Data · dataset · 2026
Statistical and deep learning approaches to phenotype discovery for precision oncology
Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.17034/32640612.v1
This thesis demonstrates the potential of advanced deep learning methods, specifically deep autoencoders, to extract features and reveal potential disease phenotypes in colorectal cancer in an unsupervised manner.
Description
Through the creation and validation of these tools, this research lays foundations for more tailored treatment regimens based on patient-specific disease profiles. This work supports the feasibility of applying deep learning to complex healthcare data, addressing key obstacles of data quality, dimensionality, and interpretability, which are crucial for clinical translation.<br><br><i>Thesis is embargoed until 31st July 2028</i>.<br><br>
Links
Where it is published
- DOI doi.org/10.17034/32640612.v1 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
Provenance · 3 source records, 19 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/32640612 | 13 d ago | JSON v1 |
| Deakin Research Online | oai:figshare.com:article/32640612 | 13 d ago | JSON v1 |
| DMU Figshare | oai:figshare.com:article/32640612 | 13 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[disease].local:disease:cancer | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[disease].local:disease:disease | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[field].anzsrc:field:461103 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['deep learning'] |
| concepts[field].anzsrc:field:461103 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['deep learning'] |
| concepts[field].anzsrc:field:461103 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['deep learning'] |
| concepts[field].local:field:computer-science-ai | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license_text | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |