Classification of AML/normal status of patients from flow cytometry
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Updated
Mar 12, 2021 - Python
Classification of AML/normal status of patients from flow cytometry
This repository contains the file codes used to complete the final project for the course Post Genomic Analysis.
The Drug Therapy Risk Prediction Accelerator is an end-to-end analytics platform that predicts the likelihood of a patient to experience a fatal adverse event (death) after receiving drug therapy. It was created for COVID-19 drugs but can be used for other prescription drugs.
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Created an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans. The data used for this competition is a slightly modified version of the PatchCamelyon (PCam) benchmark dataset (does not contain duplicates)
AML ML AI Statistics
Analyses of Penter, Borji & Nagler et al., 2023
The goal of the project is to predict AML or normal status of patients from flow cytometry data (single-cell). The samples were studied with flow cytometry to quantitate the expression of different protein markers. The challenge is to determine the state of health of the other half, based only on the provided flow cytometry data.
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