the traditional drug target-based classification. Methods: Knowledge discovery in the DrugBank and Gene Ontology databases allowed the construction of a 'drug target versus biological process' matrix as a combination of 'drug versus genes' and 'genes versus biological processes' matrices. As a canonical example, such matrices were constructed for classical analgesic drugs. These matrices were projected onto a toroid grid of 50 × 82 artificial neurons using a self-organizing map (SOM). The distance, respectively, cluster structure of the high-dimensional feature space of the matrices was visualized on top of this SOM using a U-matrix. Results: The cluster structure emerging on the U-matrix provided a correct classification of the analgesics into two main classes of opioid and non-opioid analgesics. The classification was flawless with both the functional genomics and the traditional target-based criterion. The functional genomics approach inherently included the drugs' modulatory effects on biological processes. The main pharmacological actions known from pharmacological science were captures, e.g., actions on lipid signaling for non-opioid analgesics that comprised many NSAIDs and actions on neuronal signal transmission for opioid analgesics. Conclusions: Using machine-learned techniques for computational drug classification in a comparative assessment, a functional genomics-based criterion was found to be similarly suitable for drug classification as the traditional target-based criterion. This supports a utility of functional genomics-based approaches to computational system pharmacology for drug discovery and repurposing. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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