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Current Drug Delivery

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ISSN (Print): 1567-2018
ISSN (Online): 1875-5704

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Review Article

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer

Author(s): Vishal Sharma, Amit Singh*, Sanjana Chauhan, Pramod Kumar Sharma, Shubham Chaudhary, Astha Sharma, Omji Porwal and Neeraj Kumar Fuloria

Volume 21, Issue 6, 2024

Published on: 06 September, 2023

Page: [870 - 886] Pages: 17

DOI: 10.2174/1567201821666230905090621

Price: $65

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Abstract

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.

Keywords: Artificial intelligence, machine learning, drug discovery and development, target identification, multi-omics database.

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[1]
Haenlein, M.; Kaplan, A. A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. Calif. Manage. Rev., 2019, 61(4), 5-14.
[http://dx.doi.org/10.1177/0008125619864925]
[2]
Hamet, P.; Tremblay, J. Artificial intelligence in medicine. Metabolism, 2017, 69, S36-S40.
[http://dx.doi.org/10.1016/j.metabol.2017.01.011] [PMID: 28126242]
[3]
Pasek, A. Renaissance robotics: Leonardo da vinci’s lost knight and enlivened materiality; Shift, 2014, p. 7.
[4]
Urbina, F.; Lentzos, F.; Invernizzi, C.; Ekins, S. Dual use of artificial-intelligence-powered drug discovery. Nat. Mach. Intell., 2022, 4(3), 189-191.
[http://dx.doi.org/10.1038/s42256-022-00465-9] [PMID: 36211133]
[5]
Allen, B., Jr; Seltzer, S.E.; Langlotz, C.P.; Dreyer, K.P.; Summers, R.M.; Petrick, N.; Marinac-Dabic, D.; Cruz, M.; Alkasab, T.K.; Hanisch, R.J.; Nilsen, W.J.; Burleson, J.; Lyman, K.; Kandarpa, K. A road map for translational research on artificial intelligence in medical imaging: From the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop. J. Am. Coll. Radiol., 2019, 16(9), 1179-1189.
[http://dx.doi.org/10.1016/j.jacr.2019.04.014] [PMID: 31151893]
[6]
Wetzel, S. Similarity in chemical and protein space: Finding novel starting points for library design; Dortmund , 2009.
[7]
Gupta, R.; Srivastava, D.; Sahu, M.; Tiwari, S.; Ambasta, R.K.; Kumar, P. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery. Mol. Divers., 2021, 25(3), 1315-1360.
[http://dx.doi.org/10.1007/s11030-021-10217-3] [PMID: 33844136]
[8]
He, J.; Baxter, S.L.; Xu, J.; Xu, J.; Zhou, X.; Zhang, K. The practical implementation of artificial intelligence technologies in medicine. Nat. Med., 2019, 25(1), 30-36.
[http://dx.doi.org/10.1038/s41591-018-0307-0] [PMID: 30617336]
[9]
Menyhárt, O. Győrffy, B. Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis. Comput. Struct. Biotechnol. J., 2021, 19, 949-960.
[http://dx.doi.org/10.1016/j.csbj.2021.01.009] [PMID: 33613862]
[10]
Agatonovic-Kustrin, S.; Beresford, R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J. Pharm. Biomed. Anal., 2000, 22(5), 717-727.
[http://dx.doi.org/10.1016/S0731-7085(99)00272-1] [PMID: 10815714]
[11]
Singh, A.V.; Ansari, M.H.D.; Rosenkranz, D.; Maharjan, R.S.; Kriegel, F.L.; Gandhi, K.; Kanase, A.; Singh, R.; Laux, P.; Luch, A. Artificial intelligence and machine learning in computational nanotoxicology: Unlocking and empowering nanomedicine. Adv. Healthc. Mater., 2020, 9(17), 1901862.
[http://dx.doi.org/10.1002/adhm.201901862] [PMID: 32627972]
[12]
Delen, D.; Walker, G.; Kadam, A. Predicting breast cancer survivability: A comparison of three data mining methods. Artif. Intell. Med., 2005, 34(2), 113-127.
[http://dx.doi.org/10.1016/j.artmed.2004.07.002] [PMID: 15894176]
[13]
Zhang, J.; Li, C.; Yin, Y.; Zhang, J.; Grzegorzek, M. Applications of artificial neural networks in microorganism image analysis: A comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer. Artif. Intell. Rev., 2022, 56(2), 1013-1070.
[PMID: 35528112]
[14]
Sun, N.; Zhang, J.; Rimba, P.; Gao, S.; Zhang, L.Y.; Xiang, Y. Data-driven cybersecurity incident prediction: A survey. IEEE Commun. Surv. Tutor., 2019, 21(2), 1744-1772.
[http://dx.doi.org/10.1109/COMST.2018.2885561]
[15]
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.
[http://dx.doi.org/10.1016/j.drudis.2020.10.010] [PMID: 33099022]
[16]
Ciaburro, G.; Venkateswaran, B. Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles; Packt Publishing Ltd., 2017.
[17]
Dargan, S.; Kumar, M.; Ayyagari, M.R.; Kumar, G. A survey of deep learning and its applications: A new paradigm to machine learning. Arch. Comput. Methods Eng., 2020, 27(4), 1071-1092.
[http://dx.doi.org/10.1007/s11831-019-09344-w]
[18]
Levine, A.B.; Schlosser, C.; Grewal, J.; Coope, R.; Jones, S.J.M.; Yip, S. Rise of the machines: Advances in deep learning for cancer diagnosis. Trends Cancer, 2019, 5(3), 157-169.
[http://dx.doi.org/10.1016/j.trecan.2019.02.002] [PMID: 30898263]
[19]
Jing, Y.; Bian, Y.; Hu, Z.; Wang, L.; Xie, X.Q.S. Deep learning for drug design: An artificial intelligence paradigm for drug discovery in the big data era. AAPS J., 2018, 20(3), 58.
[http://dx.doi.org/10.1208/s12248-018-0210-0] [PMID: 29603063]
[20]
Proschak, E.; Stark, H.; Merk, D. Polypharmacology by design: A medicinal chemist’s perspective on multitargeting compounds. J. Med. Chem., 2019, 62(2), 420-444.
[http://dx.doi.org/10.1021/acs.jmedchem.8b00760] [PMID: 30035545]
[21]
Kitchen, D.B.; Decornez, H.; Furr, J.R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov., 2004, 3(11), 935-949.
[http://dx.doi.org/10.1038/nrd1549] [PMID: 15520816]
[22]
Sinha, S.; Vohora, D. Drug discovery and development: An overview. In: Pharmaceutical Medicine and Translational Clinical Research; , 2018; pp. 19-32.
[23]
Malandraki-Miller, S.; Riley, P.R. Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discov. Today, 2021, 26(4), 887-901.
[http://dx.doi.org/10.1016/j.drudis.2021.01.013] [PMID: 33484947]
[24]
McGorry, P.D.; Yung, A.R.; Pantelis, C.; Hickie, I.B. A clinical trials agenda for testing interventions in earlier stages of psychotic disorders. Med. J. Aust., 2009, 190(S4), S33-S36.
[http://dx.doi.org/10.5694/j.1326-5377.2009.tb02372.x] [PMID: 19220171]
[25]
Wang, F.; Ding, Y.; Lei, X.; Liao, B.; Wu, F.X. Machine learning and deep learning strategies in drug repositioning. Curr. Bioinform., 2022, 17(3), 217-237.
[http://dx.doi.org/10.2174/1574893616666211119093100]
[26]
Bagdonas, H.; Fogarty, C.A.; Fadda, E.; Agirre, J. The case for post-predictional modifications in the AlphaFold Protein Structure Database. Nat. Struct. Mol. Biol., 2021, 28(11), 869-870.
[http://dx.doi.org/10.1038/s41594-021-00680-9] [PMID: 34716446]
[27]
Gromski, P.S.; Granda, J.M.; Cronin, L. Universal chemical synthesis and discovery with ‘The Chemputer.’. Trends Chem., 2020, 2(1), 4-12.
[http://dx.doi.org/10.1016/j.trechm.2019.07.004]
[28]
Tripathi, K.D. Essentials of medical pharmacology; JP Medical Ltd., 2013.
[29]
Basu, A.; Sarkar, A.; Maulik, U. Molecular docking study of potential phytochemicals and their effects on the complex of SARS-CoV2 spike protein and human ACE2. Sci. Rep., 2020, 10(1), 17699.
[http://dx.doi.org/10.1038/s41598-020-74715-4] [PMID: 33077836]
[30]
Gacche, R.N.; Meshram, R.J. Angiogenic factors as potential drug target: efficacy and limitations of anti-angiogenic therapy. Biochim. Biophys. Acta, 2014, 1846(1), 161-179.
[PMID: 24836679]
[31]
Paul, S.M.; Mytelka, D.S.; Dunwiddie, C.T.; Persinger, C.C.; Munos, B.H.; Lindborg, S.R.; Schacht, A.L. How to improve R&D productivity: The pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov., 2010, 9(3), 203-214.
[http://dx.doi.org/10.1038/nrd3078] [PMID: 20168317]
[32]
Miller, E.J.; Lappin, S.L. Physiology, cellular receptor. In: StatPearls; Stat Pearls Publishing, 2021.
[33]
Moffat, J.G.; Vincent, F.; Lee, J.A.; Eder, J.; Prunotto, M. Opportunities and challenges in phenotypic drug discovery: An industry perspective. Nat. Rev. Drug Discov., 2017, 16(8), 531-543.
[http://dx.doi.org/10.1038/nrd.2017.111] [PMID: 28685762]
[34]
Wang, X.; Song, K.; Li, L.; Chen, L. Structure-based drug design strategies and challenges. Curr. Top. Med. Chem., 2018, 18(12), 998-1006.
[http://dx.doi.org/10.2174/1568026618666180813152921] [PMID: 30101712]
[35]
Wang, T.; Wu, M.B.; Zhang, R.H.; Chen, Z.J.; Hua, C.; Lin, J.P.; Yang, L.R. Advances in computational structure-based drug design and application in drug discovery. Curr. Top. Med. Chem., 2015, 16(9), 901-916.
[http://dx.doi.org/10.2174/1568026615666150825142002] [PMID: 26303430]
[36]
Lee, Y.; Basith, S.; Choi, S. Recent advances in structure-based drug design targeting class AG protein-coupled receptors utilizing crystal structures and computational simulations. J. Med. Chem., 2018, 61(1), 1-46.
[http://dx.doi.org/10.1021/acs.jmedchem.6b01453] [PMID: 28657745]
[37]
Lionta, E.; Spyrou, G.; Vassilatis, D.; Cournia, Z. Structure-based virtual screening for drug discovery: Principles, applications and recent advances. Curr. Top. Med. Chem., 2014, 14(16), 1923-1938.
[http://dx.doi.org/10.2174/1568026614666140929124445] [PMID: 25262799]
[38]
Ferrando, J.; Solomon, L.A. Recent progress using de novo design to study protein structure, design, and binding interactions. Life, 2021, 11(3), 225.
[http://dx.doi.org/10.3390/life11030225] [PMID: 33802210]
[39]
Cheng, T.; Li, Q.; Wang, Y.; Bryant, S.H. Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining. J. Chem. Inf. Model., 2011, 51(9), 2440-2448.
[http://dx.doi.org/10.1021/ci200192v] [PMID: 21834535]
[40]
Simon, H.A. The organization of complex systems.Models of discovery; Springer, 1977, pp. 245-261.
[http://dx.doi.org/10.1007/978-94-010-9521-1_14]
[41]
Veselovsky, A.; Ivanov, A. Strategy of computer-aided drug design. Curr. Drug Targets Infect. Disord., 2003, 3(1), 33-40.
[http://dx.doi.org/10.2174/1568005033342145] [PMID: 12570731]
[42]
Van Norman, G.A. Drugs, devices, and the FEDAP: Part 1: An overview of approval processes for drugs. JACC Basic Transl. Sci., 2016, 1(3), 170-179.
[http://dx.doi.org/10.1016/j.jacbts.2016.03.002] [PMID: 30167510]
[43]
Pita-Juárez, Y.; Altschuler, G.; Kariotis, S.; Wei, W.; Koler, K.; Green, C.; Tanzi, R.E.; Hide, W. The pathway co-expression network: Revealing pathway relationships. PLOS Comput. Biol., 2018, 14(3), e1006042.
[http://dx.doi.org/10.1371/journal.pcbi.1006042] [PMID: 29554099]
[44]
Szklarczyk, D.; Jensen, L.J. Protein-protein interaction databases. In: Protein-protein Interactions; Springer, 2015; pp. 39-56.
[http://dx.doi.org/10.1007/978-1-4939-2425-7_3]
[45]
Zeeberg, B.R.; Qin, H.; Narasimhan, S.; Sunshine, M.; Cao, H.; Kane, D.W.; Reimers, M.; Stephens, R.M.; Bryant, D.; Burt, S.K.; Elnekave, E.; Hari, D.M.; Wynn, T.A.; Cunningham-Rundles, C.; Stewart, D.M.; Nelson, D.; Weinstein, J.N. High-Throughput GoMiner, an ‘industrial-strength’ integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID). BMC Bioinformatics, 2005, 6(1), 168.
[http://dx.doi.org/10.1186/1471-2105-6-168] [PMID: 15998470]
[46]
Dinu, I.; Potter, J.D.; Mueller, T.; Liu, Q.; Adewale, A.J.; Jhangri, G.S.; Einecke, G.; Famulski, K.S.; Halloran, P.; Yasui, Y. Improving gene set analysis of microarray data by SAM-GS. BMC Bioinformatics, 2007, 8(1), 242.
[http://dx.doi.org/10.1186/1471-2105-8-242] [PMID: 17612399]
[47]
Vaske, C.J.; Benz, S.C.; Sanborn, J.Z.; Earl, D.; Szeto, C.; Zhu, J.; Haussler, D.; Stuart, J.M. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics, 2010, 26(12), i237-i245.
[http://dx.doi.org/10.1093/bioinformatics/btq182] [PMID: 20529912]
[48]
Tomczak, K. Czerwińska, P.; Wiznerowicz, M. The cancer genome atlas (TCGA): An immeasurable source of knowledge. Contemp. Oncol., 2015, 19(1A), A68-A77.
[49]
Zhou, G.; Li, S.; Xia, J. Network-based approaches for multi-omics integration. In: Computational Methods and Data Analysis for Metabolomics; , 2020; pp. 469-487.
[50]
Dwivedi, Y.K.; Hughes, L.; Ismagilova, E.; Aarts, G.; Coombs, C.; Crick, T.; Duan, Y.; Dwivedi, R.; Edwards, J.; Eirug, A.; Galanos, V.; Ilavarasan, P.V.; Janssen, M.; Jones, P.; Kar, A.K.; Kizgin, H.; Kronemann, B.; Lal, B.; Lucini, B.; Medaglia, R.; Le Meunier-FitzHugh, K.; Le Meunier-FitzHugh, L.C.; Misra, S.; Mogaji, E.; Sharma, S.K.; Singh, J.B.; Raghavan, V.; Raman, R.; Rana, N.P.; Samothrakis, S.; Spencer, J.; Tamilmani, K.; Tubadji, A.; Walton, P.; Williams, M.D. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manage., 2021, 57, 101994.
[http://dx.doi.org/10.1016/j.ijinfomgt.2019.08.002]
[51]
Bagdonas, H.; Ungar, D.; Agirre, J. Leveraging glycomics data in glycoprotein 3D structure validation with Privateer. Beilstein J. Org. Chem., 2020, 16, 2523-2533.
[http://dx.doi.org/10.3762/bjoc.16.204] [PMID: 33093930]
[52]
Steiner, S.; Wolf, J.; Glatzel, S.; Andreou, A.; Granda, J.M.; Keenan, G.; Hinkley, T.; Aragon-Camarasa, G.; Kitson, P.J.; Angelone, D.; Cronin, L. Organic synthesis in a modular robotic system driven by a chemical programming language. Science, 2019, 363(6423), eaav2211.
[http://dx.doi.org/10.1126/science.aav2211] [PMID: 30498165]
[53]
Zhan, X.; You, Z.; Yu, C.; Li, L.; Pan, J. Ensemble learning prediction of drug-target interactions using GIST descriptor extracted from PSSM-based evolutionary information. BioMed Res. Int., 2020, 1, 4516250.
[54]
Goh, Garrett.; Sakloth, Khusheemn; Siegel, Charles; Vishnu, Abhinav Pfaendtner, Jim Multimodal deep neural networks using both engineered and learned representations for biodegradability prediction. , arXiv:1808.04456.2018
[55]
Stork, C.; Chen, Y.; Šícho, M.; Kirchmair, J. Hit Dexter 2.0: Machine-learning models for the prediction of frequent hitters. J. Chem. Inf. Model., 2019, 59(3), 1030-1043.
[http://dx.doi.org/10.1021/acs.jcim.8b00677] [PMID: 30624935]
[56]
Wang, C.; Zhang, Y. Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest. J. Comput. Chem., 2017, 38(3), 169-177.
[http://dx.doi.org/10.1002/jcc.24667] [PMID: 27859414]
[57]
Mayr, A.; Klambauer, G.; Unterthiner, T.; Hochreiter, S. DeepTox: toxicity prediction using deep learning. Front. Environ. Sci., 2016, 3, 1-15.
[58]
Duvenaud, D.; Dougal, M.; Jorge, A.I.; Rafael, G.B. Convolutional networks on graphs for learning molecular fingerprints. In: Advances in Neural Information Processing Systems; NIPS, 2015; p. 13.
[59]
Durrant, J.D.; McCammon, J.A. Molecular dynamics simulations and drug discovery. BMC Biol., 2011, 9(1), 71.
[http://dx.doi.org/10.1186/1741-7007-9-71] [PMID: 22035460]
[60]
Wojciechowski, J.; Hopkins, A.M.; Upton, R.N. Interactive pharmacometric applications using r and the shiny package. CPT Pharmacometrics Syst. Pharmacol., 2015, 4(3), 146-159.
[http://dx.doi.org/10.1002/psp4.21] [PMID: 26225240]
[61]
Sanchez-Lengeling, B.; Aspuru-Guzik, A. Inverse molecular design using machine learning: Generative models for matter engineering. Science, 2018, 361(6400), 360-365.
[http://dx.doi.org/10.1126/science.aat2663] [PMID: 30049875]
[62]
Feinberg, E.N.; Sur, D.; Wu, Z.; Husic, B.E.; Mai, H.; Li, Y.; Sun, S.; Yang, J.; Ramsundar, B.; Pande, V.S. Potential net for molecular property prediction. ACS Cent. Sci., 2018, 4(11), 1520-1530.
[http://dx.doi.org/10.1021/acscentsci.8b00507] [PMID: 30555904]
[63]
Awale, M.; Reymond, J-L. Polypharmacology browser PPB2: target prediction combining nearest neighbors with machine learning. J. Chem. Inf. Model., 2018.
[64]
Christensen, A.S.; Faber, F.A.; von Lilienfeld, O.A. Operators in quantum machine learning: Response properties in chemical space. J. Chem. Phys., 2019, 150(6), 064105.
[http://dx.doi.org/10.1063/1.5053562] [PMID: 30769998]
[65]
Olivecrona, M.; Blaschke, T.; Engkvist, O.; Chen, H. Molecular de-novo design through deep reinforcement learning. J. Cheminform., 2017, 9(1), 48.
[http://dx.doi.org/10.1186/s13321-017-0235-x] [PMID: 29086083]
[66]
Coley, C.W.; Green, W.H.; Jensen, K.F. Machine learning in computer-aided synthesis planning. Acc. Chem. Res., 2018, 51(5), 1281-1289.
[http://dx.doi.org/10.1021/acs.accounts.8b00087] [PMID: 29715002]
[67]
Yasuo, N.; Sekijima, M. Improved method of structure-based virtual screening via interaction-energy-based learning. J. Chem. Inf. Model., 2019, 59(3), 1050-1061.
[http://dx.doi.org/10.1021/acs.jcim.8b00673] [PMID: 30808172]
[68]
Deng, L.J.; Qi, M.; Li, N.; Lei, Y.H.; Zhang, D.M.; Chen, J.X. Natural products and their derivatives: Promising modulators of tumor immunotherapy. J. Leukoc. Biol., 2020, 108(2), 493-508.
[http://dx.doi.org/10.1002/JLB.3MR0320-444R] [PMID: 32678943]
[69]
Li, S.; Wan, F.; Shu, H.; Jiang, T.; Zhao, D.; Zeng, J. MONN: A multi-objective neural network for predicting compound-protein interactions and affinities. Cell Syst., 2020, 10(4), 308-322.e11.
[http://dx.doi.org/10.1016/j.cels.2020.03.002]
[70]
Ulander, S.; Gogishvili, D.; Ulander, S.; Nittinger, E.; Zhao, H. Siamese recurrent neural network with a self-attention mechanism for bioactivity prediction. ACS Omega, 2017, 6(16), 11086-11094.
[71]
Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Sci. Rep., 2021, 11(1), 321.
[http://dx.doi.org/10.1038/s41598-020-79682-4] [PMID: 33432013]
[72]
Segler, M.H.S.; Preuss, M.; Waller, M.P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 2018, 555(7698), 604-610.
[http://dx.doi.org/10.1038/nature25978] [PMID: 29595767]
[73]
Chen, Z.H.; You, Z.H.; Guo, Z.H.; Yi, H.C.; Luo, G.X.; Wang, Y.B. Prediction of drug–target interactions from multi-molecular network based on deep walk embedding model. Front. Bioeng. Biotechnol., 2020, 8, 338.
[http://dx.doi.org/10.3389/fbioe.2020.00338] [PMID: 32582646]
[74]
Li, J.; Wang, X.; Zhao, G.; Chen, C.; Chai, Z.; Alsaedi, A.; Hayat, T.; Wang, X. Metal–organic framework-based materials: superior adsorbents for the capture of toxic and radioactive metal ions. Chem. Soc. Rev., 2018, 47(7), 2322-2356.
[http://dx.doi.org/10.1039/C7CS00543A] [PMID: 29498381]
[75]
Crampon, M.; Hellal, J.; Mouvet, C.; Ollivier, P. Degradation of tetrachloroethylene by zero valent iron nanoparticles in the presence of a natural groundwater bacterial biofilm in a sandy porous media. Heliyon, 2021, 7(1), e05854.
[http://dx.doi.org/10.1016/j.heliyon.2020.e05854] [PMID: 33474508]
[76]
Yao, K.; Parkhill, J. Kinetic energy of hydrocarbons as a function of electron density and convolutional neural networks. J. Chem. Theory Comput., 2016, 12(3), 1139-1147.
[http://dx.doi.org/10.1021/acs.jctc.5b01011] [PMID: 26812530]
[77]
Kadurin, A.; Nikolenko, S.; Khrabrov, K.; Aliper, A.; Zhavoronkov, A. druGAN: An advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Mol. Pharm., 2017, 14(9), 3098-3104.
[http://dx.doi.org/10.1021/acs.molpharmaceut.7b00346] [PMID: 28703000]
[78]
Kozlovskii, I.; Popov, P. Spatiotemporal identification of druggable binding sites using deep learning. Commun. Biol., 2020, 3(1), 618.
[http://dx.doi.org/10.1038/s42003-020-01350-0] [PMID: 33110179]
[79]
Scott, J.S. Drugs, debates, deals, and deficits. Healthc. Financ. Manage., 2002, 56(12), 30-32, 33.
[PMID: 12516156]
[80]
Gök, M.; Heideman, D.A.M.; van Kemenade, F.J.; de Vries, A.L.M.; Berkhof, J.; Rozendaal, L.; Beliën, J.A.M.; Overbeek, L. Babović M.; Snijders, P.J.F.; Meijer, C.J.L.M. Offering self-sampling for human papillomavirus testing to non-attendees of the cervical screening programme: Characteristics of the responders. Eur. J. Cancer, 2012, 48(12), 1799-1808.
[http://dx.doi.org/10.1016/j.ejca.2011.11.022] [PMID: 22172570]
[81]
Wishart, D.S.; Knox, C.; Guo, A.C.; Cheng, D.; Shrivastava, S.; Tzur, D.; Gautam, B.; Hassanali, M. Drug Bank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res., 2008, 36.
[82]
Chen, Y.A.; Tripathi, L.P.; Mizuguchi, K. Target Mine, An integrated data warehouse for candidate gene prioritization and target discovery. PLoS One, 2011, 6(3), e17844.
[http://dx.doi.org/10.1371/journal.pone.0017844]
[83]
Kim, S.; Chen, J.; Cheng, T.; Gindulyte, A.; He, J.; He, S.; Li, Q.; Shoemaker, B.A.; Thiessen, P.A.; Yu, B.; Zaslavsky, L.; Zhang, J.; Bolton, E.E. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res., 2019, 47(D1), D1102-D1109.
[http://dx.doi.org/10.1093/nar/gky1033] [PMID: 30371825]
[84]
Daina, A.; Michielin, O.; Zoete, V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res., 2019, 47(W1), W357-W364.
[http://dx.doi.org/10.1093/nar/gkz382] [PMID: 31106366]
[85]
Gaulton, A.; Bellis, L.J.; Bento, A.P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J.P. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res., 2012, 40(D1), D1100-D1107.
[http://dx.doi.org/10.1093/nar/gkr777] [PMID: 21948594]
[86]
Mi, H.; Guo, N.; Kejariwal, A.; Thomas, P.D. PANTHER version 6: protein sequence and function evolution data with expanded representation of biological pathways. Nucleic Acids Res., 2007, 35(Database), D247-D252.
[http://dx.doi.org/10.1093/nar/gkl869] [PMID: 17130144]
[87]
Quantifying the impact of public general data. Nat. Commun., 2016, 10(1), 3512.
[88]
Barretina, J.; Caponigro, G.; Stransky, N.; Venkatesan, K.; Margolin, A.A.; Kim, S.; Wilson, C.J.; Lehár, J.; Kryukov, G.V.; Sonkin, D.; Reddy, A.; Liu, M.; Murray, L.; Berger, M.F.; Monahan, J.E.; Morais, P.; Meltzer, J.; Korejwa, A.; Jané-Valbuena, J.; Mapa, F.A.; Thibault, J.; Bric-Furlong, E.; Raman, P.; Shipway, A.; Engels, I.H.; Cheng, J.; Yu, G.K.; Yu, J.; Aspesi, P., Jr; de Silva, M.; Jagtap, K.; Jones, M.D.; Wang, L.; Hatton, C.; Palescandolo, E.; Gupta, S.; Mahan, S.; Sougnez, C.; Onofrio, R.C.; Liefeld, T.; MacConaill, L.; Winckler, W.; Reich, M.; Li, N.; Mesirov, J.P.; Gabriel, S.B.; Getz, G.; Ardlie, K.; Chan, V.; Myer, V.E.; Weber, B.L.; Porter, J.; Warmuth, M.; Finan, P.; Harris, J.L.; Meyerson, M.; Golub, T.R.; Morrissey, M.P.; Sellers, W.R.; Schlegel, R.; Garraway, L.A. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature, 2012, 483(7391), 603-607.
[http://dx.doi.org/10.1038/nature11003] [PMID: 22460905]
[89]
Smolle, M.; Workman, J.L. Transcription-associated histone modifications and cryptic transcription. Biochimica et Biophysica Acta (BBA)-. Gene Regulatory Mechanisms, 2013, 1, 84-97.
[90]
Hundal, J.; Carreno, B.M.; Petti, A.A.; Linette, G.P.; Griffith, O.L.; Mardis, E.R.; Griffith, M. pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens. Genome Med., 2016, 8(1), 11.
[http://dx.doi.org/10.1186/s13073-016-0264-5] [PMID: 26825632]
[91]
Jensen, L.J.; Kuhn, M.; Stark, M.; Chaffron, S.; Creevey, C.; Muller, J.; Doerks, T.; Julien, P.; Roth, A.; Simonovic, M.; Bork, P.; von Mering, C. STRING 8--a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res., 2009, 37(Database), D412-D416.
[http://dx.doi.org/10.1093/nar/gkn760] [PMID: 18940858]
[92]
Douglas, G.M.; Maffei, V.J.; Zaneveld, J.R.; Yurgel, S.N.; Brown, J.R.; Taylor, C.M.; Huttenhower, C.; Langille, M.G.I. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol., 2020, 38(6), 685-688.
[http://dx.doi.org/10.1038/s41587-020-0548-6] [PMID: 32483366]
[93]
Liu, T.; Liu, Z.; Yao, X.; Huang, Y.; Qu, Q.; Shi, X.; Zhang, H.; Shi, X. Identification of cordycepin biosynthesis-related genes through de novo transcriptome assembly and analysis in Cordyceps cicadae. R. Soc. Open Sci., 2018, 5(12), 181247.
[http://dx.doi.org/10.1098/rsos.181247] [PMID: 30662735]
[94]
Khaldun, A.B.; Huang, W.; Lv, H.; Liao, S.; Zeng, S. Wang, Y Comparative profiling of miRNAs and target gene identification in distant-grafting between tomato and lycium (Goji Berry). Front. Plant Sci., 2016, 8(7), 1475.
[95]
Zhang, Y.; Lv, J.; Liu, H.; Zhu, J.; Su, J.; Wu, Q.; Qi, Y.; Wang, F.; Li, X. HHMD: the human histone modification database. Nucleic Acids Res., 2010, 38(Database issue)(Suppl. 1), D149-D154.
[http://dx.doi.org/10.1093/nar/gkp968] [PMID: 19892823]
[96]
Latchman, Y.; Wood, C.R.; Chernova, T.; Chaudhary, D.; Borde, M.; Chernova, I.; Iwai, Y.; Long, A.J.; Brown, J.A.; Nunes, R.; Greenfield, E.A.; Bourque, K.; Boussiotis, V.A.; Carter, L.L.; Carreno, B.M.; Malenkovich, N.; Nishimura, H.; Okazaki, T.; Honjo, T.; Sharpe, A.H.; Freeman, G.J. PD-L2 is a second ligand for PD-1 and inhibits T cell activation. Nat. Immunol., 2001, 2(3), 261-268.
[http://dx.doi.org/10.1038/85330] [PMID: 11224527]
[97]
Fabregat, A.; Jupe, S.; Matthews, L.; Sidiropoulos, K.; Gillespie, M.; Garapati, P.; Haw, R.; Jassal, B.; Korninger, F.; May, B.; Milacic, M.; Roca, C.D.; Rothfels, K.; Sevilla, C.; Shamovsky, V.; Shorser, S.; Varusai, T.; Viteri, G.; Weiser, J.; Wu, G.; Stein, L.; Hermjakob, H.; D’Eustachio, P. The reactome pathway knowledgebase. Nucleic Acids Res., 2018, 46(D1), D649-D655.
[http://dx.doi.org/10.1093/nar/gkx1132] [PMID: 29145629]
[98]
Thomas, P.D.; Campbell, M.J.; Kejariwal, A.; Mi, H.; Karlak, B.; Daverman, R.; Diemer, K.; Muruganujan, A.; Narechania, A. PANTHER: A library of protein families and subfamilies indexed by function. Genome Res., 2003, 13(9), 2129-2141.
[http://dx.doi.org/10.1101/gr.772403]
[99]
The gene ontology resource: 20 years and still Going strong. Nucleic Acids Res., 2019, 8(47), 330-338.
[100]
Hu, Y.; Peng, T.; Gao, L.; Tan, K. CytoTalk: De novo construction of signal transduction networks using single-cell transcriptomic data. Sci. Adv., 2021, 7(16), eabf1356.
[http://dx.doi.org/10.1126/sciadv.abf1356] [PMID: 33853780]
[101]
Karchin, R.; Ochs, M.F.; Stuart, J.M.; Bader, J.S. Identification of aberrant pathway and network activity from high-throughput data. Biocomputing, 2013, 2013, 103-110.
[http://dx.doi.org/10.1142/9789814447973_0011]
[102]
Ben-Hamo, R.; Efroni, S. Network as biomarker. Syst. Biomed., 2013, 1(1), 35-41.
[http://dx.doi.org/10.4161/sysb.26474]
[103]
Subramanian, A.; Kuehn, H.; Gould, J.; Tamayo, P.; Mesirov, J.P. GSEA-P: A desktop application for gene set enrichment analysis. Bioinformatics, 2007, 23(23), 3251-3253.
[http://dx.doi.org/10.1093/bioinformatics/btm369]
[104]
Krämer, A.; Green, J.; Pollard, J., Jr; Tugendreich, S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics, 2014, 30(4), 523-530.
[http://dx.doi.org/10.1093/bioinformatics/btt703] [PMID: 24336805]
[105]
Taylor, B.S.; Schultz, N.; Hieronymus, H.; Gopalan, A.; Xiao, Y.; Carver, B.S.; Arora, V.K.; Kaushik, P.; Cerami, E.; Reva, B.; Antipin, Y.; Mitsiades, N.; Landers, T.; Dolgalev, I.; Major, J.E.; Wilson, M.; Socci, N.D.; Lash, A.E.; Heguy, A.; Eastham, J.A.; Scher, H.I.; Reuter, V.E.; Scardino, P.T.; Sander, C.; Sawyers, C.L.; Gerald, W.L. Integrative genomic profiling of human prostate cancer. Cancer Cell, 2010, 18(1), 11-22.
[http://dx.doi.org/10.1016/j.ccr.2010.05.026] [PMID: 20579941]
[106]
Greenblum, S.I.; Efroni, S.; Schaefer, C.F.; Buetow, K.H. The PathOlogist: an automated tool for pathway-centric analysis. BMC Bioinformatics, 2011, 12(1), 133.
[http://dx.doi.org/10.1186/1471-2105-12-133] [PMID: 21542931]
[107]
Komurov, K.; Dursun, S.; Erdin, S.; Ram, P.T. NetWalker: A contextual network analysis tool for functional genomics. BMC Genomics, 2012, 13(1), 282.
[http://dx.doi.org/10.1186/1471-2164-13-282] [PMID: 22732065]
[108]
Spasić I.; Dunn, W.B.; Velarde, G.; Tseng, A.; Jenkins, H.; Hardy, N.; Oliver, S.G.; Kell, D.B. MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics. BMC Bioinformatics, 2006, 7(1), 281.
[http://dx.doi.org/10.1186/1471-2105-7-281] [PMID: 16753052]
[109]
Smoot, M.E.; Ono, K.; Ruscheinski, J.; Wang, P.L.; Ideker, T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics, 2011, 27(3), 431-432.
[http://dx.doi.org/10.1093/bioinformatics/btq675] [PMID: 21149340]
[110]
Cerami, E.G.; Gross, B.E.; Demir, E.; Rodchenkov, I.; Babur, Ö.; Anwar, N.; Schultz, N.; Bader, G.D.; Sander, C. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res., 2010, 10(39), 685-690.
[PMID: 21071392]
[111]
Anurag, M.; Jaehnig, E.J.; Krug, K.; Lei, J.T.; Bergstrom, E.J.; Kim, B.J.; Vashist, T.D.; Huynh, A.M.T.; Dou, Y.; Gou, X.; Huang, C.; Shi, Z.; Wen, B.; Korchina, V.; Gibbs, R.A.; Muzny, D.M.; Doddapaneni, H.; Dobrolecki, L.E.; Rodriguez, H.; Robles, A.I.; Hiltke, T.; Lewis, M.T.; Nangia, J.R.; Nemati Shafaee, M.; Li, S.; Hagemann, I.S.; Hoog, J.; Lim, B.; Osborne, C.K.; Mani, D.R.; Gillette, M.A.; Zhang, B.; Echeverria, G.V.; Miles, G.; Rimawi, M.F.; Carr, S.A.; Ademuyiwa, F.O.; Satpathy, S.; Ellis, M.J. Proteogenomic markers of chemotherapy resistance and response in triple-negative breast cancer. Cancer Discov., 2022, 12(11), 2586-2605.
[http://dx.doi.org/10.1158/2159-8290.CD-22-0200] [PMID: 36001024]
[112]
Farberov, L.; Ionescu, A.; Zoabi, Y.; Shapira, G.; Ibraheem, A.; Azan, Y.; Perlson, E.; Shomron, N. Multiple copies of microRNA binding sites in long 3′ utr variants regulate axonal translation. Cells, 2023, 12(2), 233.
[http://dx.doi.org/10.3390/cells12020233] [PMID: 36672174]
[113]
Su, S.; Zou, J.J.; Zeng, Y.Y.; Cen, W.C.; Zhou, W.; Liu, Y.; Su, D.H.; Zhang, X.L.; Huang, H.Y.; Lei, A.; Huang, Z.H.; Jin, Y.; Li, L.; Su, N.; Xie, Y.L.; Zhao, Z.G.; Liu, J.X. Tumor mutational burden and genomic alterations in chinese small cell lung cancer measured by whole-exome sequencing. BioMed Res. Int., 2019, 2019, 1-8.
[http://dx.doi.org/10.1155/2019/6096350] [PMID: 31781628]
[114]
Perez-Riverol, Y.; Zorin, A.; Dass, G.; Vu, M.T.; Xu, P.; Glont, M.; Vizcaíno, J.A.; Jarnuczak, A.F.; Petryszak, R.; Ping, P.; Hermjakob, H. Quantifying the impact of public omics data. Nat. Commun., 2019, 10(1), 3512.
[http://dx.doi.org/10.1038/s41467-019-11461-w] [PMID: 31383865]
[115]
Zhang, B.; Wang, J.; Wang, X.; Zhu, J.; Liu, Q.; Shi, Z.; Chambers, M.C.; Zimmerman, L.J.; Shaddox, K.F.; Kim, S.; Davies, S.R.; Wang, S.; Wang, P.; Kinsinger, C.R.; Rivers, R.C.; Rodriguez, H.; Townsend, R.R.; Ellis, M.J.C.; Carr, S.A.; Tabb, D.L.; Coffey, R.J.; Slebos, R.J.C.; Liebler, D.C. Proteogenomic characterization of human colon and rectal cancer. Nature, 2014, 513(7518), 382-387.
[http://dx.doi.org/10.1038/nature13438] [PMID: 25043054]
[116]
Mo, Q.; Wang, S.; Seshan, V.E.; Olshen, A.B.; Schultz, N.; Sander, C.; Powers, R.S.; Ladanyi, M.; Shen, R. Pattern discovery and cancer gene identification in integrated cancer genomic data. Proc. Natl. Acad. Sci. USA, 2013, 110(11), 4245-4250.
[http://dx.doi.org/10.1073/pnas.1208949110] [PMID: 23431203]
[117]
Heo, Y.J.; Hwa, C.; Lee, G.H.; Park, J.M.; An, J.Y. Integrative multi-omics approaches in cancer research: From biological networks to clinical subtypes. Mol. Cells, 2021, 44(7), 433-443.
[http://dx.doi.org/10.14348/molcells.2021.0042] [PMID: 34238766]
[118]
Mason, S.A.; Sayyid, F.; Kirk, P.D.W.; Starr, C.; Wild, D.L. MDI-GPU: accelerating integrative modelling for genomic-scale data using GP-GPU computing. Stat. Appl. Genet. Mol. Biol., 2016, 15(1), 83-86.
[http://dx.doi.org/10.1515/sagmb-2015-0055] [PMID: 26910751]
[119]
Mancuso, N.; Shi, H.; Goddard, P.; Kichaev, G.; Gusev, A.; Pasaniuc, B. Integrating gene expression with summary association statistics to identify genes associated with 30 complex traits. Am. J. Hum. Genet., 2017, 100(3), 473-487.
[http://dx.doi.org/10.1016/j.ajhg.2017.01.031] [PMID: 28238358]
[120]
Singh, A.; Shannon, C.P.; Gautier, B.; Rohart, F.; Vacher, M.; Tebbutt, S.J.; Lê Cao, K.A. DIABLO: An integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics, 2019, 35(17), 3055-3062.
[http://dx.doi.org/10.1093/bioinformatics/bty1054] [PMID: 30657866]
[121]
Rohart, F.; Gautier, B.; Singh, A.; Lê Cao, K.A. mixOmics: An R package for ‘omics feature selection and multiple data integration. PLOS Comput. Biol., 2017, 13(11), e1005752.
[http://dx.doi.org/10.1371/journal.pcbi.1005752] [PMID: 29099853]
[122]
Meng, C.; Zeleznik, O.A.; Thallinger, G.G.; Kuster, B.; Gholami, A.M.; Culhane, A.C. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief. Bioinform., 2016, 17(4), 628-641.
[http://dx.doi.org/10.1093/bib/bbv108] [PMID: 26969681]
[123]
Meng, C.; Kuster, B.; Culhane, A.C.; Gholami, A.M. A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics, 2014, 15(1), 162.
[http://dx.doi.org/10.1186/1471-2105-15-162] [PMID: 24884486]
[124]
Pathak, G.A.; Singh, K.; Wendt, F.R.; Fleming, T.W.; Overstreet, C.; Koller, D.; Tylee, D.S.; De Angelis, F.; Cabrera Mendoza, B.; Levey, D.F.; Koenen, K.C.; Krystal, J.H.; Pietrzak, R.H.; O’ Donell, C.; Gaziano, J.M.; Falcone, G.; Stein, M.B.; Gelernter, J.; Pasaniuc, B.; Mancuso, N.; Davis, L.K.; Polimanti, R. Genetically regulated multi-omics study for symptom clusters of posttraumatic stress disorder highlights pleiotropy with hematologic and cardio-metabolic traits. Mol. Psychiatry, 2022, 27(3), 1394-1404.
[http://dx.doi.org/10.1038/s41380-022-01488-9] [PMID: 35241783]
[125]
Bernardes, J.P.; Mishra, N.; Tran, F.; Bahmer, T.; Best, L.; Blase, J.I.; Bordoni, D.; Franzenburg, J.; Geisen, U.; Josephs-Spaulding, J.; Köhler, P.; Künstner, A.; Rosati, E.; Aschenbrenner, A.C.; Bacher, P.; Baran, N.; Boysen, T.; Brandt, B.; Bruse, N.; Dörr, J.; Dräger, A.; Elke, G.; Ellinghaus, D.; Fischer, J.; Forster, M.; Franke, A.; Franzenburg, S.; Frey, N.; Friedrichs, A.; Fuß, J.; Glück, A.; Hamm, J.; Hinrichsen, F.; Hoeppner, M.P.; Imm, S.; Junker, R.; Kaiser, S.; Kan, Y.H.; Knoll, R.; Lange, C.; Laue, G.; Lier, C.; Lindner, M.; Marinos, G.; Markewitz, R.; Nattermann, J.; Noth, R.; Pickkers, P.; Rabe, K.F.; Renz, A.; Röcken, C.; Rupp, J.; Schaffarzyk, A.; Scheffold, A.; Schulte-Schrepping, J.; Schunk, D.; Skowasch, D.; Ulas, T.; Wandinger, K.P.; Wittig, M.; Zimmermann, J.; Busch, H.; Hoyer, B.F.; Kaleta, C.; Heyckendorf, J.; Kox, M.; Rybniker, J.; Schreiber, S.; Schultze, J.L.; Rosenstiel, P.; Banovich, N.E.; Desai, T.; Eickelberg, O.; Haniffa, M.; Horvath, P.; Kropski, J.A.; Lafyatis, R.; Lundeberg, J.; Meyer, K.; Nawijn, M.C.; Nikolic, M.; Ordovas Montanes, J.; Pe’er, D.; Tata, P.R.; Rawlins, E.; Regev, A.; Reyfman, P.; Samakovlis, C.; Schultze, J.; Shalek, A.; Shepherd, D.; Spence, J.; Teichmann, S.; Theis, F.; Tsankov, A.; van den Berge, M.; von Papen, M.; Whitsett, J.; Zaragosi, L.E.; Angelov, A.; Bals, R.; Bartholomäus, A.; Becker, A.; Bezdan, D.; Bonifacio, E.; Bork, P.; Clavel, T.; Colme-Tatche, M.; Diefenbach, A.; Dilthey, A.; Fischer, N.; Förstner, K.; Frick, J-S.; Gagneur, J.; Goesmann, A.; Hain, T.; Hummel, M.; Janssen, S.; Kalinowski, J.; Kallies, R.; Kehr, B.; Keller, A.; Kim-Hellmuth, S.; Klein, C.; Kohlbacher, O.; Korbel, J.O.; Kurth, I.; Landthaler, M.; Li, Y.; Ludwig, K.; Makarewicz, O.; Marz, M.; McHardy, A.; Mertes, C.; Nöthen, M.; Nürnberg, P.; Ohler, U.; Ossowski, S.; Overmann, J.; Peter, S.; Pfeffer, K.; Poetsch, A.R.; Pühler, A.; Rajewsky, N.; Ralser, M.; Rieß, O.; Ripke, S.; Nunes da Rocha, U.; Rosenstiel, P.; Saliba, A-E.; Sander, L.E.; Sawitzki, B.; Schiffer, P.; Schulte, E-C.; Schultze, J.L.; Sczyrba, A.; Stegle, O.; Stoye, J.; Theis, F.; Vehreschild, J.; Vogel, J.; von Kleist, M.; Walker, A.; Walter, J.; Wieczorek, D.; Ziebuhr, J. Longitudinal multi-omics analyses identify responses of megakaryocytes, erythroid cells, and plasmablasts as hallmarks of severe COVID-19. Immunity, 2020, 53(6), 1296-1314.e9.
[http://dx.doi.org/10.1016/j.immuni.2020.11.017] [PMID: 33296687]
[126]
Abdi, H.; Williams, L.J.; Valentin, D. Multiple factor analysis: principal component analysis for multitable and multiblock data sets. Wiley Interdiscip. Rev. Comput. Stat., 2013, 5(2), 149-179.
[http://dx.doi.org/10.1002/wics.1246]
[127]
Cantini, L.; Zakeri, P.; Hernandez, C.; Naldi, A.; Thieffry, D.; Remy, E.; Baudot, A. Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nat. Commun., 2021, 12(1), 124.
[http://dx.doi.org/10.1038/s41467-020-20430-7] [PMID: 33402734]
[128]
Dimitrakopoulos, C.; Hindupur, S.K.; Häfliger, L.; Behr, J.; Montazeri, H.; Hall, M.N.; Beerenwinkel, N. Network-based integration of multi-omics data for prioritizing cancer genes. Bioinformatics, 2018, 34(14), 2441-2448.
[http://dx.doi.org/10.1093/bioinformatics/bty148] [PMID: 29547932]
[129]
Huang, S.; Chaudhary, K.; Garmire, L.X. More is better: recent progress in multi-omics data integration methods. Front. Genet., 2017, 8, 84.
[http://dx.doi.org/10.3389/fgene.2017.00084] [PMID: 28670325]
[130]
Scala, G.; Kinaret, P.; Marwah, V.; Sund, J.; Fortino, V.; Greco, D. Multi-omics analysis of ten carbon nanomaterials effects highlights cell type specific patterns of molecular regulation and adaptation. NanoImpact, 2018, 11, 99-108.
[http://dx.doi.org/10.1016/j.impact.2018.05.003] [PMID: 32140619]
[131]
Lock, E.F.; Hoadley, K.A.; Marron, J.S.; Nobel, A.B. Joint and individual variation explained (JIVE) for integrated analysis of multiple data types. Ann. Appl. Stat., 2013, 7(1), 523-542.
[http://dx.doi.org/10.1214/12-AOAS597] [PMID: 23745156]
[132]
Ray, P.; Zheng, L.; Lucas, J.; Carin, L. Bayesian joint analysis of heterogeneous genomics data. Bioinformatics, 2014, 30(10), 1370-1376.
[http://dx.doi.org/10.1093/bioinformatics/btu064] [PMID: 24489367]
[133]
Lahti, L.; Schäfer, M.; Klein, H.U.; Bicciato, S.; Dugas, M. Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review. Brief. Bioinform., 2013, 14(1), 27-35.
[http://dx.doi.org/10.1093/bib/bbs005] [PMID: 22441573]
[134]
Li, W.; Zhang, S.; Liu, C.C.; Zhou, X.J. Identifying multi-layer gene regulatory modules from multi-dimensional genomic data. Bioinformatics, 2012, 28(19), 2458-2466.
[http://dx.doi.org/10.1093/bioinformatics/bts476] [PMID: 22863767]
[135]
Subramanian, I.; Verma, S.; Kumar, S.; Jere, A.; Anamika, K. Multi-omics data integration, interpretation, and its application. Bioinform. Biol. Insights, 2020, 14.
[http://dx.doi.org/10.1177/1177932219899051] [PMID: 32076369]
[136]
Lapointe, C.P.; Stefely, J.A.; Jochem, A.; Hutchins, P.D.; Wilson, G.M.; Kwiecien, N.W.; Coon, J.J.; Wickens, M.; Pagliarini, D.J. Multi-omics reveal specific targets of the RNA-binding protein Puf3p and its orchestration of mitochondrial biogenesis. Cell Syst., 2018, 6(1), 125-135.e6.
[http://dx.doi.org/10.1016/j.cels.2017.11.012] [PMID: 29248374]
[137]
Hu, X.; Wang, Z.; Wang, Q.; Chen, K.; Han, Q.; Bai, S.; Du, J.; Chen, W. Molecular classification reveals the diverse genetic and prognostic features of gastric cancer: A multi-omics consensus ensemble clustering. Biomed. Pharmacother., 2021, 144, 112222.
[138]
Mo, F.; Lin, D.; Takhar, M.; Ramnarine, V.R.; Dong, X.; Bell, R.H.; Volik, S.V.; Wang, K.; Xue, H.; Wang, Y.; Haegert, A.; Anderson, S.; Brahmbhatt, S.; Erho, N.; Wang, X.; Gout, P.W.; Morris, J.; Karnes, R.J.; Den, R.B.; Klein, E.A.; Schaeffer, E.M.; Ross, A.; Ren, S.; Sahinalp, S.C.; Li, Y.; Xu, X.; Wang, J.; Wang, J.; Gleave, M.E.; Davicioni, E.; Sun, Y.; Wang, Y.; Collins, C.C. Stromal gene expression is predictive for metastatic primary prostate cancer. Eur. Urol., 2018, 73(4), 524-532.
[http://dx.doi.org/10.1016/j.eururo.2017.02.038] [PMID: 28330676]
[139]
Nguyen, H.; Shrestha, S.; Draghici, S.; Nguyen, T. PINSPlus: A tool for tumor subtype discovery in integrated genomic data. Bioinformatics, 2019, 35(16), 2843-2846.
[http://dx.doi.org/10.1093/bioinformatics/bty1049] [PMID: 30590381]

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