Date of Award
2022
Degree Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Statistics
First Advisor
Joan Peckham
Abstract
The goal of this thesis is to re-design and re-implement a functioning and efficient database with uniformity, coherence and most importantly transparency which clearly explains the system needs, methods and procedures openly to the users. The main users of the database are researchers, biologists, biochemists and engineers. We also developed a tool called MIC (Minimum Inhibitory Concentration) for the users of the database by which, researchers can upload their lab data, perform the calculation for average peptides with timepoints and see results using graphical visualizations.In addition, we developed a user-friendly web interface to support transparency. We revisited the normalization of the database, as this is an editable database and as it evolves, we need to add and delete attributes and change the database structure to assure consistency, correctness and efficiency. Finally, this work developed a secure web interface that assures the privacy of the users' data.
Recommended Citation
Singamaneni, Anusha, "Anti-Microbial Peptide Database" (2022). Open Access Master's Theses. Paper 2258.
https://digitalcommons.uri.edu/theses/2258
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