A more powerful analysis of tumor heterogeneity
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Updated
Apr 1, 2018 - Jupyter Notebook
A more powerful analysis of tumor heterogeneity
CAYA Analysis
Tumors detections on MRI slice using semantic segmentation.
Data analysis of how two TPSs determine which DVH points to export
Biorepository application for tracking biospecimens.
Research code for the investigation of the role of sample size in synthetic data generation
A systematic approach in using GEO2R for investigating gene expression differences between a low-metastatic group and the highly metastatic MDA-MB-231 (TNBC) human breast cancer cell lines.
NUS ZB4171: Building an image classification model to identify leukemia blast cells from normal healthy cells in microscopic images.
HarvardX: PH125.9x: Data Science - Capstone Breast Cancer Diagnosis Project
This work is an initial investigation into the predictive potential of a range of machine learning algorithms applied to baseline circulating biomarkers for the prognostic stratification of patients with advanced neuroendocrine tumours receiving 177Lu oxodotreotide therapy
CAYA Analysis
Deep learning model to predict chemotherapeutic sensitivity based on transcriptomic data.
Used to obtain the results in "A stochastically curtailed two-arm randomised phase II trial design for binary outcomes" (DOI: 10.1002/pst.2067) by Law et al. Superseded by package "curtailment".
The study of how cancer cells gain stemness.
A classifier prototype that predicts cancer cell line sex and genotype from gene-based sequencing read counts.
Development version of ph2rand, an R package for the design of randomized comparative phase II oncology trials
Brain Tumor Detection ML Model Trained on Kaggle Dataset
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