Computational Approaches: A New Frontier in Cancer Research


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Abstract

Cancer is a broad category of disease that can start in virtually any organ or tissue of the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to the most recent statistics, cancer will be the cause of 10 million deaths worldwide in 2020, accounting for one death out of every six worldwide. The typical approach used in anti-cancer research is highly time-consuming and expensive, and the outcomes are not particularly encouraging. Computational techniques have been employed in anti-cancer research to advance our understanding. Recent years have seen a significant and exceptional impact on anticancer research due to the rapid development of computational tools for novel drug discovery, drug design, genetic studies, genome characterization, cancer imaging and detection, radiotherapy, cancer metabolomics, and novel therapeutic approaches. In this paper, we examined the various subfields of contemporary computational techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, and QSAR, and their applications in the study of cancer.

About the authors

Shubham Srivastava

Department of Pharmacy, IIMT College of Pharmacy

Author for correspondence.
Email: info@benthamscience.net

Pushpendra Jain

Department of Pharmacy, IIMT College of Pharmacy

Email: info@benthamscience.net

References

  1. Mak, L.; Liggi, S.; Tan, L.; Kusonmano, K.; Rollinger, J.M.; Koutsoukas, A.; Glen, R.C.; Kirchmair, J. Anti-cancer drug development: computational strategies to identify and target proteins involved in cancer metabolism. Curr. Pharm. Des., 2013, 19(4), 532-577. doi: 10.2174/138161213804581855 PMID: 23016852
  2. Cui, W.; Aouidate, A.; Wang, S.; Yu, Q.; Li, Y.; Yuan, S. Discovering anti-cancer drugs via computational methods. Front. Pharmacol., 2020, 11, 733. doi: 10.3389/fphar.2020.00733 PMID: 32508653
  3. Massard, C.; Michiels, S.; Ferté, C.; Le Deley, M.C.; Lacroix, L.; Hollebecque, A.; Verlingue, L.; Ileana, E.; Rosellini, S.; Ammari, S.; Ngo-Camus, M.; Bahleda, R.; Gazzah, A.; Varga, A.; Postel-Vinay, S.; Loriot, Y.; Even, C.; Breuskin, I.; Auger, N.; Job, B.; De Baere, T.; Deschamps, F.; Vielh, P.; Scoazec, J.Y.; Lazar, V.; Richon, C.; Ribrag, V.; Deutsch, E.; Angevin, E.; Vassal, G.; Eggermont, A.; André, F.; Soria, J.C. High-throughput genomics and clinical outcome in hard-to-treat advanced cancers: Results of the MOSCATO 01 trial. Cancer Discov., 2017, 7(6), 586-595. doi: 10.1158/2159-8290.CD-16-1396 PMID: 28365644
  4. Meric-Bernstam, F.; Mills, G.B. Overcoming implementation challenges of personalized cancer therapy. Nat. Rev. Clin. Oncol., 2012, 9(9), 542-548. doi: 10.1038/nrclinonc.2012.127 PMID: 22850751
  5. Flaherty, K.T.; Hodi, F.S.; Fisher, D.E. From genes to drugs: targeted strategies for melanoma. Nat. Rev. Cancer, 2012, 12(5), 349-361. doi: 10.1038/nrc3218 PMID: 22475929
  6. Higgins, M.J.; Baselga, J. Targeted therapies for breast cancer. J. Clin. Invest., 2011, 121(10), 3797-3803. doi: 10.1172/JCI57152 PMID: 21965336
  7. Hanna, T.P.; Kangolle, A.C.T. Cancer control in developing countries: using health data and health services research to measure and improve access, quality and efficiency. BMC Int. Health Hum. Rights, 2010, 10(1), 24. doi: 10.1186/1472-698X-10-24 PMID: 20942937
  8. WHO-Cancer. Available from: https://www.who.int/news-room/fact-sheets/detail/cancer
  9. Cancer statistics. Available from: https://www.cancer.gov/about-cancer/understanding/statistics
  10. Kapetanovic, I.M. Computer-aided drug discovery and development (CADDD): In silico-chemico-biological approach. Chem. Biol. Interact., 2008, 171(2), 165-176. doi: 10.1016/j.cbi.2006.12.006 PMID: 17229415
  11. Prada-Gracia, D.; Huerta-Yépez, S.; Moreno-Vargas, L.M. Application of computational methods for anticancer drug discovery, design, and optimization. Boletín Médico Del Hosp Infant México, 2016, 73(6), 411-423. doi: 10.1016/j.bmhime.2017.11.040
  12. Sudhakar, A. History of cancer, ancient and modern treatment methods. J. Cancer Sci. Ther., 2009, 1(2), 1-4. PMID: 20740081
  13. Overington, J.P.; Al-Lazikani, B.; Hopkins, A.L. How many drug targets are there? Nat. Rev. Drug Discov., 2006, 5(12), 993-996. doi: 10.1038/nrd2199 PMID: 17139284
  14. Reymond, J.L.; Blum, L.C.; Van Deursen, R. Exploring the chemical space of known and unknown organic small molecules at www.gdb.unibe.ch. Chimia (Aarau), 2011, 65(11), 863-7.
  15. Brogi, S.; Kladi, M.; Vagias, C.; Papazafiri, P.; Roussis, V.; Tafi, A. Pharmacophore modeling for qualitative prediction of antiestrogenic activity. J. Chem. Inf. Model., 2009, 49(11), 2489-2497. doi: 10.1021/ci900254b PMID: 19877675
  16. Brogi, S.; Papazafiri, P.; Roussis, V.; Tafi, A. 3D-QSAR using pharmacophore-based alignment and virtual screening for discovery of novel MCF-7 cell line inhibitors. Eur. J. Med. Chem., 2013, 67, 344-351. doi: 10.1016/j.ejmech.2013.06.048 PMID: 23880359
  17. Zaccagnini, L.; Brogi, S.; Brindisi, M.; Gemma, S.; Chemi, G.; Legname, G. Identification of novel fluorescent probes preventing PrPScreplication in prion diseases. Eur. J. Med. Chem., 2017, 127, 859-887. doi: 10.1016/j.ejmech.2016.10.064
  18. Vallone, A.; D’Alessandro, S.; Brogi, S.; Brindisi, M.; Chemi, G.; Alfano, G.; Lamponi, S.; Lee, S.G.; Jez, J.M.; Koolen, K.J.M.; Dechering, K.J.; Saponara, S.; Fusi, F.; Gorelli, B.; Taramelli, D.; Parapini, S.; Caldelari, R.; Campiani, G.; Gemma, S.; Butini, S. Antimalarial agents against both sexual and asexual parasites stages: structure-activity relationships and biological studies of the Malaria Box compound 1-5-(4-bromo-2-chlorophenyl)furan-2-yl-N-(piperidin-4-yl)methylmethanamine (MMV019918) and analogues. Eur. J. Med. Chem., 2018, 150, 698-718.
  19. Brogi, S. Computational approaches for drug discovery. Molecules, 2019, 24(17), 3061. doi: 10.3390/molecules24173061 PMID: 31443558
  20. Sebastian, A.M.; Peter, D. Artificial intelligence in cancer research: Trends, challenges and future directions. Life (Basel), 2022, 12(12), 1991. doi: 10.3390/life12121991 PMID: 36556356
  21. Bhinder, B.; Gilvary, C.; Madhukar, N.S.; Elemento, O. Artificial intelligence in cancer research and precision medicine. Cancer Discov., 2021, 11(4), 900-915. doi: 10.1158/2159-8290.CD-21-0090 PMID: 33811123
  22. Xie, J.; Luo, X.; Deng, X.; Tang, Y.; Tian, W.; Cheng, H.; Zhang, J.; Zou, Y.; Guo, Z.; Xie, X. Advances in artificial intelligence to predict cancer immunotherapy efficacy. Front. Immunol., 2023, 13, 1076883. doi: 10.3389/fimmu.2022.1076883 PMID: 36685496
  23. Hephzibah Cathryn, R.; Udhaya Kumar, S.; Younes, S.; Zayed, H.; George Priya Doss, C. Chapter Three - A review of bioinformatics tools and web servers in different microarray platforms used in cancer research. In: Advances in Protein Chemistry and Structural Biology; , 2022; 131, pp. 85-164. doi: 10.1016/bs.apcsb.2022.05.002
  24. Brenner, C. Applications of bioinformatics in cancer. Cancers (Basel), 2019, 11(11), 1630. doi: 10.3390/cancers11111630 PMID: 31652939
  25. Beg, A.; Parveen, R. Role of Bioinformatics in cancer research and drug development. Translational Bioinformatics in Healthcare and Medicine; Elsevier, 2021, pp. 141-148. doi: 10.1016/B978-0-323-89824-9.00011-2
  26. de Oliveira, T.A.; da Silva, M.P.; Maia, E.H.B.; da Silva, A.M.; Taranto, A.G. Virtual Screening algorithms in drug discovery: A review focused on machine and deep learning methods. Drugs Drug Candidates, 2023, 2(2), 311-334. doi: 10.3390/ddc2020017
  27. Gimeno, A.; Ojeda-Montes, M.; Tomás-Hernández, S.; Cereto-Massagué, A.; Beltrán-Debón, R.; Mulero, M.; Pujadas, G.; Garcia-Vallvé, S. The light and dark sides of virtual screening: What is there to know? Int. J. Mol. Sci., 2019, 20(6), 1375. doi: 10.3390/ijms20061375 PMID: 30893780
  28. Maicheen, C.; Ungwitayatorn, J. Molecular docking study of chromone derivatives as dual inhibitor against plasmepsin ii and falcipain-2. Warasan Khana Witthayasat Maha Witthayalai Chiang Mai, 2020, 47(1), 98-113.
  29. Agarwal, S.; Mehrotra, R. An overview of molecular simulation. JSM Chem., 2016, 4(2), 1024-1028.
  30. Stephen, N. Cancer Drug Design and Discovery No Title, 2nd ed; Elsevier, 2014. doi: 10.1016/C2011-0-07765-7
  31. Sliwoski, G.R.; Meiler, J.; Lowe, E.W. Computational methods in drug discovery prediction of protein structure and ensembles from limited experimental data view project antibody modeling, antibody design and antigen-antibody interactions view project. Comput. Methods Drug Discov., 2014, 66(1), 334-395.
  32. National Cancer Institute Informatics Technology for Cancer Research. Available from: https://itcr.cancer.gov/informatics-tools-table
  33. PBD-101. Available from: https://pdb101.rcsb.org/browse/cancer
  34. Basith, S.; Cui, M.; Macalino, S.J.Y.; Choi, S. Expediting the design, discovery and development of anticancer drugs using computational approaches. Curr. Med. Chem., 2017, 24(42), 4753-4778. PMID: 27593958
  35. Monticolo, F.; Chiusano, M.L. Computational approaches for cancer-fighting: from gene expression to functional foods. Cancers (Basel), 2021, 13(16), 4207. doi: 10.3390/cancers13164207 PMID: 34439361
  36. Berardi, R.; Morgese, F.; Rinaldi, S.; Torniai, M.; Mentrasti, G.; Scortichini, L.; Giampieri, R. Benefits and limitations of a multidisciplinary approach in cancer patient management. Cancer Manag. Res., 2020, 12, 9363-9374. doi: 10.2147/CMAR.S220976 PMID: 33061625
  37. Jain, S.; Naicker, D.; Raj, R.; Patel, V.; Hu, Y.C.; Srinivasan, K.; Jen, C.P. Computational intelligence in cancer diagnostics: A contemporary review of smart phone apps, current problems, and future research potentials. Diagnostics (Basel), 2023, 13(9), 1563. doi: 10.3390/diagnostics13091563 PMID: 37174954
  38. Rajkomar, A.; Dean, J.; Kohane, I. Machine learning in medicine. N. Engl. J. Med., 2019, 380(14), 1347-1358. doi: 10.1056/NEJMra1814259 PMID: 30943338
  39. Sherbet, G.; Woo, W.L.; Dlay, S. Application of artificial intelligence-based technology in cancer management: A commentary on the deployment of artificial neural networks. Anticancer Res., 2018, 38(12), 6607-6613. doi: 10.21873/anticanres.13027 PMID: 30504368
  40. Nagarajan, N.; Yapp, E.K.Y.; Le, N.Q.K.; Kamaraj, B.; Al-Subaie, A.M.; Yeh, H.Y. Application of computational biology and artificial intelligence technologies in cancer precision drug discovery. BioMed Res. Int., 2019, 2019, 8427042. doi: 10.1155/2019/8427042
  41. Wang, D.; Khosla, A.; Gargeya, R.; Irshad, H.; Beck, A.H. Deep Learning for Identifying Metastatic Breast Cancer. arXiv, 2016, 1-6.
  42. Esteva, A.; Kuprel, B.; Novoa, R.A.; Ko, J.; Swetter, S.M.; Blau, H.M.; Thrun, S. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639), 115-118. doi: 10.1038/nature21056 PMID: 28117445
  43. Luo, G.; Sun, G.; Wang, K.; Dong, S.; Zhang, H. A novel left ventricular volumes prediction method based on deep learning network in cardiac MRI. Comput. Cardiol., 2010, 2016(43), 89-92.
  44. Chan, H.C.S.; Shan, H.; Dahoun, T.; Vogel, H.; Yuan, S. Advancing drug discovery via artificial intelligence. Trends Pharmacol. Sci., 2019, 40(8), 592-604. doi: 10.1016/j.tips.2019.06.004 PMID: 31320117
  45. Nitta, N.; Sugimura, T.; Isozaki, A.; Mikami, H.; Hiraki, K.; Sakuma, S.; Iino, T.; Arai, F.; Endo, T.; Fujiwaki, Y.; Fukuzawa, H.; Hase, M.; Hayakawa, T.; Hiramatsu, K.; Hoshino, Y.; Inaba, M.; Ito, T.; Karakawa, H.; Kasai, Y.; Koizumi, K.; Lee, S.; Lei, C.; Li, M.; Maeno, T.; Matsusaka, S.; Murakami, D.; Nakagawa, A.; Oguchi, Y.; Oikawa, M.; Ota, T.; Shiba, K.; Shintaku, H.; Shirasaki, Y.; Suga, K.; Suzuki, Y.; Suzuki, N.; Tanaka, Y.; Tezuka, H.; Toyokawa, C.; Yalikun, Y.; Yamada, M.; Yamagishi, M.; Yamano, T.; Yasumoto, A.; Yatomi, Y.; Yazawa, M.; Di Carlo, D.; Hosokawa, Y.; Uemura, S.; Ozeki, Y.; Goda, K. Intelligent image-activated cell sorting. Cell, 2018, 175(1), 266-276.e13. doi: 10.1016/j.cell.2018.08.028 PMID: 30166209
  46. Tripathy, R.K.; Mahanta, S.; Paul, S. Artificial intelligence-based classification of breast cancer using cellular images. RSC Advances, 2014, 4(18), 9349-9355. doi: 10.1039/c3ra47489e
  47. von Lilienfeld, O.A. Quantum machine learning in chemical compound space. Angew. Chem. Int. Ed., 2018, 57(16), 4164-4169. doi: 10.1002/anie.201709686 PMID: 29216413
  48. Zhou, Z.; Li, X.; Zare, R.N. Optimizing chemical reactions with deep reinforcement learning. ACS Cent. Sci., 2017, 3(12), 1337-1344. doi: 10.1021/acscentsci.7b00492 PMID: 29296675
  49. Coley, C.W.; Barzilay, R.; Jaakkola, T.S.; Green, W.H.; Jensen, K.F. Prediction of organic reaction outcomes using machine learning. ACS Cent. Sci., 2017, 3(5), 434-443. doi: 10.1021/acscentsci.7b00064 PMID: 28573205
  50. Popova, M.; Isayev, O.; Tropsha, A. Deep reinforcement learning for de novo drug design. Sci. Adv., 2018, 4(7), eaap7885. doi: 10.1126/sciadv.aap7885 PMID: 30050984
  51. Hofmarcher, M.; Rumetshofer, E.; Clevert, D.A.; Hochreiter, S.; Klambauer, G. Accurate prediction of biological assays with high-throughput microscopy images and convolutional networks. J. Chem. Inf. Model., 2019, 59(3), 1163-1171. doi: 10.1021/acs.jcim.8b00670 PMID: 30840449
  52. Klambauer, G.; Hochreiter, S.; Rarey, M. Machine learning in drug discovery. J. Chem. Inf. Model., 2019, 59(3), 945-946. doi: 10.1021/acs.jcim.9b00136 PMID: 30905159
  53. Yin, Z.; Ai, H.; Zhang, L.; Ren, G.; Wang, Y.; Zhao, Q.; Liu, H. Predicting the cytotoxicity of chemicals using ensemble learning methods and molecular fingerprints. J. Appl. Toxicol., 2019, 39(10), 1366-1377. doi: 10.1002/jat.3785 PMID: 30763981
  54. Machado, J.F.; Silva, R.D.; Melo, R.; Correia, J.D.G. Less exploited GPCRs in precision medicine: Targets for molecular imaging and theranostics. Molecules, 2019, 24(1), 1-29. PMID: 31861256
  55. Born, J.; Manica, M.; Oskooei, A.; Cadow, J.; Rodríguez Martínez, M. PaccmannRL: Designing anticancer drugs from transcriptomic data via reinforcement learning. In: Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics); , 2020; 12074 LNBI, p. 231-3.
  56. Lind, A.P.; Anderson, P.C. Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties. PLoS One, 2019, 14(7), e0219774. doi: 10.1371/journal.pone.0219774 PMID: 31295321
  57. Hossain, M.A.; Saiful Islam, S.M.; Quinn, J.M.W.; Huq, F.; Moni, M.A. Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality. J. Biomed. Inform., 2019, 100, 103313. doi: 10.1016/j.jbi.2019.103313 PMID: 31655274
  58. Paik, E.S.; Lee, J.W.; Park, J.Y.; Kim, J.H.; Kim, M.; Kim, T.J.; Choi, C.H.; Kim, B.G.; Bae, D.S.; Seo, S.W. Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods. J. Gynecol. Oncol., 2019, 30(4), e65. doi: 10.3802/jgo.2019.30.e65 PMID: 31074247
  59. McDonald, J.F. Back to the future - The integration of big data with machine learning is re-establishing the importance of predictive correlations in ovarian cancer diagnostics and therapeutics. Gynecol. Oncol., 2018, 149(2), 230-231. doi: 10.1016/j.ygyno.2018.03.053 PMID: 29572028
  60. Li, Q.; Qi, L.; Feng, Q.X.; Liu, C.; Sun, S.W.; Zhang, J.; Yang, G.; Ge, Y.Q.; Zhang, Y.D.; Liu, X.S. Machine learning–based computational models derived from large-scale radiographic-radiomic images can help predict adverse histopathological status of gastric cancer. Clin. Transl. Gastroenterol., 2019, 10(10), e00079. doi: 10.14309/ctg.0000000000000079 PMID: 31577560
  61. Taninaga, J.; Nishiyama, Y.; Fujibayashi, K.; Gunji, T.; Sasabe, N.; Iijima, K.; Naito, T. Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study. Sci. Rep., 2019, 9(1), 12384. doi: 10.1038/s41598-019-48769-y PMID: 31455831
  62. Liu, C.; Qi, L.; Feng, Q.X.; Sun, S.W.; Zhang, Y.D.; Liu, X.S. Performance of a machine learning-based decision model to help clinicians decide the extent of lymphadenectomy (D1 vs. D2) in gastric cancer before surgical resection. Abdom. Radiol. (N.Y.), 2019, 44(9), 3019-3029. doi: 10.1007/s00261-019-02098-w PMID: 31201432
  63. Arnaldo, S.; Cuocolo, R.; Renata, D.G.; Anna, N.; Valeria, R.; Antonio, T. Deep myometrial infiltration of endometrial cancer on MRI: A radiomics-powered machine learning pilot study. Acad. Radiol., 2020, 28(5), 737-744. doi: 10.1016/j.acra.2020.02.028
  64. Günakan, E.; Atan, S.; Haberal, A.N. Küçükyıldız, İ.A.; Gökçe, E.; Ayhan, A. A novel prediction method for lymph node involvement in endometrial cancer: machine learning. Int. J. Gynecol. Cancer, 2019, 29(2), 320-324. doi: 10.1136/ijgc-2018-000033 PMID: 30718313
  65. Ciallella, H.L.; Zhu, H. Advancing computational toxicology in the big data era by artificial intelligence: Data-driven and mechanism-driven modeling for chemical toxicity. Chem. Res. Toxicol., 2019, 32(4), 536-547. doi: 10.1021/acs.chemrestox.8b00393 PMID: 30907586
  66. Zhu, H. Big data and artificial intelligence modeling for drug discovery. Annu. Rev. Pharmacol. Toxicol., 2020, 60(1), 573-589. doi: 10.1146/annurev-pharmtox-010919-023324 PMID: 31518513
  67. Réda, C.; Kaufmann, E.; Delahaye-Duriez, A. Machine learning applications in drug development. Comput. Struct. Biotechnol. J., 2019, 18, 241-252. PMID: 33489002
  68. Brown, N.; Hirst, J. In Silico Medicinal Chemistry. Computational Methods to Support Drug Design. In Silico Med. Chem., 2015, 232. Available from: https://www.google.co.in/books/edition/In_Silico_Medicinal_Chemistry/-mooDwAAQBAJ?hl=en&gbpv=0
  69. Pereira, J.C.; Caffarena, E.R.; dos Santos, C.N. Boosting docking-based virtual screening with deep learning. J. Chem. Inf. Model., 2016, 56(12), 2495-2506. doi: 10.1021/acs.jcim.6b00355 PMID: 28024405
  70. Paul, D.; Sanap, G.; Shenoy, S.; Kalyane, D.; Kalia, K.; Tekade, R.K. Artificial intelligence in drug discovery and development. Drug Discov. Today, 2021, 26(1), 80-93. doi: 10.1016/j.drudis.2020.10.010 PMID: 33099022
  71. Awale, M.; Reymond, J.L. Polypharmacology browser PPB2: Target prediction combining nearest neighbors with machine learning. J. Chem. Inf. Model., 2019, 59(1), 10-17. doi: 10.1021/acs.jcim.8b00524 PMID: 30558418
  72. Durrant, J.D.; McCammon, J.A. NNScore 2.0: A neural-network receptor-ligand scoring function. J. Chem. Inf. Model., 2011, 51(11), 2897-2903. doi: 10.1021/ci2003889 PMID: 22017367
  73. Zhang, W.; Lee, A.M.; Jena, S.; Huang, Y.; Ho, Y.; Tietz, K.T.; Miller, C.R.; Su, M.C.; Mentzer, J.; Ling, A.L.; Li, Y.; Dehm, S.M.; Huang, R.S. Computational drug discovery for castration-resistant prostate cancers through in vitro drug response modeling. Proc. Natl. Acad. Sci. USA, 2023, 120(17), e2218522120. doi: 10.1073/pnas.2218522120 PMID: 37068243
  74. Alqahtani, A. Application of artificial intelligence in discovery and development of anticancer and antidiabetic therapeutic agents. Evid. Based Compl. Alternat. Med., 2022, 2022, 6201067. doi: 10.1155/2022/6201067
  75. Cassidy, W.J.; Taylor, B. Artificial Intelligence in Oncology Drug Discovery and Development; IntechOpen, 2020. Available from: https://www.intechopen.com/books/artificial-intelligence-in-oncology-drug-discovery-and-development
  76. Gayvert, K.M.; Madhukar, N.S.; Elemento, O. A data-driven approach to predicting successes and failures of clinical trials. Cell Chem. Biol., 2016, 23(10), 1294-1301. doi: 10.1016/j.chembiol.2016.07.023 PMID: 27642066
  77. Zhavoronkov, A.; Ivanenkov, Y.A.; Aliper, A.; Veselov, M.S.; Aladinskiy, V.A.; Aladinskaya, A.V.; Terentiev, V.A.; Polykovskiy, D.A.; Kuznetsov, M.D.; Asadulaev, A.; Volkov, Y.; Zholus, A.; Shayakhmetov, R.R.; Zhebrak, A.; Minaeva, L.I.; Zagribelnyy, B.A.; Lee, L.H.; Soll, R.; Madge, D.; Xing, L.; Guo, T.; Aspuru-Guzik, A. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol., 2019, 37(9), 1038-1040. doi: 10.1038/s41587-019-0224-x PMID: 31477924
  78. Raies, A.; Tulodziecka, E.; Stainer, J.; Middleton, L.; Dhindsa, R.S.; Hill, P.; Engkvist, O.; Harper, A.R.; Petrovski, S.; Vitsios, D. Author Correction: DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets. Commun. Biol., 2023, 6(1), 710. doi: 10.1038/s42003-023-05086-5 PMID: 37433831
  79. Liu, L.; Chen, X.; Hu, C.; Zhang, D.; Shao, Z.; Jin, Q.; Yang, J.; Xie, H.; Liu, B.; Hu, M.; Ke, K. Synthetic lethality-based identification of targets for anticancer drugs in the human signaling network. Sci. Rep., 2018, 8(1), 8440. doi: 10.1038/s41598-018-26783-w PMID: 29855504
  80. Wang, L.; Song, Y.; Wang, H.; Zhang, X.; Wang, M.; He, J.; Li, S.; Zhang, L.; Li, K.; Cao, L. Advances of artificial intelligence in anti-cancer drug design: A review of the past decade. Pharmaceuticals (Basel), 2023, 16(2), 253. doi: 10.3390/ph16020253 PMID: 37259400
  81. Ren, F.; Ding, X.; Zheng, M.; Korzinkin, M.; Cai, X.; Zhu, W.; Mantsyzov, A.; Aliper, A.; Aladinskiy, V.; Cao, Z.; Kong, S.; Long, X.; Man Liu, B.H.; Liu, Y.; Naumov, V.; Shneyderman, A.; Ozerov, I.V.; Wang, J.; Pun, F.W.; Polykovskiy, D.A.; Sun, C.; Levitt, M.; Aspuru-Guzik, A.; Zhavoronkov, A. AlphaFold accelerates artificial intelligence powered drug discovery: Efficient discovery of a novel CDK20 small molecule inhibitor. Chem. Sci. (Camb.), 2023, 14(6), 1443-1452. doi: 10.1039/D2SC05709C PMID: 36794205
  82. Li, T.; Shetty, S.; Kamath, A.; Jaiswal, A.; Jiang, X.; Ding, Y. CancerGPT: Few-shot drug pair synergy prediction using large pre-trained language models. ArXiv, 2023, arXiv:2304.10946v1. PMID: 37131872
  83. Celebi, R.; Bear Don’t Walk, O., IV; Movva, R.; Alpsoy, S.; Dumontier, M. In-silico prediction of synergistic anti-cancer drug combinations using multi-omics data. Sci. Rep., 2019, 9(1), 8949. doi: 10.1038/s41598-019-45236-6 PMID: 31222109
  84. Ahuja, A.S. The impact of artificial intelligence in medicine on the future role of the physician. PeerJ, 2019, 7, e7702. doi: 10.7717/peerj.7702 PMID: 31592346
  85. Nicolle, R.; Raffenne, J.; Paradis, V.; Couvelard, A.; de Reynies, A.; Blum, Y.; Cros, J. Prognostic biomarkers in pancreatic cancer: Avoiding errata when using the TCGA dataset. Cancers (Basel), 2019, 11(1), 126. doi: 10.3390/cancers11010126 PMID: 30669703
  86. Laakkonen, P.; Vuorinen, K. Homing peptides as targeted delivery vehicles. Integr. Biol. (Camb), 2010, 2(7-8), 326-337.
  87. Sharma, A.; Kapoor, P.; Gautam, A.; Chaudhary, K.; Kumar, R.; Chauhan, J.S. Computational approach for designing tumor homing peptides. Sci. Rep., 2013, 3, 1607. doi: 10.1038/srep01607
  88. Shoombuatong, W.; Schaduangrat, N.; Pratiwi, R.; Nantasenamat, C. THPep: A machine learning-based approach for predicting tumor homing peptides. Comput. Biol. Chem., 2019, 80(April), 441-451. doi: 10.1016/j.compbiolchem.2019.05.008 PMID: 31151025
  89. Kapoor, P.; Singh, H.; Gautam, A.; Chaudhary, K.; Kumar, R.; Raghava, G.P.S. TumorHoPe: a database of tumor homing peptides. PLoS One, 2012, 7(4), e35187. doi: 10.1371/journal.pone.0035187 PMID: 22523575
  90. Thundimadathil, J. Cancer treatment using peptides: Current therapies and future prospects. J. Amino Acids, 2012, 2012, 1-13. doi: 10.1155/2012/967347 PMID: 23316341
  91. Bayat, A. Science, medicine, and the future: Bioinformatics. BMJ, 2002, 324(7344), 1018-1022. doi: 10.1136/bmj.324.7344.1018 PMID: 11976246
  92. Mitra, A.K.; Mukherjee, U.K.; Harding, T.; Jang, J.S.; Stessman, H.; Li, Y.; Abyzov, A.; Jen, J.; Kumar, S.; Rajkumar, V.; Van Ness, B. Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors. Leukemia, 2016, 30(5), 1094-1102. doi: 10.1038/leu.2015.361 PMID: 26710886
  93. Scherf, U.; Ross, D.T.; Waltham, M.; Smith, L.H.; Lee, J.K.; Tanabe, L.; Kohn, K.W.; Reinhold, W.C.; Myers, T.G.; Andrews, D.T.; Scudiero, D.A.; Eisen, M.B.; Sausville, E.A.; Pommier, Y.; Botstein, D.; Brown, P.O.; Weinstein, J.N. A gene expression database for the molecular pharmacology of cancer. Nat. Genet., 2000, 24(3), 236-244. doi: 10.1038/73439 PMID: 10700175
  94. Li, K.; Du, Y.; Li, L.; Wei, D. Bioinformatics approaches for anti-cancer drug discovery. Curr. Drug Targets, 2020, 21(1), 3-17. doi: 10.2174/1389450120666190923162203
  95. Marstrand, T.T.; Borup, R.; Willer, A.; Borregaard, N.; Sandelin, A.; Porse, B.T.; Theilgaard-Mönch, K. A conceptual framework for the identification of candidate drugs and drug targets in acute promyelocytic leukemia. Leukemia, 2010, 24(7), 1265-1275. doi: 10.1038/leu.2010.95 PMID: 20508621
  96. Hashemzadeh, S.; Ramezani, F.; Rafii-Tabar, H. Study of molecular mechanism of the interaction between MEK1/2 and trametinib with docking and molecular dynamic simulation. Interdiscip. Sci., 2019, 11(1), 115-124. doi: 10.1007/s12539-018-0305-4 PMID: 30465279
  97. de Matos, M.; Posa, I.; Carvalho, F.; Morais, V.; Grosso, A.; de Almeida, S. A systematic pan-cancer analysis of genetic heterogeneity reveals associations with epigenetic modifiers. Cancers (Basel), 2019, 11(3), 391. doi: 10.3390/cancers11030391 PMID: 30897760
  98. Wooller, S.K.; Benstead-Hume, G.; Chen, X.; Ali, Y.; Pearl, F.M.G. Bioinformatics in translational drug discovery. Biosci. Rep., 2017, 37(4), BSR20160180. doi: 10.1042/BSR20160180 PMID: 28487472
  99. Zhang, Y.; Tang, X.; Pang, Y.; Huang, L.; Wang, D.; Yuan, C.; Hu, X.; Qu, L. The potential mechanism of bufadienolide-like chemicals on breast cancer via bioinformatics analysis. Cancers (Basel), 2019, 11(1), 91. doi: 10.3390/cancers11010091 PMID: 30646630
  100. Kumar, V.; Krishna, S.; Siddiqi, M.I. Virtual screening strategies: Recent advances in the identification and design of anti-cancer agents. Methods, 2015, 71(C), 64-70. doi: 10.1016/j.ymeth.2014.08.010 PMID: 25171960
  101. Purushottamachar, P.; Khandelwal, A.; Chopra, P.; Maheshwari, N.; Gediya, L.K.; Vasaitis, T.S.; Bruno, R.D.; Clement, O.O.; Njar, V.C.O. First pharmacophore-based identification of androgen receptor down-regulating agents: Discovery of potent anti-prostate cancer agents. Bioorg. Med. Chem., 2007, 15(10), 3413-3421. doi: 10.1016/j.bmc.2007.03.019 PMID: 17383188
  102. Füllbeck, M.; Huang, X.; Dumdey, R.; Frommel, C.; Dubiel, W.; Preissner, R. Novel curcumin- and emodin-related compounds identified by in silico 2D/3D conformer screening induce apoptosis in tumor cells. BMC Cancer, 2005, 5(1), 97. doi: 10.1186/1471-2407-5-97 PMID: 16083495
  103. Wang, Z.; Lu, Y.; Seibel, W.; Miller, D.D.; Li, W. Identifying novel molecular structures for advanced melanoma by ligand-based virtual screening. J. Chem. Inf. Model., 2009, 49(6), 1420-1427. doi: 10.1021/ci800445a PMID: 19445498
  104. Siddiquee, K.; Zhang, S.; Guida, W.C.; Blaskovich, M.A.; Greedy, B.; Lawrence, H.R.; Yip, M.L.R.; Jove, R.; McLaughlin, M.M.; Lawrence, N.J.; Sebti, S.M.; Turkson, J. Selective chemical probe inhibitor of Stat3, identified through structure-based virtual screening, induces antitumor activity. Proc. Natl. Acad. Sci. USA, 2007, 104(18), 7391-7396. doi: 10.1073/pnas.0609757104 PMID: 17463090
  105. Nolan, K.A.; Dunstan, M.S.; Caraher, M.C.; Scott, K.A.; Leys, D.; Stratford, I.J. In silico screening reveals structurally diverse, nanomolar inhibitors of NQO2 that are functionally active in cells and can modulate NF-κB signaling. Mol. Cancer Ther., 2012, 11(1), 194-203. doi: 10.1158/1535-7163.MCT-11-0543 PMID: 22090421
  106. Lu, Y.; Nikolovska-Coleska, Z.; Fang, X.; Gao, W.; Shangary, S.; Qiu, S.; Qin, D.; Wang, S. Discovery of a nanomolar inhibitor of the human murine double minute 2 (MDM2)-p53 interaction through an integrated, virtual database screening strategy. J. Med. Chem., 2006, 49(13), 3759-3762.
  107. Krishna, S.; Singh, D.K.; Meena, S.; Datta, D.; Siddiqi, M.I.; Banerjee, D. Pharmacophore-based screening and identification of novel human ligase I inhibitors with potential anticancer activity. J. Chem. Inf. Model., 2014, 54(3), 781-792. doi: 10.1021/ci5000032 PMID: 24593844
  108. Dokla, E.M.; Mahmoud, A.H.; Elsayed, M.S.A.; El-Khatib, A.H.; Linscheid, M.W.; Abouzid, K.A. Applying ligands profiling using multiple extended electron distribution based field templates and feature trees similarity searching in the discovery of new generation of urea-based antineoplastic kinase inhibitors. PLoS One, 2012, 7(11), e49284. doi: 10.1371/journal.pone.0049284 PMID: 23185312
  109. Ren, J.X.; Li, L.L.; Zheng, R.L.; Xie, H.Z.; Cao, Z.X.; Feng, S.; Pan, Y.L.; Chen, X.; Wei, Y.Q.; Yang, S.Y. Discovery of novel Pim-1 kinase inhibitors by a hierarchical multistage virtual screening approach based on SVM model, pharmacophore, and molecular docking. J. Chem. Inf. Model., 2011, 51(6), 1364-1375. doi: 10.1021/ci100464b PMID: 21618971
  110. Massarotti, A.; Theeramunkong, S.; Mesenzani, O.; Caldarelli, A.; Genazzani, A.A.; Tron, G.C. Identification of novel antitubulin agents by using a virtual screening approach based on a 7-point pharmacophore model of the tubulin colchi-site. Chem. Biol. Drug Des., 2011, 78(6), 913-922. doi: 10.1111/j.1747-0285.2011.01245.x PMID: 22039890
  111. Kong, X.; Qin, J.; Li, Z.; Vultur, A.; Tong, L.; Feng, E.; Rajan, G.; Liu, S.; Lu, J.; Liang, Z.; Zheng, M.; Zhu, W.; Jiang, H.; Herlyn, M.; Liu, H.; Marmorstein, R.; Luo, C. Development of a novel class of B-RafV600E-selective inhibitors through virtual screening and hierarchical hit optimization. Org. Biomol. Chem., 2012, 10(36), 7402-7417. doi: 10.1039/c2ob26081f PMID: 22875039
  112. Lung, J.; Hung, M.S.; Lin, Y.C.; Hung, C.H.; Chen, C.C.; Lee, K.D.; Tsai, Y.H. Virtual screening and in vitro evaluation of PD-1 dimer stabilizers for uncoupling PD-1/PD-L1 interaction from natural products. Molecules, 2020, 25(22), 529.
  113. Aziz, M.; Ejaz, S.A.; Zargar, S.; Akhtar, N.; Aborode, A.T.A.; A Wani, T. Batiha, G.E.; Siddique, F.; Alqarni, M.; Akintola, A.A. Deep learning and structure-based virtual screening for drug discovery against NEK7: A novel target for the treatment of cancer. Molecules, 2022, 27(13), 4098. doi: 10.3390/molecules27134098 PMID: 35807344
  114. Singh, S.P.; Konwar, B.K. Molecular docking studies of quercetin and its analogues against human inducible nitric oxide synthase. Springerplus, 2012, 1(1), 69. doi: 10.1186/2193-1801-1-69 PMID: 23556141
  115. Talambedu, U.; Sushil, K.; Arvind, K.; Mahesh, K.; Da, M.; Syed, F.; Peyush, G.; Hp, P.; Veena, P. Molecular docking studies of anti-cancerous candidates in Hippophae rhamnoides and Hippophae salicifolia. J. Biomed. Res., 2014, 28(5), 406-415. doi: 10.7555/JBR.28.20130110 PMID: 25332713
  116. Umar, A.B.; Uzairu, A.; Shallangwa, G.A.; Uba, S. QSAR modelling and molecular docking studies for anti-cancer compounds against melanoma cell line SK-MEL-2. Heliyon, 2020, 6(3), e03640. doi: 10.1016/j.heliyon.2020.e03640 PMID: 32258485
  117. Ammad-ud-din. M.; Georgii, E.; Gönen, M.; Laitinen, T.; Kallioniemi, O.; Wennerberg, K.; Poso, A.; Kaski, S. Integrative and personalized QSAR analysis in cancer by kernelized Bayesian matrix factorization. J. Chem. Inf. Model., 2014, 54(8), 2347-2359. doi: 10.1021/ci500152b PMID: 25046554
  118. Martin, Y.C. 3D QSAR: Current state, scope, and limitations. Perspect. Drug Discov. Des., 1998, 12/14, 3-23. doi: 10.1023/A:1017037831628
  119. Peter, R. Ashton, Matthew C. T. Fyfe, Sarah K. Hickingbottom, J. Fraser Stoddart AJPW and DJW. Hammett correlations ‘beyond the molecule. J. Chem. Soc. Perkin Trans. 2, 1998. doi: 10.1039/a802406e
  120. Abdulrahman, H.L.; Uzairu, A.; Uba, S. QSAR, ligand based design and pharmacokinetic studies of parviflorons derivatives as anti-breast cancer drug compounds against MCF-7 cell line. Chem. Africa, 2021, 4(1), 175-187. doi: 10.1007/s42250-020-00207-7
  121. Drews, J. Drug discovery: A historical perspective. Science, 2000, 287(5460), 1960-1964. doi: 10.1126/science.287.5460.1960
  122. Zarrei, M.; MacDonald, J.R.; Merico, D.; Scherer, S.W. A copy number variation map of the human genome. Nat. Rev. Genet., 2015, 16(3), 172-183. doi: 10.1038/nrg3871 PMID: 25645873
  123. Dees, N.D.; Zhang, Q.; Kandoth, C.; Wendl, M.C.; Schierding, W.; Koboldt, D.C.; Mooney, T.B.; Callaway, M.B.; Dooling, D.; Mardis, E.R.; Wilson, R.K.; Ding, L. MuSiC: Identifying mutational significance in cancer genomes. Genome Res., 2012, 22(8), 1589-1598. doi: 10.1101/gr.134635.111 PMID: 22759861
  124. Lawrence, M.S.; Stojanov, P.; Polak, P.; Kryukov, G.V.; Cibulskis, K.; Sivachenko, A.; Carter, S.L.; Stewart, C.; Mermel, C.H.; Roberts, S.A.; Kiezun, A.; Hammerman, P.S.; McKenna, A.; Drier, Y.; Zou, L.; Ramos, A.H.; Pugh, T.J.; Stransky, N.; Helman, E.; Kim, J.; Sougnez, C.; Ambrogio, L.; Nickerson, E.; Shefler, E.; Cortés, M.L.; Auclair, D.; Saksena, G.; Voet, D.; Noble, M.; DiCara, D.; Lin, P.; Lichtenstein, L.; Heiman, D.I.; Fennell, T.; Imielinski, M.; Hernandez, B.; Hodis, E.; Baca, S.; Dulak, A.M.; Lohr, J.; Landau, D.A.; Wu, C.J.; Melendez-Zajgla, J.; Hidalgo-Miranda, A.; Koren, A.; McCarroll, S.A.; Mora, J.; Lee, R.S.; Crompton, B.; Onofrio, R.; Parkin, M.; Winckler, W.; Ardlie, K.; Gabriel, S.B.; Roberts, C.W.M.; Biegel, J.A.; Stegmaier, K.; Bass, A.J.; Garraway, L.A.; Meyerson, M.; Golub, T.R.; Gordenin, D.A.; Sunyaev, S.; Lander, E.S.; Getz, G. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature, 2013, 499(7457), 214-218. doi: 10.1038/nature12213 PMID: 23770567
  125. Hua, X.; Xu, H.; Yang, Y.; Zhu, J.; Liu, P.; Lu, Y. DrGaP: a powerful tool for identifying driver genes and pathways in cancer sequencing studies. Am. J. Hum. Genet., 2013, 93(3), 439-451. doi: 10.1016/j.ajhg.2013.07.003 PMID: 23954162
  126. Youn, A.; Simon, R. Identifying cancer driver genes in tumor genome sequencing studies. Bioinformatics, 2011, 27(2), 175-181. doi: 10.1093/bioinformatics/btq630 PMID: 21169372
  127. Reimand, J.; Bader, G.D. Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Mol. Syst. Biol., 2013, 9(1), 637. doi: 10.1038/msb.2012.68 PMID: 23340843
  128. Gonzalez-Perez, A.; Lopez-Bigas, N. Functional impact bias reveals cancer drivers. Nucleic Acids Res., 2012, 40(21), e169. doi: 10.1093/nar/gks743 PMID: 22904074
  129. Tamborero, D.; Gonzalez-Perez, A.; Lopez-Bigas, N. OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics, 2013, 29(18), 2238-2244. doi: 10.1093/bioinformatics/btt395 PMID: 23884480
  130. Tian, R.; Basu, M.K.; Capriotti, E. ContrastRank: a new method for ranking putative cancer driver genes and classification of tumor samples. Bioinformatics, 2014, 30(17), i572-i578. doi: 10.1093/bioinformatics/btu466 PMID: 25161249
  131. Weinstein, J.N.; Collisson, E.A.; Mills, G.B.; Shaw, K.R.M.; Ozenberger, B.A.; Ellrott, K.; Shmulevich, I.; Sander, C.; Stuart, J.M. The cancer genome atlas pan-cancer analysis project. Nat. Genet., 2013, 45(10), 1113-1120. doi: 10.1038/ng.2764 PMID: 24071849
  132. Hudson, T.J.; Anderson, W.; Artez, A.; Barker, A.D.; Bell, C.; Bernabé, R.R.; Bhan, M.K.; Calvo, F.; Eerola, I.; Gerhard, D.S.; Guttmacher, A.; Guyer, M.; Hemsley, F.M.; Jennings, J.L.; Kerr, D.; Klatt, P.; Kolar, P.; Kusada, J.; Lane, D.P.; Laplace, F.; Youyong, L.; Nettekoven, G.; Ozenberger, B.; Peterson, J.; Rao, T.S.; Remacle, J.; Schafer, A.J.; Shibata, T.; Stratton, M.R.; Vockley, J.G.; Watanabe, K.; Yang, H.; Yuen, M.M.; Knoppers, B.M.; Bobrow, M.; Cambon-Thomsen, A.; Dressler, L.G.; Dyke, S.O.; Joly, Y.; Kato, K.; Kennedy, K.L.; Nicolás, P.; Parker, M.J.; Rial-Sebbag, E.; Romeo-Casabona, C.M.; Shaw, K.M.; Wallace, S.; Wiesner, G.L.; Zeps, N.; Lichter, P.; Biankin, A.V.; Chabannon, C.; Chin, L.; Clément, B.; de Alava, E.; Degos, F.; Ferguson, M.L.; Geary, P.; Hayes, D.N.; Hudson, T.J.; Johns, A.L.; Kasprzyk, A.; Nakagawa, H.; Penny, R.; Piris, M.A.; Sarin, R.; Scarpa, A.; Shibata, T.; van de Vijver, M.; Futreal, P.A.; Aburatani, H.; Bayés, M.; Botwell, D.D.; Campbell, P.J.; Estivill, X.; Gerhard, D.S.; Grimmond, S.M.; Gut, I.; Hirst, M.; López-Otín, C.; Majumder, P.; Marra, M.; McPherson, J.D.; Nakagawa, H.; Ning, Z.; Puente, X.S.; Ruan, Y.; Shibata, T.; Stratton, M.R.; Stunnenberg, H.G.; Swerdlow, H.; Velculescu, V.E.; Wilson, R.K.; Xue, H.H.; Yang, L.; Spellman, P.T.; Bader, G.D.; Boutros, P.C.; Campbell, P.J.; Flicek, P.; Getz, G.; Guigó, R.; Guo, G.; Haussler, D.; Heath, S.; Hubbard, T.J.; Jiang, T.; Jones, S.M.; Li, Q.; López-Bigas, N.; Luo, R.; Muthuswamy, L.; Ouellette, B.F.; Pearson, J.V.; Puente, X.S.; Quesada, V.; Raphael, B.J.; Sander, C.; Shibata, T.; Speed, T.P.; Stein, L.D.; Stuart, J.M.; Teague, J.W.; Totoki, Y.; Tsunoda, T.; Valencia, A.; Wheeler, D.A.; Wu, H.; Zhao, S.; Zhou, G.; Stein, L.D.; Guigó, R.; Hubbard, T.J.; Joly, Y.; Jones, S.M.; Kasprzyk, A.; Lathrop, M.; López-Bigas, N.; Ouellette, B.F.; Spellman, P.T.; Teague, J.W.; Thomas, G.; Valencia, A.; Yoshida, T.; Kennedy, K.L.; Axton, M.; Dyke, S.O.; Futreal, P.A.; Gerhard, D.S.; Gunter, C.; Guyer, M.; Hudson, T.J.; McPherson, J.D.; Miller, L.J.; Ozenberger, B.; Shaw, K.M.; Kasprzyk, A.; Stein, L.D.; Zhang, J.; Haider, S.A.; Wang, J.; Yung, C.K.; Cros, A.; Liang, Y.; Gnaneshan, S.; Guberman, J.; Hsu, J.; Bobrow, M.; Chalmers, D.R.; Hasel, K.W.; Joly, Y.; Kaan, T.S.; Kennedy, K.L.; Knoppers, B.M.; Lowrance, W.W.; Masui, T.; Nicolás, P.; Rial-Sebbag, E.; Rodriguez, L.L.; Vergely, C.; Yoshida, T.; Grimmond, S.M.; Biankin, A.V.; Bowtell, D.D.; Cloonan, N.; deFazio, A.; Eshleman, J.R.; Etemadmoghadam, D.; Gardiner, B.B.; Kench, J.G.; Scarpa, A.; Sutherland, R.L.; Tempero, M.A.; Waddell, N.J.; Wilson, P.J.; McPherson, J.D.; Gallinger, S.; Tsao, M.S.; Shaw, P.A.; Petersen, G.M.; Mukhopadhyay, D.; Chin, L.; DePinho, R.A.; Thayer, S.; Muthuswamy, L.; Shazand, K.; Beck, T.; Sam, M.; Timms, L.; Ballin, V.; Lu, Y.; Ji, J.; Zhang, X.; Chen, F.; Hu, X.; Zhou, G.; Yang, Q.; Tian, G.; Zhang, L.; Xing, X.; Li, X.; Zhu, Z.; Yu, Y.; Yu, J.; Yang, H.; Lathrop, M.; Tost, J.; Brennan, P.; Holcatova, I.; Zaridze, D.; Brazma, A.; Egevard, L.; Prokhortchouk, E.; Banks, R.E.; Uhlén, M.; Cambon-Thomsen, A.; Viksna, J.; Ponten, F.; Skryabin, K.; Stratton, M.R.; Futreal, P.A.; Birney, E.; Borg, A.; Børresen-Dale, A.L.; Caldas, C.; Foekens, J.A.; Martin, S.; Reis-Filho, J.S.; Richardson, A.L.; Sotiriou, C.; Stunnenberg, H.G.; Thoms, G.; van de Vijver, M.; van’t Veer, L.; Calvo, F.; Birnbaum, D.; Blanche, H.; Boucher, P.; Boyault, S.; Chabannon, C.; Gut, I.; Masson-Jacquemier, J.D.; Lathrop, M.; Pauporté, I.; Pivot, X.; Vincent-Salomon, A.; Tabone, E.; Theillet, C.; Thomas, G.; Tost, J.; Treilleux, I.; Calvo, F.; Bioulac-Sage, P.; Clément, B.; Decaens, T.; Degos, F.; Franco, D.; Gut, I.; Gut, M.; Heath, S.; Lathrop, M.; Samuel, D.; Thomas, G.; Zucman-Rossi, J.; Lichter, P.; Eils, R.; Brors, B.; Korbel, J.O.; Korshunov, A.; Landgraf, P.; Lehrach, H.; Pfister, S.; Radlwimmer, B.; Reifenberger, G.; Taylor, M.D.; von Kalle, C.; Majumder, P.P.; Sarin, R.; Rao, T.S.; Bhan, M.K.; Scarpa, A.; Pederzoli, P.; Lawlor, R.A.; Delledonne, M.; Bardelli, A.; Biankin, A.V.; Grimmond, S.M.; Gress, T.; Klimstra, D.; Zamboni, G.; Shibata, T.; Nakamura, Y.; Nakagawa, H.; Kusada, J.; Tsunoda, T.; Miyano, S.; Aburatani, H.; Kato, K.; Fujimoto, A.; Yoshida, T.; Campo, E.; López-Otín, C.; Estivill, X.; Guigó, R.; de Sanjosé, S.; Piris, M.A.; Montserrat, E.; González-Díaz, M.; Puente, X.S.; Jares, P.; Valencia, A.; Himmelbauer, H.; Quesada, V.; Bea, S.; Stratton, M.R.; Futreal, P.A.; Campbell, P.J.; Vincent-Salomon, A.; Richardson, A.L.; Reis-Filho, J.S.; van de Vijver, M.; Thomas, G.; Masson-Jacquemier, J.D.; Aparicio, S.; Borg, A.; Børresen-Dale, A.L.; Caldas, C.; Foekens, J.A.; Stunnenberg, H.G.; van’t Veer, L.; Easton, D.F.; Spellman, P.T.; Martin, S.; Barker, A.D.; Chin, L.; Collins, F.S.; Compton, C.C.; Ferguson, M.L.; Gerhard, D.S.; Getz, G.; Gunter, C.; Guttmacher, A.; Guyer, M.; Hayes, D.N.; Lander, E.S.; Ozenberger, B.; Penny, R.; Peterson, J.; Sander, C.; Shaw, K.M.; Speed, T.P.; Spellman, P.T.; Vockley, J.G.; Wheeler, D.A.; Wilson, R.K.; Hudson, T.J.; Chin, L.; Knoppers, B.M.; Lander, E.S.; Lichter, P.; Stein, L.D.; Stratton, M.R.; Anderson, W.; Barker, A.D.; Bell, C.; Bobrow, M.; Burke, W.; Collins, F.S.; Compton, C.C.; DePinho, R.A.; Easton, D.F.; Futreal, P.A.; Gerhard, D.S.; Green, A.R.; Guyer, M.; Hamilton, S.R.; Hubbard, T.J.; Kallioniemi, O.P.; Kennedy, K.L.; Ley, T.J.; Liu, E.T.; Lu, Y.; Majumder, P.; Marra, M.; Ozenberger, B.; Peterson, J.; Schafer, A.J.; Spellman, P.T.; Stunnenberg, H.G.; Wainwright, B.J.; Wilson, R.K.; Yang, H. International network of cancer genome projects. Nature, 2010, 464(7291), 993-998. doi: 10.1038/nature08987 PMID: 20393554
  133. Wu, Y.; Cheng, Y.; Wang, X.; Fan, J.; Gao, Q. Spatial omics: Navigating to the golden era of cancer research. Clin. Transl. Med., 2022, 12(1), e696. doi: 10.1002/ctm2.696 PMID: 35040595
  134. Bergom, H.E.; Shabaneh, A.; Day, A.; Ali, A.; Boytim, E.; Tape, S.; Lozada, J.R.; Shi, X.; Kerkvliet, C.P.; McSweeney, S.; Pitzen, S.P.; Ludwig, M.; Antonarakis, E.S.; Drake, J.M.; Dehm, S.M.; Ryan, C.J.; Wang, J.; Hwang, J. ALAN is a computational approach that interprets genomic findings in the context of tumor ecosystems. Commun. Biol., 2023, 6(1), 417. doi: 10.1038/s42003-023-04795-1 PMID: 37059746
  135. Jiang, P.; Sinha, S.; Aldape, K.; Hannenhalli, S.; Sahinalp, C.; Ruppin, E. Big data in basic and translational cancer research. Nat. Rev. Cancer, 2022, 22(11), 625-639. doi: 10.1038/s41568-022-00502-0 PMID: 36064595
  136. Kim, T.; Rao, J. "SMART" cytology: The next generation cytology for precision diagnosis. Semin. Diagn. Pathol., 2023, 40(2), 95-99. doi: 10.1053/j.semdp.2023.01.001 PMID: 36639316
  137. Yang, S.; Yang, Z.; Yang, J. 4mCBERT: A computing tool for the identification of DNA N4-methylcytosine sites by sequence- and chemical-derived information based on ensemble learning strategies. Int. J. Biol. Macromol., 2023, 231, 123180. doi: 10.1016/j.ijbiomac.2023.123180 PMID: 36646347
  138. Bhatt, M.; Shende, P. Advancement in machine learning: A strategic lookout from cancer identification to treatment. Arch. Comput. Methods Eng., 2023, 30(4), 2777-2792. doi: 10.1007/s11831-023-09886-0
  139. Rajitha Perera, R.P. BRDriver: Breast cancer driver gene predictor. bioRxiv, 2023, 0-4. Available from: https://www.biorxiv.org/content/10.1101/2023.01.09.523362v1?rss=1&utm_source=researcher_app&utm_medium=referral&utm_campaign=RESR_MRKT_Researcher_inbound

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