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

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

Data analysis for HCC screening study.

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

Mutation-to-epigenome origin analysis with benchmark workflows and result summaries

Research project to track movement of Hepatoceullar Carcinoma cells.

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

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.

Code repository for the manuscript: 'Immunotherapy Following Induction Arterial Chemotherapy for Unresectable Hepatocellular Carcinoma — A Biomarker-Guided On-Demand Decision Framework' (3,885 patients; PSM/IPTW + RCS interaction + Target Trial Emulation)