Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTan, Joshua Zhi Enen_US
dc.identifier.citationTan, J. Z. E. (2022). Topology based machine learning model for the prediction of anticancer peptides. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractIn recent years, interest in the use of therapeutic peptides for treating cancer has grown vastly. A variety of approaches based on machine learning have been explored for anticancer peptide identification while the featurization of these peptides is also critical to attaining any reasonable predictive efficacy using machine learning algorithms. In this paper, we propose three topological-based featurization encodings. Machine learning models were developed using these features on two datasets: main and alternative datasets which were subsequently benchmarked with existing machine learning models. The independent testing results demonstrated that the models developed in this study had marked improvements in accuracy, specificity, and sensitivity to that of the baseline model AntiCP2.0 on both datasets. There is great potential in leveraging topological-based featurization alongside existing feature encoding techniques to accelerate the reliable identification of anticancer peptides for clinical usage.en_US
dc.publisherNanyang Technological Universityen_US
dc.titleTopology based machine learning model for the prediction of anticancer peptidesen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorXia Kelinen_US
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.description.degreeBachelor of Science in Mathematical Sciences and Economicsen_US
item.fulltextWith Fulltext-
Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
MH4900 FYP Report_TanZhiEnJoshua_U1840872E.pdf
  Restricted Access
Thesis1.62 MBAdobe PDFView/Open

Page view(s)

Updated on Jun 30, 2022


Updated on Jun 30, 2022

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.