Titel: Development of interactive software and AI-based algorithms for the analysis of biomedical data
Sprache: Englisch
Autor*in: Magruder, Daniel Sumner
GND-Schlagwörter: Künstliche IntelligenzGND
Erscheinungsdatum: 2021
Tag der mündlichen Prüfung: 2022-02-10
Drastic rise in publications and biomedical data repositories leave modern researchers with the core problem that it is not feasible to consume and understand all available data. Coupled with increasing technical complexity, researchers may thus seek avenues to promote both the accessibility and the impact of their contributions.
Progressive web applications (PWAs) may serve researchers in increasing, prolonging, and promoting the relevancy of their work; however producing them introduces a non-research related time constraint, which I herein address via a boilerplate setup for converting aca- demic contributions into PWAs. As a seemingly universal medium, PWAs make otherwise desktop or specialize software accessible even for use in classrooms (e.g. KNIT, SEA). Fur- ther, I’ve made research with expensive hardware requirements (like GPUs) accessible at one’s fingertips (e.g. SCADEN, BED.AI). As the focus is facilitating research not app pro- duction, the development of novel tools and techniques - which may latter be encapsulated in PWAs - was also fundamental for this dissertation (e.g. KNIT, SEA, scGANs, etc).
Development of PWAs provide a convenient but time-intensive solution for interfacing with complex, technical, or otherwise cost prohibitive research; however, boilerplate setups lowering time and e↵ort required to manufacture PWAs may result in an influx thereof equally diminishing their value i.e. PWAs could no longer abate publication accessibility and relevancy. Given COVID-19 and a rise in online education, PWAs may also provide a promising avenue to increase scientific literacy via interaction in classrooms. This thesis demonstrates both the need of novel bioinformatic tools (SCADEN, BED.AI, KNIT, SEA, OASIS) in their own right and their increased accessibility when coupled with PWAs.
URL: https://ediss.sub.uni-hamburg.de/handle/ediss/9506
URN: urn:nbn:de:gbv:18-ediss-99195
Dokumenttyp: Dissertation
Betreuer*in: Bonn, Stefan
Kubisch, Christian
Oertner, Thomas
Enthalten in den Sammlungen:Elektronische Dissertationen und Habilitationen

Dateien zu dieser Ressource:
Datei Beschreibung Prüfsumme GrößeFormat  
DSMagruder_Cumulative_Dissertation.pdfa69b78f68d03702753c8a7d61698246f11.9 MBAdobe PDFÖffnen/Anzeigen
Zur Langanzeige



Letzte Woche
Letzten Monat
geprüft am 17.05.2022


Letzte Woche
Letzten Monat
geprüft am 17.05.2022

Google ScholarTM