Advancements in Artificial Intelligence (AI) for Cancer Genomics and Genetics

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cancer Biology and Oncology".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1232

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Istituto di Biologia e Patologia Molecolari del Consiglio Nazionale delle Ricerche (IBPM-CNR), Dipartimento di Biologia e Biotecnologie, Università Sapienza di Roma, Piazzale Aldo Moro 5, 00185 Rome, Italy
Interests: chromatin structure and function; heterochromatin; Drosophila melanogaster; mitosis and male meiosis; cytokinesis; DNA repair; cancer epigenetics
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Dear Colleagues,

The progress made through the application of computational approaches to biology and medicine requires a broad expertise for the management, processing, analysis, and interpretation of the results produced using very heterogeneous data. Inter-disciplinary collaborations and resources are key because large amounts of information cannot be handled by single scientists working in their specialized fields. Data need to be collected, reviewed, processed, and analyzed in order to create models aimed at understanding diseases at the molecular level, allowing the basis for personalized medicine to be established. Research regarding the neoplastic transformation will significantly benefit from this progress as well, and the use of Artificial Intelligence (AI) to leverage large datasets is expected to become more and more important.

Cancer is a multistep and complex disease; its onset is influenced by several factors including—but not limited to—the genetic background of patients, their lifestyle, the environment in which they live, and multiple interactions among these factors. Moreso than before, cancer characterization cannot be limited to the evaluation of cytological/histological features and the testing of a few biomarkers. An integrated approach and the use of AI appear necessary, involving not only high-throughput data analysis, but also its contextualization in order to identify the best tools for diagnosis, prognosis, and treatment. 

We are pleased to invite you to submit your contribution, with the goal of understanding and characterizing human cancer in terms of all the aforementioned aspects. Comprehensive reviews, illustrating the state of the art in the use of AI in cancer genetics/genomics, are equally welcome.

Research areas may include (but are not limited to) the following: (i) the use of AI to collect, filter, and/or analyze large datasets for cancer characterization; (ii) the use of genetics and genomics approaches in cancer; (iii) the response of genes to complex environmental insults; (iv) studies based on cancer transcriptomics; ad (v) new approaches for the early diagnosis and personalized treatment of cancer, as well as related topics.

We are looking forward to receiving your contribution.

Dr. Roberto Piergentili
Guest Editor

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Keywords

  • artificial intelligence
  • cancer
  • genetics
  • epigenetics
  • genomics
  • epigenomics
  • gene-environment interaction
  • cancer diagnosis and prognosis
  • personalized medicine
  • drug discovery

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Published Papers (1 paper)

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25 pages, 2816 KiB  
Article
GastricAITool: A Clinical Decision Support Tool for the Diagnosis and Prognosis of Gastric Cancer
by Rocío Aznar-Gimeno, María Asunción García-González, Rubén Muñoz-Sierra, Patricia Carrera-Lasfuentes, María de la Vega Rodrigálvarez-Chamarro, Carlos González-Muñoz, Enrique Meléndez-Estrada, Ángel Lanas and Rafael del Hoyo-Alonso
Biomedicines 2024, 12(9), 2162; https://doi.org/10.3390/biomedicines12092162 - 23 Sep 2024
Viewed by 722
Abstract
Background/Objective: Gastric cancer (GC) is a complex disease representing a significant global health concern. Advanced tools for the early diagnosis and prediction of adverse outcomes are crucial. In this context, artificial intelligence (AI) plays a fundamental role. The aim of this work was [...] Read more.
Background/Objective: Gastric cancer (GC) is a complex disease representing a significant global health concern. Advanced tools for the early diagnosis and prediction of adverse outcomes are crucial. In this context, artificial intelligence (AI) plays a fundamental role. The aim of this work was to develop a diagnostic and prognostic tool for GC, providing support to clinicians in critical decision-making and enabling personalised strategies. Methods: Different machine learning and deep learning techniques were explored to build diagnostic and prognostic models, ensuring model interpretability and transparency through explainable AI methods. These models were developed and cross-validated using data from 590 Spanish Caucasian patients with primary GC and 633 cancer-free individuals. Up to 261 variables were analysed, including demographic, environmental, clinical, tumoral, and genetic data. Variables such as Helicobacter pylori infection, tobacco use, family history of GC, TNM staging, metastasis, tumour location, treatment received, gender, age, and genetic factors (single nucleotide polymorphisms) were selected as inputs due to their association with the risk and progression of the disease. Results: The XGBoost algorithm (version 1.7.4) achieved the best performance for diagnosis, with an AUC value of 0.68 using 5-fold cross-validation. As for prognosis, the Random Survival Forest algorithm achieved a C-index of 0.77. Of interest, the incorporation of genetic data into the clinical–demographics models significantly increased discriminatory ability in both diagnostic and prognostic models. Conclusions: This article presents GastricAITool, a simple and intuitive decision support tool for the diagnosis and prognosis of GC. Full article
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