Deep learning mediated identification of the origins of cancer of unknown primary
|
PDF
(8MB)
Mayur Divate Thesis. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
Cancers of unknown primary (CUPs) are responsible for a significant percentage of global cancer cases, yet their origins remain a mystery. This lack of information hampers effective treatment. In our research, we harnessed the power of deep learning and pan-cancer gene expression data to predict the tissue of origin for CUPs. Our model identified cancer-specific gene expression signatures and showed promise in primary tumor diagnosis. We also explored critical cell-surface and secreted proteins and unveiled potential proto-oncogenes. Our work opens doors to more reliable diagnostic tools and has implications for early cancer detection. Explore our cloud-based tool at www.deepcap.org.
Impact and interest:
Citation counts are sourced monthly from Scopus and Web of Science® citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads:
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
| ID Code: | 246033 | ||
|---|---|---|---|
| Item Type: | QUT Thesis (PhD) | ||
| Supervisor: | Nagaraj, Shivashankar, Richard, Derek, & Gowda, Harsha | ||
| ORCID iD: |
|
||
| Keywords: | Cancer, Cancer of unknown primary, Deep learning, Gene expression, Gene expression signatures, Machine learning, Metastatic cancer, RNA-seq, SHapley Additive exPlanations, Tissue of origin | ||
| DOI: | 10.5204/thesis.eprints.246033 | ||
| Pure ID: | 155723971 | ||
| Divisions: | Current > QUT Faculties and Divisions > Faculty of Health Current > Schools > School of Biomedical Sciences |
||
| Institution: | Queensland University of Technology | ||
| Deposited On: | 01 Feb 2024 05:18 | ||
| Last Modified: | 01 Feb 2024 05:18 |
Export: EndNote | Dublin Core | BibTeX
Repository Staff Only: item control page
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)