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Asset Detail:
Gene expression-based prediction of treatment response in ovarian cancer
Asset Detail:
Gene expression-based prediction of treatment response in ovarian cancer
Overview
| ASSET LINK: | https://modac.cancer.gov/assetDetails?dme_data_id=NCI-DME-MS01-98736781 |
| PROGRAM NAME: | NCI Data Challenges |
| STUDY NAME: | NCI Cancer Research Data Commons (CRDC) Artificial Intelligence Data-Readiness (AIDR) Challenge |
| ASSET NAME: | Gene expression-based prediction of treatment response in ovarian cancer |
| ASSET PATH: | /NCI_DOE_Archive/challenges/crdc_aidr_challenge/jennifer_blase_tier1_submission |
Asset Attributes
| ATTRIBUTE | VALUE |
|---|---|
| ASSET NAME | Gene expression-based prediction of treatment response in ovarian cancer |
| ASSET DESCRIPTION | This asset contains the submission from Jennifer Blase and team of Ruvos, the first place winner of the 2024 AI Data Readiness Challenge for the NCI Cancer Research Data Commons (CRDC) Tier 1: Training an AI/ML model with single modal data. In this tier, participants must train an AI/ML model utilizing data from a single data class. Their submission focused on Category 8 (Treatment). General use case: Predict the efficacy of a single or combination therapy. Specific use case: Use gene expression data from Genomic Data Commons to predict treatment-response in patients with ovarian cancer. |
| ASSET IDENTIFIER | jennifer_blase_tier1_submission |
| ASSET TYPE | Model |
| MODEL DOMAIN | classification |
| MODEL FRAMEWORK | Scikit-learn |
| MODEL PLATFORM | None |
| PLATFORM VERSION | None |
| POC NAME | Jennifer Blase |
| POC EMAIL | jblase@ruvos.com |
| IS MODEL DEPLOYED | No |
| COLLECTION SIZE | 545.0 KB |
| CURATION STATUS | Unverified |
Asset Files
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| FILE/COLLECTION | FILE SIZE | ACTIONS |
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