Dissertation/ Thesis
Dažnų sekų algoritmo taikymas klinikinių tyrimų duomenų analizei ; Application of Frequent Sequences Algorithm in Analyses of Clinical Trial
| Τίτλος: | Dažnų sekų algoritmo taikymas klinikinių tyrimų duomenų analizei ; Application of Frequent Sequences Algorithm in Analyses of Clinical Trial |
|---|---|
| Συγγραφείς: | Pancireva, Natalija |
| Συνεισφορές: | Tumasonienė, Inga |
| Στοιχεία εκδότη: | Institutional Repository of Vilnius Gediminas Technical University |
| Έτος έκδοσης: | 2016 |
| Θεματικοί όροι: | clinical trials, frequent sequences, GSP algorithm, info, stat |
| Περιγραφή: | Impact of information technology an integral part of modern life. Any activities or business processes related to information, data collection, storage and analyses. Data mining technology allows analyses of data to obtain new facts or knowledge that scientists can use to develop new treatments methods or develop new drugs. Master thesis research area is application of frequent sequences in analyses of clinical trial data processing. In clinical trials, data processing is a complex task, because the data structure is complex, and have to evaluate many parameters. The main objective of work is to investigate frequent sequences search algorithm application for the clinical trial data analyses by processing frequent sequences, or fragments of it and to evaluate the results obtained by these algorithms application prospects. Structure: introduction, literature analyses, clinical trial data analyses, conclusions and suggestions, references. Thesis consist of: 45 pages text without appendixes, 12 pictures, 13 tables, 20 bibliographical entries. |
| Τύπος εγγράφου: | thesis |
| Γλώσσα: | unknown English |
| Relation: | http://vgtu.oai.elaba.lt/documents/8692303.pdf |
| Διαθεσιμότητα: | http://vgtu.oai.elaba.lt/documents/8692303.pdf |
| Rights: | undefined |
| Αριθμός Καταχώρησης: | edsbas.DEDFF400 |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: http://vgtu.oai.elaba.lt/documents/8692303.pdf# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Dažnų sekų algoritmo taikymas klinikinių tyrimų duomenų analizei ; Application of Frequent Sequences Algorithm in Analyses of Clinical Trial – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pancireva%2C+Natalija%22">Pancireva, Natalija</searchLink> – Name: Author Label: Contributors Group: Au Data: Tumasonienė, Inga – Name: Publisher Label: Publisher Information Group: PubInfo Data: Institutional Repository of Vilnius Gediminas Technical University – Name: DatePubCY Label: Publication Year Group: Date Data: 2016 – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22clinical+trials%22">clinical trials</searchLink><br /><searchLink fieldCode="DE" term="%22frequent+sequences%22">frequent sequences</searchLink><br /><searchLink fieldCode="DE" term="%22GSP+algorithm%22">GSP algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22info%22">info</searchLink><br /><searchLink fieldCode="DE" term="%22stat%22">stat</searchLink> – Name: Abstract Label: Description Group: Ab Data: Impact of information technology an integral part of modern life. Any activities or business processes related to information, data collection, storage and analyses. Data mining technology allows analyses of data to obtain new facts or knowledge that scientists can use to develop new treatments methods or develop new drugs. Master thesis research area is application of frequent sequences in analyses of clinical trial data processing. In clinical trials, data processing is a complex task, because the data structure is complex, and have to evaluate many parameters. The main objective of work is to investigate frequent sequences search algorithm application for the clinical trial data analyses by processing frequent sequences, or fragments of it and to evaluate the results obtained by these algorithms application prospects. Structure: introduction, literature analyses, clinical trial data analyses, conclusions and suggestions, references. Thesis consist of: 45 pages text without appendixes, 12 pictures, 13 tables, 20 bibliographical entries. – Name: TypeDocument Label: Document Type Group: TypDoc Data: thesis – Name: Language Label: Language Group: Lang Data: unknown<br />English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: http://vgtu.oai.elaba.lt/documents/8692303.pdf – Name: URL Label: Availability Group: URL Data: http://vgtu.oai.elaba.lt/documents/8692303.pdf – Name: Copyright Label: Rights Group: Cpyrght Data: undefined – Name: AN Label: Accession Number Group: ID Data: edsbas.DEDFF400 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.DEDFF400 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: unknown – Text: English Subjects: – SubjectFull: clinical trials Type: general – SubjectFull: frequent sequences Type: general – SubjectFull: GSP algorithm Type: general – SubjectFull: info Type: general – SubjectFull: stat Type: general Titles: – TitleFull: Dažnų sekų algoritmo taikymas klinikinių tyrimų duomenų analizei ; Application of Frequent Sequences Algorithm in Analyses of Clinical Trial Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pancireva, Natalija – PersonEntity: Name: NameFull: Tumasonienė, Inga IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2016 Identifiers: – Type: issn-locals Value: edsbas |
| ResultId | 1 |