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Gene & Protein in Disease                                                     Identification of new genes



            biomarkers,  and therapeutic  targets.  The  involvement  of   Nat Commun. 2020;11:69.
            AI in cancer research is multifaceted. It integrates clinical      doi: 10.1038/s41467-019-13803-0
            records as well as genomics, proteomics, and imaging
            data. AI algorithms efficiently process and integrate large   2.   Sinkala M. Mutational landscape of cancer-driver genes
            volumes of heterogeneous data. AI techniques are used for   across human cancers. Sci Rep. 2023;13:12742.
            data cleaning, normalization, and preprocessing to address      doi: 10.1038/s41598-023-39608-2
            issues such as missing data and variations in data formats.   3.   Santarius T, Shipley J, Brewer D, Stratton MR, Cooper CS.
            Machine learning and deep learning models can analyze   A  census of amplified and overexpressed human cancer
            data patterns to identify potential biomarkers associated   genes. Nat Rev Cancer. 2010;10:59-64.
            with different cancer  types. These biomarkers could be
            indicative of specific genetic mutations or other factors      doi: 10.1038/nrc2771
            contributing to cancer development. They can predict   4.   Martínez-Jiménez F, Muiños F, Sentís I, et al. A compendium
            tumor  responses  to  therapy  treatments,  aiding  in  the   of  mutational  cancer  driver  genes.  Nat Rev Cancer.
            development of better therapeutic options. AI is used to   2020;20:555-572.
            analyze genomic sequencing data to identify novel cancer-     doi: 10.1038/s41568-020-0290-x
            associated genes. Deep learning models can detect subtle   5.   Nielsen F, van Overeem Hansen T, Sørensen C. Hereditary
            patterns and mutations that might be missed by traditional   breast and ovarian cancer: New genes in confined pathways.
            methods.  AI  algorithms  can  annotate genetic  variants,   Nat Rev Cancer. 2016;16:599-612.
            aiding researchers in understanding the functional
            significance of specific mutations. They assist in identifying      doi: 10.1038/nrc.2016.72
            potential drug targets, accelerating drug discovery by   6.   Sosinsky A, Ambrose J, Cross W, et al. Insights for precision
            pinpointing genes or proteins that play a crucial role in   oncology from the integration of genomic and clinical
            cancer development. These tools can analyze complex   data of 13,880 tumors from the 100,000 Genomes Cancer
            biological pathways, revealing interconnected networks   Programme. Nat Med. 2024;30:279-289.
            of genes and proteins involved in cancer. This holistic      doi: 10.1038/s41591-023-02682-0
            view aids in understanding underlying mechanisms
            and potential therapeutic therapy treatment. AI can also   7.   Koh G, Degasperi A, Zou X, Momen S, Nik-Zainal S.
            suggest  existing  drugs  for  repurposing  based  on  their   Mutational signatures: Emerging concepts, caveats and
            interaction with the identified genes or pathways.    clinical applications. Nat Rev Cancer. 2021;21:619-637.
                                                                  doi: 10.1038/s41568-021-00377-7
              Despite  their  relevance,  AI-based  findings  should
            be experimentally validated before clinical translation.   8.   Saito Y, Koya J, Araki M, et al. Landscape and function of
            Collaboration among researchers, access to comprehensive   multiple mutations within individual oncogenes.  Nature.
            databases, advanced technologies, and integration of   2020;582:95-99.
            multiple approaches can lead to the identification of new      doi: 10.1038/s41586-020-2175-2
            genes involved in cancer. These findings pave the way for   9.   Terekhanova NV, Karpova A, Liang WW, et al. Epigenetic
            a better understanding of the disease and the development   regulation during cancer transitions across 11 tumour types.
            of targeted therapies.
                                                                  Nature. 2023;623:432-441.
            Conflict of interest                                  doi: 10.1038/s41586-023-06682-5

            Amancio Carnero is an Associate Editor of this journal.  10.  Eifert C, Powers RS. From cancer genomes to oncogenic
                                                                  drivers, tumour dependencies and therapeutic targets. Nat
            Further disclosure                                    Rev Cancer. 2012;12:572-578.

            There are tens of thousands of works describing techniques      doi: 10.1038/nrc3299
            to identify new genes involved in cancer. It would have   11.  Katti A, Diaz BJ, Caragine CM, Sanjana NE, Dow LE.
            been impossible to cite all of them, even citations of only   CRISPR in cancer biology and therapy.  Nat Rev Cancer.
            some landmark works using every technique. Therefore,   2022;22:259-279.
            we only suggest further reading on the same topic and the      doi: 10.1038/s41568-022-00441-w
            references listed in this editorial for a better understanding.
                                                               12.  Paczkowska M, Barenboim J, Sintupisut N, et al. Integrative
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            1.   Colaprico A, Olsen C, Bailey MH,  et al. Interpreting
               pathways to discover cancer driver genes with Moonlight.      doi: 10.1038/s41467-019-13983-9

            Volume 4 Issue 1 (2025)                         2                               doi: 10.36922/gpd.2892
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