DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | von Luck, Kai | - |
dc.contributor.author | Flemming, Deike Maria | - |
dc.date.accessioned | 2024-10-25T10:09:08Z | - |
dc.date.available | 2024-10-25T10:09:08Z | - |
dc.date.created | 2022-02-09 | - |
dc.date.issued | 2024-10-25 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.12738/16437 | - |
dc.description.abstract | Künstliche Intelligenz (KI), insbesondere ihre Unterbereiche des maschinellen Lernens und des Deep Learning, ist ein vielversprechendes Werkzeug, das Bioinformatikern und pharmazeutischen Forschern zur Verfügung steht. Sie kann den Entwicklungsprozess von neuen Medikamenten auf vielfältige Weise unterstützen und beschleunigen. Diese Arbeit befasst sich mit Techniken der KI, die während des Forschungsprozesses angewendet werden, um neue Medikamente für bekannte und neu entdeckte Krankheiten zu entdecken. | de |
dc.description.abstract | Artificial intelligence, particularly its subsets of machine learning and deep learning, is a promising tool available to bioinformaticians and pharmaceutical researchers. It can support the drug discovery process in numerous ways that help to make the drug discovery process faster and more targeted. This thesis takes a detailed look at the various artificial intelligence techniques that are being proposed and applied during the research process to discover new medications for both well-known and newly discovered diseases and medical conditions. | en |
dc.language.iso | en | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | Drug Discovery Pipeline | en_US |
dc.subject | Künstliche Intelligenz | en_US |
dc.subject.ddc | 004: Informatik | en_US |
dc.title | How can AI support wet lab work? A literature review of current artificial intelligence techniques for the early stages of the drug discovery pipeline | en |
dc.type | Thesis | en_US |
openaire.rights | info:eu-repo/semantics/openAccess | en_US |
thesis.grantor.department | Department Informatik | en_US |
thesis.grantor.universityOrInstitution | Hochschule für Angewandte Wissenschaften Hamburg | en_US |
tuhh.contributor.referee | Lins, Christian | - |
tuhh.identifier.urn | urn:nbn:de:gbv:18302-reposit-196323 | - |
tuhh.oai.show | true | en_US |
tuhh.publication.institute | Department Informatik | en_US |
tuhh.publication.institute | Fakultät Technik und Informatik | en_US |
tuhh.type.opus | Bachelor Thesis | - |
dc.type.casrai | Supervised Student Publication | - |
dc.type.dini | bachelorThesis | - |
dc.type.driver | bachelorThesis | - |
dc.type.status | info:eu-repo/semantics/publishedVersion | en_US |
dc.type.thesis | bachelorThesis | en_US |
dcterms.DCMIType | Text | - |
tuhh.dnb.status | domain | en_US |
item.advisorGND | von Luck, Kai | - |
item.creatorGND | Flemming, Deike Maria | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
item.creatorOrcid | Flemming, Deike Maria | - |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
item.openairetype | Thesis | - |
Appears in Collections: | Theses |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
BA_How can AI support wet lab work_geschwärzt.pdf | 1.27 MB | Adobe PDF | View/Open |
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