Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking
Background. Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and int...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chiara Frascarelli [verfasserIn] Giuseppina Bonizzi [verfasserIn] Camilla Rosella Musico [verfasserIn] Eltjona Mane [verfasserIn] Cristina Cassi [verfasserIn] Elena Guerini Rocco [verfasserIn] Annarosa Farina [verfasserIn] Aldo Scarpa [verfasserIn] Rita Lawlor [verfasserIn] Luca Reggiani Bonetti [verfasserIn] Stefania Caramaschi [verfasserIn] Albino Eccher [verfasserIn] Stefano Marletta [verfasserIn] Nicola Fusco [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Übergeordnetes Werk: |
In: Journal of Personalized Medicine - MDPI AG, 2012, 13(2023), 1390, p 1390 |
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| Übergeordnetes Werk: |
volume:13 ; year:2023 ; number:1390, p 1390 |
| Links: |
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| DOI / URN: |
10.3390/jpm13091390 |
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DOAJ093371276 |
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| 520 | |a Background. Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and “omics” data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. Methods. In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting “Big Data” and the analytic approaches used for analysis. Results. These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. Conclusions. The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies. | ||
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10.3390/jpm13091390 doi (DE-627)DOAJ093371276 (DE-599)DOAJa78114dce9c646fe8469b540d7f73078 DE-627 ger DE-627 rakwb eng Chiara Frascarelli verfasserin aut Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background. Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and “omics” data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. Methods. In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting “Big Data” and the analytic approaches used for analysis. Results. These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. Conclusions. The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies. biobank digital pathology cancer research artificial intelligence Medicine R Giuseppina Bonizzi verfasserin aut Camilla Rosella Musico verfasserin aut Eltjona Mane verfasserin aut Cristina Cassi verfasserin aut Elena Guerini Rocco verfasserin aut Annarosa Farina verfasserin aut Aldo Scarpa verfasserin aut Rita Lawlor verfasserin aut Luca Reggiani Bonetti verfasserin aut Stefania Caramaschi verfasserin aut Albino Eccher verfasserin aut Stefano Marletta verfasserin aut Nicola Fusco verfasserin aut In Journal of Personalized Medicine MDPI AG, 2012 13(2023), 1390, p 1390 (DE-627)71862713X (DE-600)2662248-8 20754426 nnns volume:13 year:2023 number:1390, p 1390 https://doi.org/10.3390/jpm13091390 kostenfrei https://doaj.org/article/a78114dce9c646fe8469b540d7f73078 kostenfrei https://www.mdpi.com/2075-4426/13/9/1390 kostenfrei https://doaj.org/toc/2075-4426 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1390, p 1390 |
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10.3390/jpm13091390 doi (DE-627)DOAJ093371276 (DE-599)DOAJa78114dce9c646fe8469b540d7f73078 DE-627 ger DE-627 rakwb eng Chiara Frascarelli verfasserin aut Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background. Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and “omics” data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. Methods. In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting “Big Data” and the analytic approaches used for analysis. Results. These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. Conclusions. The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies. biobank digital pathology cancer research artificial intelligence Medicine R Giuseppina Bonizzi verfasserin aut Camilla Rosella Musico verfasserin aut Eltjona Mane verfasserin aut Cristina Cassi verfasserin aut Elena Guerini Rocco verfasserin aut Annarosa Farina verfasserin aut Aldo Scarpa verfasserin aut Rita Lawlor verfasserin aut Luca Reggiani Bonetti verfasserin aut Stefania Caramaschi verfasserin aut Albino Eccher verfasserin aut Stefano Marletta verfasserin aut Nicola Fusco verfasserin aut In Journal of Personalized Medicine MDPI AG, 2012 13(2023), 1390, p 1390 (DE-627)71862713X (DE-600)2662248-8 20754426 nnns volume:13 year:2023 number:1390, p 1390 https://doi.org/10.3390/jpm13091390 kostenfrei https://doaj.org/article/a78114dce9c646fe8469b540d7f73078 kostenfrei https://www.mdpi.com/2075-4426/13/9/1390 kostenfrei https://doaj.org/toc/2075-4426 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1390, p 1390 |
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10.3390/jpm13091390 doi (DE-627)DOAJ093371276 (DE-599)DOAJa78114dce9c646fe8469b540d7f73078 DE-627 ger DE-627 rakwb eng Chiara Frascarelli verfasserin aut Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background. Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and “omics” data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. Methods. In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting “Big Data” and the analytic approaches used for analysis. Results. These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. Conclusions. The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies. biobank digital pathology cancer research artificial intelligence Medicine R Giuseppina Bonizzi verfasserin aut Camilla Rosella Musico verfasserin aut Eltjona Mane verfasserin aut Cristina Cassi verfasserin aut Elena Guerini Rocco verfasserin aut Annarosa Farina verfasserin aut Aldo Scarpa verfasserin aut Rita Lawlor verfasserin aut Luca Reggiani Bonetti verfasserin aut Stefania Caramaschi verfasserin aut Albino Eccher verfasserin aut Stefano Marletta verfasserin aut Nicola Fusco verfasserin aut In Journal of Personalized Medicine MDPI AG, 2012 13(2023), 1390, p 1390 (DE-627)71862713X (DE-600)2662248-8 20754426 nnns volume:13 year:2023 number:1390, p 1390 https://doi.org/10.3390/jpm13091390 kostenfrei https://doaj.org/article/a78114dce9c646fe8469b540d7f73078 kostenfrei https://www.mdpi.com/2075-4426/13/9/1390 kostenfrei https://doaj.org/toc/2075-4426 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1390, p 1390 |
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10.3390/jpm13091390 doi (DE-627)DOAJ093371276 (DE-599)DOAJa78114dce9c646fe8469b540d7f73078 DE-627 ger DE-627 rakwb eng Chiara Frascarelli verfasserin aut Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background. Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and “omics” data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. Methods. In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting “Big Data” and the analytic approaches used for analysis. Results. These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. Conclusions. The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies. biobank digital pathology cancer research artificial intelligence Medicine R Giuseppina Bonizzi verfasserin aut Camilla Rosella Musico verfasserin aut Eltjona Mane verfasserin aut Cristina Cassi verfasserin aut Elena Guerini Rocco verfasserin aut Annarosa Farina verfasserin aut Aldo Scarpa verfasserin aut Rita Lawlor verfasserin aut Luca Reggiani Bonetti verfasserin aut Stefania Caramaschi verfasserin aut Albino Eccher verfasserin aut Stefano Marletta verfasserin aut Nicola Fusco verfasserin aut In Journal of Personalized Medicine MDPI AG, 2012 13(2023), 1390, p 1390 (DE-627)71862713X (DE-600)2662248-8 20754426 nnns volume:13 year:2023 number:1390, p 1390 https://doi.org/10.3390/jpm13091390 kostenfrei https://doaj.org/article/a78114dce9c646fe8469b540d7f73078 kostenfrei https://www.mdpi.com/2075-4426/13/9/1390 kostenfrei https://doaj.org/toc/2075-4426 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1390, p 1390 |
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10.3390/jpm13091390 doi (DE-627)DOAJ093371276 (DE-599)DOAJa78114dce9c646fe8469b540d7f73078 DE-627 ger DE-627 rakwb eng Chiara Frascarelli verfasserin aut Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background. Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and “omics” data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. Methods. In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting “Big Data” and the analytic approaches used for analysis. Results. These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. Conclusions. The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies. biobank digital pathology cancer research artificial intelligence Medicine R Giuseppina Bonizzi verfasserin aut Camilla Rosella Musico verfasserin aut Eltjona Mane verfasserin aut Cristina Cassi verfasserin aut Elena Guerini Rocco verfasserin aut Annarosa Farina verfasserin aut Aldo Scarpa verfasserin aut Rita Lawlor verfasserin aut Luca Reggiani Bonetti verfasserin aut Stefania Caramaschi verfasserin aut Albino Eccher verfasserin aut Stefano Marletta verfasserin aut Nicola Fusco verfasserin aut In Journal of Personalized Medicine MDPI AG, 2012 13(2023), 1390, p 1390 (DE-627)71862713X (DE-600)2662248-8 20754426 nnns volume:13 year:2023 number:1390, p 1390 https://doi.org/10.3390/jpm13091390 kostenfrei https://doaj.org/article/a78114dce9c646fe8469b540d7f73078 kostenfrei https://www.mdpi.com/2075-4426/13/9/1390 kostenfrei https://doaj.org/toc/2075-4426 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1390, p 1390 |
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Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking |
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Background. Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and “omics” data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. Methods. In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting “Big Data” and the analytic approaches used for analysis. Results. These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. Conclusions. The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies. |
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Background. Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and “omics” data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. Methods. In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting “Big Data” and the analytic approaches used for analysis. Results. These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. Conclusions. The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies. |
| abstract_unstemmed |
Background. Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and “omics” data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. Methods. In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting “Big Data” and the analytic approaches used for analysis. Results. These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. Conclusions. The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies. |
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Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking |
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https://doi.org/10.3390/jpm13091390 https://doaj.org/article/a78114dce9c646fe8469b540d7f73078 https://www.mdpi.com/2075-4426/13/9/1390 https://doaj.org/toc/2075-4426 |
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Giuseppina Bonizzi Camilla Rosella Musico Eltjona Mane Cristina Cassi Elena Guerini Rocco Annarosa Farina Aldo Scarpa Rita Lawlor Luca Reggiani Bonetti Stefania Caramaschi Albino Eccher Stefano Marletta Nicola Fusco |
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Giuseppina Bonizzi Camilla Rosella Musico Eltjona Mane Cristina Cassi Elena Guerini Rocco Annarosa Farina Aldo Scarpa Rita Lawlor Luca Reggiani Bonetti Stefania Caramaschi Albino Eccher Stefano Marletta Nicola Fusco |
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