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Carcinoma  \nAntonio Lacalamita 1,2,†, Grazia Serino 3,†, Ester Pantaleo 1,2, Alfonso Monaco 1,2, *, Nicola Amoroso 2,4, Loredana Bellantuono 2,5, Emanuele Piccinno 3, Viviana Scalavino 3, Francesco Dituri 3, Sabina Tangaro 2,6, Roberto Bellotti 1,2,‡ and Gianluigi Giannelli 3,‡  \nCitation: Lacalamita, A.; Serino, G.; Pantaleo, E.; Monaco, A.; Amoroso, N.; Bellantuono, L.; Piccinno, E.; Scalavino, V.; Dituri, F.; Tangaro, S.; et al. Artificial Intelligence and Complex Network Approaches Reveal Potential Gene Biomarkers for Hepatocellular Carcinoma. Int. J. Mol. Sci. 2023, 24, 15286. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/ijms242015286](10.3390/ijms242015286)  \nAcademic Editor: Nam Deuk Kim  \nReceived: 11 September 2023  \nRevised: 5 October 2023  \nAccepted: 16 October 2023  \nPublished: 18 October 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Dipartimento Interateneo di Fisica M. Merlin, Universit degli Studi di Bari Aldo Moro, Via G. Amendola 173,  \n70125 Bari, Italy; antonio.lacalamita@uniba.it (A.L.); [ester.pantaleo@uniba.it](ester.pantaleo@uniba.it) (E.P.);  \n[roberto.bellotti@ba.infn.it](roberto.bellotti@ba.infn.it) (R.B.)  \n2 Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, 70125 Bari, Italy;  \n[nicola.amoroso@uniba.it](nicola.amoroso@uniba.it) (N.A.); [loredana.bellantuono@ba.infn.it](loredana.bellantuono@ba.infn.it) (L.B.); [sonia.tangaro@ba.infn.it](sonia.tangaro@ba.infn.it) (S.T.)  \n3 National Institute of Gastroenterology S. De Bellis, IRCCS Research Hospital, Via Turi 27,  \n70013 Castellana Grotte, BA, Italy; [grazia.serino@irccsdebellis.it](grazia.serino@irccsdebellis.it) (G.S.);  \n[emanuele.piccinno@irccsdebellis.it](emanuele.piccinno@irccsdebellis.it) (E.P.); [viviana.scalavino@irccsdebellis.it](viviana.scalavino@irccsdebellis.it) (V.S.);  \nfrancesco.dituri@irccsdebellis.it (F.D.); gianluigi.giannelli@irccsdebellis.it (G.G.)  \n4 Dipartimento di Farmacia-Scienze del Farmaco, Universit degli Studi di Bari Aldo Moro, Via A. Orabona 4, 70125 Bari, Italy  \n5 Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Universit degli Studi di Bari Aldo Moro, Piazza G. Cesare 11, 70124 Bari, Italy  \n6 Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universit degli Studi di Bari Aldo Moro, Via G. Amendola 165/a, 70126 Bari, Italy  \n* Correspondence: [alfonso.monaco@ba.infn.it](alfonso.monaco@ba.infn.it)[ ](alfonso.monaco@ba.infn.it)† These authors contributed equally to this work.  \n‡ These authors contributed equally to this work.  \nAbstract: 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 conclus","cbCaihu0POzZptgT","https://ap.wps.com/l/cbCaihu0POzZptgT","pdf",1116513,13,"English","# Introduction\n## Hepatocellular carcinoma and the need for early diagnosis\n## Limitations of serum biomarkers such as AFP\n## Rationale for combining multiple biomarkers\n# Methods\n## Supervised learning framework with hierarchical community detection\n## Gene community identification and classification\n## Explainable AI (XAI) gene contribution analysis\n# Results\n## Discriminative gene communities and classification accuracy\n## Validation on independent dataset\n## Functional enrichment and key gene contributions\n# Conclusion","[{\"question\":\"What is the main goal of the study on hepatocellular carcinoma?\",\"answer\":\"To build a supervised learning framework that identifies gene communities and helps classify patients versus controls for improved HCC diagnosis.\"},{\"question\":\"How does the framework use complex networks and artificial intelligence?\",\"answer\":\"It applies hierarchical community detection to gene networks and uses supervised learning to classify samples based on the resulting gene communities.\"},{\"question\":\"What role does explainable AI (XAI) play in the analysis?\",\"answer\":\"After selecting biologically relevant communities, XAI is used to assess how each gene contributes to the classification task, supporting biomarker discovery.\"}]","Artificial Intelligence and Complex Network Approaches Reveal Potential Gene Biomarkers for Hepatocellular Carcinoma | PDF",1790224335,33]