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Tumor Discovery

EDITORIAL
The transformative role of AI in cancer research

Amancio Carnero *
1,2
1 Instituto de Biomedicina de Sevilla (IBIS), Hospital Universitario Virgen del Rocío (HUVR), Consejo
Superior de Investigaciones Científicas, Universidad de Sevilla, Seville, Spain
2 CIBERONC, Instituto de Salud Carlos III, Madrid, Spain

1. AI in the management of complex cancer data
Cancer research is inherently data-intensive. It integrates information from diverse
domains such as genomics, proteomics, clinical records, and imaging, each providing
unique insights into disease origins and progression. The volume and complexity of
these data sets often exceed the capabilities of traditional analytical approaches. This
is where artificial intelligence (AI) becomes a powerful ally. AI excels at managing
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heterogeneous data sets. It processes raw data by performing essential tasks such as
cleaning, normalization, and preprocessing. These steps are crucial, as cancer data sets
often present issues such as missing data points and variations in format across studies
and institutions. By automating these processes, AI reduces the risk of human error and
speeds up the preparation of data for further analysis. Once preprocessed, the data must
be analyzed to find patterns that can reveal biomarkers – indicators of disease presence
or progression. Machine learning algorithms, especially supervised and unsupervised
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learning models, are adept at this task. They can examine large amount of genomic and
proteomic data to identify subtle patterns that correlate with specific types of cancer. For
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example, machine learning models can link genetic mutations to certain cancer types,
uncovering biomarkers that might not be apparent through traditional techniques. These
biomarkers are invaluable in a variety of contexts, including early detection, prognosis
prediction, and therapeutic targeting. Furthermore, AI-based predictive models can
*Corresponding author:
Amancio Carnero estimate a patient’s response to specific treatments, allowing for personalized cancer
(acarnero-ibis@us.es) therapy – a pillar of modern oncology. 4
Citation: Carnero A. The One of the most transformative applications of AI in cancer research lies in the
transformative role of AI in analysis of genomic sequencing. Genomic data holds clues about the mutations that
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cancer research. Tumor Discov.
2025;4(2):1-3. drive cancer. Decoding this information is essential to understanding the disease and
doi: 10.36922/TD025040006 designing targeted interventions. Deep learning models have proven particularly effective
Received: January 21, 2025 in this arena. Deep learning excels at identifying subtle genetic mutations that traditional
statistical methods might miss. These algorithms analyze sequence data to identify
Published online: March 10, 2025 new genes associated with cancer, annotate genetic variants, and infer their functional
Copyright: © 2025 Author(s). significance. By unraveling these genetic mysteries, researchers can identify potential
This is an Open-Access article drug targets, laying the groundwork for innovative therapies. In addition, AI helps in
distributed under the terms of the
Creative Commons Attribution building complex models of biological pathways. These models reveal intricate networks
License, permitting distribution, of genes and proteins, offering insights into the underlying mechanisms of cancer. Such
and reproduction in any medium, pathways help researchers identify points of therapeutic intervention, paving the way for
provided the original work is
properly cited. designing drugs that disrupt these networks and halt disease progression.
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Publisher’s Note: AccScience Medical imaging is another domain where AI is making significant progress.
Publishing remains neutral with Cancer diagnosis often relies on imaging technologies such as magnetic resonance
regard to jurisdictional claims in
published maps and institutional imaging, computed tomography scans, and histopathological slides. Interpreting these
affiliations. images requires precision, as subtle features can indicate the presence or progression

Volume 4 Issue 2 (2025) 1 doi: 10.36922/TD025040006