ArticleInternational journal of molecular sciences2023
Artificial Intelligence and Complex Network Approaches Reveal Potential Gene Biomarkers for Hepatocellular Carcinoma.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- From black-box prediction to transparent insight: the status quo and paradigm shift of explainable artificial intelligence in hepatocellular carcinoma research.Journal of the Egyptian National Cancer Institute · 2026Review
- Article
- Current methods in explainable artificial intelligence and future prospects for integrative physiology.Pflugers Archiv : European journal of physiology · 2025Review
- Leveraging innovative diagnostics as a tool to contain superbugs.Antonie van Leeuwenhoek · 2025Review
- The Role of the Gut-Biliary-Liver Axis in Primary Hepatobiliary Liver Cancers: From Molecular Insights to Clinical Applications.Journal of personalized medicine · 2025Review
- Clinical Applications of Artificial Intelligence (AI) in Human Cancer: Is It Time to Update the Diagnostic and Predictive Models in Managing Hepatocellular Carcinoma (HCC)?Diagnostics (Basel, Switzerland) · 2025Review
- Unveiling complex patterns: An information-theoretic approach to high-order behaviors in microarray data.PloS one · 2025Article
- Network assortativity for a multidimensional evaluation of socio-economic territorial biases in university rankings.PloS one · 2025Article
- A joint complex network and machine learning approach for the identification of discriminative gene communities in autistic brain.PloS one · 2025Article
- PPM1G promotes cell proliferation via modulating mutant GOF p53 protein expression in hepatocellular carcinoma.iScience · 2024Article
- ChatGPT's performance in German OB/GYN exams - paving the way for AI-enhanced medical education and clinical practice.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, and the number of cases is constantly increasing. Early and accurate HCC diagnosis is crucial to improving the effectiveness of treatment. The aim of the study is to develop a supervised learning framework based on hierarchical community detection and artificial intelligence in order to classify patients and controls using publicly available microarray data. With our methodology, we identified 20 gene communities that discriminated between healthy and cancerous samples, with an accuracy exceeding 90%. We validated the performance of these communities on an independent dataset, and with two of them, we reached an accuracy exceeding 80%. Then, we focused on two communities, selected because they were enriched with relevant biological functions, and on these we applied an explainable artificial intelligence (XAI) approach to analyze the contribution of each gene to the classification task. In conclusion, the proposed framework provides an effective methodological and quantitative tool helping to find gene communities, which may uncover pivotal mechanisms responsible for HCC and thus discover new biomarkers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.