Semi-Supervised Learning-Based Genetic Biomarkers Dataset for Multiple-Stage Hepatocellular Carcinoma Prediction

• URL: http://arxiv.org/abs/2609.17100v1
• Date: Tue, 15 Sep 2026 12:33:12 GMT
• Title: Semi-Supervised Learning-Based Genetic Biomarkers Dataset for Multiple-Stage Hepatocellular Carcinoma Prediction
• Abstract summary: The most common form of liver cancer is carcinoma (HCC), accounting for over 90% of liver cancer cases.<n>There is a distinct lack of publicly available HCC datasets utilizing genomic data.<n>This study proposes constructing a multi-stage HCC dataset using XGBoost and Semi-Supervised learning on three separate datasets of genomic biomarkers.
• Score: 2.2251452674183922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Liver cancer is a complex disease responsible for a high number of deaths across the globe each year, making automated solutions for liver cancer classification urgent. The most common form of liver cancer is hepatocellular carcinoma (HCC), accounting for over 90% of liver cancer cases. There is a distinct lack of publicly available HCC datasets utilizing genomic data, which is necessary for training artificial intelligence (AI) models for automated HCC classification. This study proposes constructing a multi-stage HCC dataset using XGBoost and Semi-Supervised learning on three separate datasets of genomic biomarkers, utilizing their existing labels in the Semi-Supervised learning process to label the proposed dataset. The proposed dataset consists of 770 patient samples in total, categorized into five classes that represent normal tissue alongside different stages of HCC. Each sample in the dataset consists of 11,150 different gene expression levels. The XGBoost model demonstrated a final classification accuracy of 96.5% during the Semi-Supervised learning process.

Related papers

• A Digital Pathology Resource for Liver Cancer Quantification with Datasets, Benchmarks, and Tools [17.811013755898365]
  We release HepatoBench, a patch-level image database for liver cancer with annotations for seven key tissue categories.<n>Based on HepatoBench, we train and open-source a deep learning classification model as a tissue recognition tool.<n>We build HepatoQuant, an end-to-end, disease-specific regional quantification tool for liver cancer.
  arXiv  Detail & Related papers  (2026-04-22T14:13:09Z)
• Transforming Multi-Omics Integration with GANs: Applications in Alzheimer's and Cancer [0.0]
  We introduce Omics-GAN, a Generative Adversarial Network (GAN)-based framework designed to generate high-quality synthetic multi-omics profiles.<n>We demonstrated Omics-GAN on three omics types (mRNA, methylation and DNA methylation) using the ROSMAP cohort for Alzheimer's disease.<n>A support vector machine (SVM) with repeated 5-fold cross-validation improved prediction accuracy compared to original omics profiles.<n>Boxplot analyses confirmed that synthetic data preserved statistical distributions while reducing noise and outliers.
  arXiv  Detail & Related papers  (2025-10-22T05:55:49Z)
• SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers [0.3262230127283452]
  We present SurGen, a dataset comprising 1,020 H&E-stained whole-slide images (WSIs) from 843 colorectal cancer cases.<n>The dataset includes detailed annotations for key genetic mutations (KRAS, NRAS, BRAF) and mismatch repair status, as well as survival data for 426 cases.
  arXiv  Detail & Related papers  (2025-02-07T14:12:07Z)
• Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images [2.833640239679924]
  Hepatocellular carcinoma (HCC) is a common type of liver cancer whose early-stage diagnosis is a common challenge.<n>We propose a hybrid deep learning-based architecture that uses transfer learning to extract the features from pre-trained convolutional neural network (CNN) models.<n>The proposed hybrid model showed improvement in accuracy of 2% and 4% over the pre-trained models in TCGA-LIHC and KMC databases.
  arXiv  Detail & Related papers  (2024-12-04T07:26:36Z)
• LASSO-MOGAT: A Multi-Omics Graph Attention Framework for Cancer Classification [41.94295877935867]
  This paper introduces LASSO-MOGAT, a graph-based deep learning framework that integrates messenger RNA, microRNA, and DNA methylation data to classify 31 cancer types.
  arXiv  Detail & Related papers  (2024-08-30T16:26:04Z)
• MMIL: A novel algorithm for disease associated cell type discovery [58.044870442206914]
  Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. We introduce Mixture Modeling for Multiple Learning Instance (MMIL), an expectation method that enables the training and calibration of cell-level classifiers.
  arXiv  Detail & Related papers  (2024-06-12T15:22:56Z)
• Primary liver cancer classification from routine tumour biopsy using weakly supervised deep learning [10.50491279664907]
  The diagnosis of primary liver cancers (PLCs) can be challenging, especially on biopsies and for combined-cholangiocarcinoma (cHCC-CCA) We automatically classified PLCs on routine-stained biopsies using a weakly supervised learning method. Weak tumour/non-tumour annotations served as labels for training a Resnet18 neural network, and the network's last convolutional layer was used to extract new tumour tile features.
  arXiv  Detail & Related papers  (2024-04-07T15:03:46Z)
• CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H\&E stained images [42.771819949806655]
  We introduce CIMIL-CRC', a framework that solves the MSI/MSS MIL problem by efficiently combining a pre-trained feature extraction model with principal component analysis (PCA) to aggregate information from all patches. We assessed our CIMIL-CRC method using the average area under the curve (AUC) from a 5-fold cross-validation experimental setup for model development on the TCGA-CRC-DX cohort.
  arXiv  Detail & Related papers  (2024-01-29T12:56:11Z)
• Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review [77.34726150561087]
  Cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods.
  arXiv  Detail & Related papers  (2023-01-28T15:03:03Z)
• Cancer Gene Profiling through Unsupervised Discovery [49.28556294619424]
  We introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. Our signature reports promising results on distinguishing immune inflammatory and immune desert tumors.
  arXiv  Detail & Related papers  (2021-02-11T09:04:45Z)
• Topological Data Analysis of copy number alterations in cancer [70.85487611525896]
  We explore the potential to capture information contained in cancer genomic information using a novel topology-based approach. We find that this technique has the potential to extract meaningful low-dimensional representations in cancer somatic genetic data.
  arXiv  Detail & Related papers  (2020-11-22T17:31:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.