Early prediction of liver cancer using longitudinal MRI

Predicting Hepatocellular Carcinoma through Supervised Machine Learning

"Identification of Biomarkers for Early-Stage Hepatocellular Carcinoma (HCC)" aims to address the critical global challenge of late-stage cancer diagnosis, which significantly lowers patient survival rates. It explores microarray gene expression datasets from GEO to identify potential early-stage biomarkers for improved patient outcomes.

Simultaneous Integration of Gene Expression and Nutrient Availability for Studying the Metabolism of Hepatocellular Carcinoma Cell Lines | Ewelina Węglarz-Tomczak, Thierry D.G.A. Mondeel, Diewertje G.E. Piebes, Hans V. Westerhoff | Biomolecules 2021

Data analysis for HCC screening study.

A classifier written in R which predicts whether a patient, diagnosed with "Hepatocellular Carcinoma", is likely to live or die within a year

Research project to track movement of Hepatoceullar Carcinoma cells.

Machine Learning pipeline for predicting 1-year survival of Hepatocellular Carcinoma patients. Includes Exploratory Data Analysis (EDA), preprocessing, supervised models (DT, KNN, RF, GB, MLP, LR, SVC, Stacking) and evaluation.

Developed a complete data science pipeline to predict one-year survival in Hepatocellular Carcinoma (HCC) patients, achieving top performance and graded 20/20.

Repository of code for experiments in the paper "Detection of Esophageal Varices and Prediction of Liver Decompensation in Unresectable Hepatocellular Carcinoma using Artificial Intelligence"

[KTH/HT17] BB2491 - Analysis of Data from High-throughput Molecular Biology Experiments (BigData) | Diary: cf. wiki