Artificial Intelligence in Cancer Research: Learning at Different Levels of Data Granularity

Overview

Journal Mol Oncol

Specialties Molecular Biology
Oncology

Date 2021 Feb 3

PMID 33533192

Citations 11

Authors

Davide Cirillo

Iker Nunez-Carpintero

Alfonso Valencia

Affiliations

Soon will be listed here.

Abstract

From genome-scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co-existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications.

Citing Articles

The role of Hospital-Based Cancer Registries (HBCRs) as information systems in the delivery of evidence-based integrated cancer care: a scoping review.

Tripathee S, MacLennan S, Poobalan A, Omar M, Guntupalli A Health Syst (Basingstoke). 2024; 13(3):177-191.

PMID: 39175499 PMC: 11338207. DOI: 10.1080/20476965.2023.2216749.

Building Prediction Models for 30-Day Readmissions Among ICU Patients Using Both Structured and Unstructured Data in Electronic Health Records.

Moerschbacher A, He Z Proceedings (IEEE Int Conf Bioinformatics Biomed). 2024; 2023:4368-4373.

PMID: 39055130 PMC: 11271049. DOI: 10.1109/bibm58861.2023.10385612.

Liquid biopsy for gastric cancer: Techniques, applications, and future directions.

Diaz Del Arco C, Fernandez Acenero M, Ortega Medina L World J Gastroenterol. 2024; 30(12):1680-1705.

PMID: 38617733 PMC: 11008373. DOI: 10.3748/wjg.v30.i12.1680.

Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review.

Yim D, Khuntia J, Parameswaran V, Meyers A JMIR Med Inform. 2024; 12:e52073.

PMID: 38506918 PMC: 10993141. DOI: 10.2196/52073.

Screening Programs for Breast Cancer: Toward Individualized, Risk-Adapted Strategies of Early Detection.

Trapani D, Sandoval J, Trillo Aliaga P, Ascione L, Berton Giachetti P, Curigliano G Cancer Treat Res. 2024; 188:63-88.

PMID: 38175342 DOI: 10.1007/978-3-031-33602-7_3.

References

Pop B, Fetica B, Blaga M, Trifa A, Achimas-Cadariu P, Vlad C . The role of medical registries, potential applications and limitations. Med Pharm Rep. 2019; 92(1):7-14. PMC: 6448488. DOI: 10.15386/cjmed-1015. View

Topol E . Welcoming new guidelines for AI clinical research. Nat Med. 2020; 26(9):1318-1320. DOI: 10.1038/s41591-020-1042-x. View

Zavala V, Bracci P, Carethers J, Carvajal-Carmona L, Coggins N, Cruz-Correa M . Cancer health disparities in racial/ethnic minorities in the United States. Br J Cancer. 2020; 124(2):315-332. PMC: 7852513. DOI: 10.1038/s41416-020-01038-6. View

Liu S, Shen P, Chen C, Hsu A, Cho Y, Lai Y . DriverDBv3: a multi-omics database for cancer driver gene research. Nucleic Acids Res. 2019; 48(D1):D863-D870. PMC: 7145679. DOI: 10.1093/nar/gkz964. View

van der Ploeg T, Austin P, Steyerberg E . Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints. BMC Med Res Methodol. 2014; 14:137. PMC: 4289553. DOI: 10.1186/1471-2288-14-137. View

Unger J, Cook E, Tai E, Bleyer A . The Role of Clinical Trial Participation in Cancer Research: Barriers, Evidence, and Strategies. Am Soc Clin Oncol Educ Book. 2016; 35:185-98. PMC: 5495113. DOI: 10.1200/EDBK_156686. View

Grassberger C, Ellsworth S, Wilks M, Keane F, Loeffler J . Assessing the interactions between radiotherapy and antitumour immunity. Nat Rev Clin Oncol. 2019; 16(12):729-745. DOI: 10.1038/s41571-019-0238-9. View

Benjamens S, Dhunnoo P, Mesko B . The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med. 2020; 3:118. PMC: 7486909. DOI: 10.1038/s41746-020-00324-0. View

Agrawal R, Prabakaran S . Big data in digital healthcare: lessons learnt and recommendations for general practice. Heredity (Edinb). 2020; 124(4):525-534. PMC: 7080757. DOI: 10.1038/s41437-020-0303-2. View

10.

Castrignano T, Gioiosa S, Flati T, Cestari M, Picardi E, Chiara M . ELIXIR-IT HPC@CINECA: high performance computing resources for the bioinformatics community. BMC Bioinformatics. 2020; 21(Suppl 10):352. PMC: 7446135. DOI: 10.1186/s12859-020-03565-8. View

11.

Vasudevan S, Flashner-Abramson E, Remacle F, Levine R, Kravchenko-Balasha N . Personalized disease signatures through information-theoretic compaction of big cancer data. Proc Natl Acad Sci U S A. 2018; 115(30):7694-7699. PMC: 6065026. DOI: 10.1073/pnas.1804214115. View

12.

Hoof I, Peters B, Sidney J, Pedersen L, Sette A, Lund O . NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics. 2008; 61(1):1-13. PMC: 3319061. DOI: 10.1007/s00251-008-0341-z. View

13.

Zhang Z, Zhou L, Xie N, Nice E, Zhang T, Cui Y . Overcoming cancer therapeutic bottleneck by drug repurposing. Signal Transduct Target Ther. 2020; 5(1):113. PMC: 7331117. DOI: 10.1038/s41392-020-00213-8. View

14.

Chen B, Khodadoust M, Olsson N, Wagar L, Fast E, Liu C . Predicting HLA class II antigen presentation through integrated deep learning. Nat Biotechnol. 2019; 37(11):1332-1343. PMC: 7075463. DOI: 10.1038/s41587-019-0280-2. View

15.

Hanahan D, Weinberg R . Hallmarks of cancer: the next generation. Cell. 2011; 144(5):646-74. DOI: 10.1016/j.cell.2011.02.013. View

16.

Jiang D, Liao J, Duan H, Wu Q, Owen G, Shu C . A machine learning-based prognostic predictor for stage III colon cancer. Sci Rep. 2020; 10(1):10333. PMC: 7316723. DOI: 10.1038/s41598-020-67178-0. View

17.

Yap T, Aerts J, Popat S, Fennell D . Novel insights into mesothelioma biology and implications for therapy. Nat Rev Cancer. 2017; 17(8):475-488. DOI: 10.1038/nrc.2017.42. View

18.

Shi W, Ng C, Lim R, Jiang T, Kumar S, Li X . Reliability of Whole-Exome Sequencing for Assessing Intratumor Genetic Heterogeneity. Cell Rep. 2018; 25(6):1446-1457. PMC: 6261536. DOI: 10.1016/j.celrep.2018.10.046. View

19.

Anda-Jauregui G, Hernandez-Lemus E . Computational Oncology in the Multi-Omics Era: State of the Art. Front Oncol. 2020; 10:423. PMC: 7154096. DOI: 10.3389/fonc.2020.00423. View

20.

Li C, Prokopec S, Sun R, Yousif F, Schmitz N, Boutros P . Sex differences in oncogenic mutational processes. Nat Commun. 2020; 11(1):4330. PMC: 7455744. DOI: 10.1038/s41467-020-17359-2. View