Asset Detail:
Distinguishing primary tumor from normal solid tissue in lung squamous cell carcinoma
Asset Detail:
Distinguishing primary tumor from normal solid tissue in lung squamous cell carcinoma
Overview
| ASSET LINK: | https://modac.cancer.gov/assetDetails?dme_data_id=NCI-DME-MS01-97594292 |
| PROGRAM NAME: | NCI Data Challenges |
| STUDY NAME: | NCI Cancer Research Data Commons (CRDC) Artificial Intelligence Data-Readiness (AIDR) Challenge |
| ASSET NAME: | Distinguishing primary tumor from normal solid tissue in lung squamous cell carcinoma |
| ASSET PATH: | /NCI_DOE_Archive/challenges/crdc_aidr_challenge/abhishek_jha_tier2_submission |
Asset Attributes
| ATTRIBUTE | VALUE |
|---|---|
| ASSET NAME | Distinguishing primary tumor from normal solid tissue in lung squamous cell carcinoma |
| ASSET DESCRIPTION | This asset contains the submission from Abhishek Jha and team of Elucidata, the first place winner of the 2024 AI Data Readiness Challenge for the NCI Cancer Research Data Commons (CRDC) Tier 2: Training an AI/ML model with multi-modal data. In this tier, participants must train an AI/ML model utilizing data from more than one data class. Their submission focused on Category 4 (Diagnosis). General use case: Classify cancer cells versus healthy cells in a specific tissue. Specific use case: Use of transcriptomics (RNA-seq) from Genomic Data Commons (GDC) and proteomics data from Proteomics Data Commons (PDC) to distinguish primary tumor from normal solid tissue in lung in the context of lung squamous cell carcinoma. |
| ASSET IDENTIFIER | abhishek_jha_tier2_submission |
| ASSET TYPE | Model |
| MODEL DOMAIN | classification |
| MODEL FRAMEWORK | Scikit-learn |
| MODEL PLATFORM | None |
| PLATFORM VERSION | None |
| POC NAME | Abhishek Jha |
| POC EMAIL | Abhishek.jha@elucidata.io |
| IS MODEL DEPLOYED | No |
| COLLECTION SIZE | 803.4 KB |
| CURATION STATUS | Unverified |
Asset Files
To download files, please login.
| FILE/COLLECTION | FILE SIZE | ACTIONS |
|---|
