Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Drug Design and Disease Diagnosis: The Potential of Deep Learning Models in Biology

Author(s): Sarojini Sreeraman, Mayuri P. Kannan, Raja Babu Singh Kushwah, Vickram Sundaram*, Alaguraj Veluchamy, Anand Thirunavukarasou* and Konda Mani Saravanan*

Volume 18, Issue 3, 2023

Published on: 17 March, 2023

Page: [208 - 220] Pages: 13

DOI: 10.2174/1574893618666230227105703

Price: $65

Abstract

Early prediction and detection enable reduced transmission of human diseases and provide healthcare professionals ample time to make subsequent diagnoses and treatment strategies. This, in turn, aids in saving more lives and results in lower medical costs. Designing small chemical molecules to treat fatal disorders is also urgently needed to address the high death rate of these diseases worldwide. A recent analysis of published literature suggested that deep learning (DL) based models apply more potential algorithms to hybrid databases of chemical data. Considering the above, we first discussed the concept of DL architectures and their applications in drug development and diagnostics in this review. Although DL-based approaches have applications in several fields, in the following sections of the article, we focus on recent developments of DL-based techniques in biology, notably in structure prediction, cancer drug development, COVID infection diagnostics, and drug repurposing strategies. Each review section summarizes several cutting-edge, recently developed DL-based techniques. Additionally, we introduced the approaches presented in our group, whose prediction accuracy is relatively comparable with current computational models. We concluded the review by discussing the benefits and drawbacks of DL techniques and outlining the future paths for data collecting and developing efficient computational models.

« Previous Next »
Graphical Abstract

[1]
Zhao Q, Yu H, Ji M, Zhao Y, Chen X. Computational model development of drug-target interaction prediction: A review. Curr Protein Pept Sci 2019; 20(6): 492-4.
[http://dx.doi.org/10.2174/1389203720666190123164310] [PMID: 30674253]
[2]
Ekins S. The next era: Deep learning in pharmaceutical research. Pharm Res 2016; 33(11): 2594-603.
[http://dx.doi.org/10.1007/s11095-016-2029-7] [PMID: 27599991]
[3]
Li TH, Wang CC, Zhang L, Chen X. SNRMPACDC: Computational model focused on Siamese network and random matrix projection for anticancer synergistic drug combination prediction. Brief Bioinform 2023; 24(1): bbac503.
[http://dx.doi.org/10.1093/bib/bbac503] [PMID: 36418927]
[4]
Zhao Z, Bourne PE. Harnessing systematic protein–ligand interaction fingerprints for drug discovery. Drug Discov Today 2022; 27(10): 103319.
[http://dx.doi.org/10.1016/j.drudis.2022.07.004] [PMID: 35850431]
[5]
Meng FR, You ZH, Chen X, Zhou Y, An JY. Prediction of drug–target interaction networks from the integration of protein sequences and drug chemical structures. Molecules 2017; 22(7): 1119.
[http://dx.doi.org/10.3390/molecules22071119] [PMID: 28678206]
[6]
Selvaraj C, Chandra I, Singh SK. Artificial intelligence and machine learning approaches for drug design: Challenges and opportunities for the pharmaceutical industries. Mol Divers 2022; 26(3): 1893-913.
[http://dx.doi.org/10.1007/s11030-021-10326-z] [PMID: 34686947]
[7]
Blunt NS, Camps J, Crawford O, et al. Perspective on the current state-of-the-art of quantum computing for drug discovery applications. J Chem Theory Comput 2022; 18(12): 7001-23.
[http://dx.doi.org/10.1021/acs.jctc.2c00574] [PMID: 36355616]
[8]
Sahlgren C, Meinander A, Zhang H, et al. Tailored approaches in drug development and diagnostics: From molecular design to biological model systems. Adv Healthc Mater 2017; 6(21): 1700258.
[http://dx.doi.org/10.1002/adhm.201700258] [PMID: 28892296]
[9]
Feng Y, Cheng X, Wu S, Mani Saravanan K, Liu W. Hybrid drug-screening strategy identifies potential SARS-COV-2 cell-entry inhibitors targeting human transmembrane serine protease. Struct Chem 2022; 33(5): 1503-15.
[http://dx.doi.org/10.1007/s11224-022-01960-w] [PMID: 35571866]
[10]
Zhang H, Gong X, Peng Y, et al. An efficient modern strategy to screen drug candidates targeting rdrp of SARS-COV-2 with potentially high selectivity and specificity. Front Chem 2022; 10: 933102.
[http://dx.doi.org/10.3389/fchem.2022.933102] [PMID: 35903186]
[11]
Weimer D, Scholz-Reiter B, Shpitalni M. Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Ann 2016; 65(1): 417-20.
[http://dx.doi.org/10.1016/j.cirp.2016.04.072]
[12]
Cao C, Liu F, Tan H, et al. Deep learning and its applications in biomedicine. Genom Proteom Bioinformat 2018; 16(1): 17-32.
[http://dx.doi.org/10.1016/j.gpb.2017.07.003] [PMID: 29522900]
[13]
Mamoshina P, Vieira A, Putin E, Zhavoronkov A. Applications of deep learning in biomedicine. Mol Pharm 2016; 13(5): 1445-54.
[http://dx.doi.org/10.1021/acs.molpharmaceut.5b00982] [PMID: 27007977]
[14]
Min S, Lee B, Yoon S. Deep learning in bioinformatics. Brief Bioinform 2017; 18(5): 851-69.
[PMID: 27473064]
[15]
Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: Review, opportunities and challenges. Brief Bioinform 2018; 19(6): 1236-46.
[http://dx.doi.org/10.1093/bib/bbx044] [PMID: 28481991]
[16]
Zhang H, Zhang T, Saravanan KM, et al. DeepBindBC: A practical deep learning method for identifying native-like protein-ligand complexes in virtual screening. Methods 2022; 205: 247-62.
[http://dx.doi.org/10.1016/j.ymeth.2022.07.009] [PMID: 35878751]
[17]
Jing Y, Bian Y, Hu Z, Wang L, Xie XQS. 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]
[18]
Li Y, Huang C, Ding L, Li Z, Pan Y, Gao X. Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. Methods 2019; 166: 4-21.
[http://dx.doi.org/10.1016/j.ymeth.2019.04.008] [PMID: 31022451]
[19]
Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery. Mol Divers 2021; 25(3): 1315-60.
[http://dx.doi.org/10.1007/s11030-021-10217-3] [PMID: 33844136]
[20]
Bian Y, Xie XQ. Generative chemistry: Drug discovery with deep learning generative models. J Mol Model 2021; 27(3): 71.
[http://dx.doi.org/10.1007/s00894-021-04674-8] [PMID: 33543405]
[21]
Hudson IL. Data integration using advances in machine learning in drug discovery and molecular biology bt - artificial neural networks. Springer US. New York, NY 2021; pp. 167-84.
[22]
Terranova N, Venkatakrishnan K, Benincosa LJ. Application of machine learning in translational medicine: Current status and future opportunities. AAPS J 2021; 23(4): 74.
[http://dx.doi.org/10.1208/s12248-021-00593-x] [PMID: 34008139]
[23]
Elbadawi M, Gaisford S, Basit AW. Advanced machine-learning techniques in drug discovery. Drug Discov Today 2021; 26(3): 769-77.
[http://dx.doi.org/10.1016/j.drudis.2020.12.003] [PMID: 33290820]
[24]
Bagherian M, Sabeti E, Wang K, Sartor MA, Nikolovska-Coleska Z, Najarian K. Machine learning approaches and databases for prediction of drug–target interaction: A survey paper. Brief Bioinform 2021; 22(1): 247-69.
[http://dx.doi.org/10.1093/bib/bbz157] [PMID: 31950972]
[25]
Wang J, Zhu H, Wang SH, Zhang YD. A review of deep learning on medical image analysis. Mob Netw Appl 2021; 26(1): 351-80.
[http://dx.doi.org/10.1007/s11036-020-01672-7]
[26]
Sarvamangala DR, Kulkarni RV. Convolutional neural networks in medical image understanding: A survey. Evol Intell 2022; 15(1): 1-22.
[http://dx.doi.org/10.1007/s12065-020-00540-3] [PMID: 33425040]
[27]
Lu J, Tan L, Jiang H. Review on convolutional neural network (CNN) applied to plant leaf disease classification. Agric 2021; 11: 707.
[28]
Goenka N, Tiwari S. Deep learning for Alzheimer prediction using brain biomarkers. Artif Intell Rev 2021; 54(7): 4827-71.
[http://dx.doi.org/10.1007/s10462-021-10016-0]
[29]
AlSaeed D, Omar SF. Brain MRI analysis for Alzheimer’s disease diagnosis using cnn-based feature extraction and machine learning. Sensors 2022; 22(8): 2911.
[http://dx.doi.org/10.3390/s22082911] [PMID: 35458896]
[30]
Chen L, Lu Y, Pei R, et al. Deep learning in molecular biology marker recognition of patients with acute myeloid leukemia. J Supercomput 2022; 78(9): 11283-97.
[http://dx.doi.org/10.1007/s11227-021-04104-9]
[31]
Zeng N, Li H, Peng Y. A new deep belief network-based multi-task learning for diagnosis of Alzheimer’s disease. Neural Comput Appl 2021.
[http://dx.doi.org/10.1007/s00521-021-06149-6]
[32]
Ackerson J, Dave R, Seliya N. Applications of recurrent neural network for biometric authentication & anomaly detection. Information 2021; 12(7): 272.
[http://dx.doi.org/10.3390/info12070272]
[33]
Cossu A, Carta A, Lomonaco V, Bacciu D. Continual learning for recurrent neural networks: An empirical evaluation. Neural Netw 2021; 143: 607-27.
[http://dx.doi.org/10.1016/j.neunet.2021.07.021] [PMID: 34343775]
[34]
Huang R, Wei C, Wang B, et al. Well performance prediction based on Long Short-Term Memory (LSTM) neural network. J Petrol Sci Eng 2022; 208: 109686.
[http://dx.doi.org/10.1016/j.petrol.2021.109686]
[35]
Wunsch A, Liesch T, Broda S. Groundwater level forecasting with artificial neural networks: A comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX). Hydrol Earth Syst Sci 2021; 25(3): 1671-87.
[http://dx.doi.org/10.5194/hess-25-1671-2021]
[36]
Srinidhi CL, Ciga O, Martel AL. Deep neural network models for computational histopathology: A survey. Med Image Anal 2021; 67: 101813.
[http://dx.doi.org/10.1016/j.media.2020.101813] [PMID: 33049577]
[37]
Masih N, Naz H, Ahuja S. Multilayer perceptron based deep neural network for early detection of coronary heart disease. Health Technol 2021; 11(1): 127-38.
[http://dx.doi.org/10.1007/s12553-020-00509-3]
[38]
Azizimazreah A, Chen L. Polymorphic accelerators for deep neural networks. IEEE Trans Comput 2022; 71(3): 534-46.
[http://dx.doi.org/10.1109/TC.2020.3048624]
[39]
Akanksha E, Sharma N, Gulati K. OPNN: Optimized probabilistic neural network based automatic detection of maize plant disease detection. Proceedings of the 2021 6th International Conference on Inventive Computation Technologies (ICICT) 2021; 1(1): 1322-8.
[http://dx.doi.org/10.1109/ICICT50816.2021.9358763]
[40]
Selvaraj S, Saravanan KM. Better theoretical models and protein design experiments can help to understand protein folding. J Nat Sci Biol Med 2015; 6(1): 202-4.
[http://dx.doi.org/10.4103/0976-9668.149122] [PMID: 25810661]
[41]
Jisna VA, Jayaraj PB. Protein structure prediction: Conventional and deep learning perspectives. Protein J 2021; 40(4): 522-44.
[http://dx.doi.org/10.1007/s10930-021-10003-y] [PMID: 34050498]
[42]
Kuhlman B, Bradley P. Advances in protein structure prediction and design. Nat Rev Mol Cell Biol 2019; 20(11): 681-97.
[http://dx.doi.org/10.1038/s41580-019-0163-x] [PMID: 31417196]
[43]
Petrey D, Honig B. Protein structure prediction: Inroads to biology. Mol Cell 2005; 20(6): 811-9.
[http://dx.doi.org/10.1016/j.molcel.2005.12.005] [PMID: 16364908]
[44]
Bongirwar V, Mokhade AS. Different methods, techniques and their limitations in protein structure prediction: A review. Prog Biophys Mol Biol 2022; 173: 72-82.
[http://dx.doi.org/10.1016/j.pbiomolbio.2022.05.002] [PMID: 35588858]
[45]
Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021; 596(7873): 583-9.
[http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]
[46]
Zhang H, Hao M, Wu H, et al. Protein residue contact prediction based on deep learning and massive statistical features from multi-sequence alignment. Tsinghua Sci Technol 2022; 27(5): 843-54.
[http://dx.doi.org/10.26599/TST.2021.9010064]
[47]
Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A. Critical assessment of methods of protein structure prediction: Progress and new directions in round XI. Proteins 2016; 84(S1): 4-14.
[http://dx.doi.org/10.1002/prot.25064] [PMID: 27171127]
[48]
Zhang H, Bei Z, Xi W, et al. Evaluation of residue-residue contact prediction methods: From retrospective to prospective. PLOS Comput Biol 2021; 17(5): e1009027.
[http://dx.doi.org/10.1371/journal.pcbi.1009027] [PMID: 34029314]
[49]
Davariashtiyani A, Kadkhodaie Z, Kadkhodaei S. Predicting synthesizability of crystalline materials via deep learning. Communic Mater 2021; 2(1): 115.
[http://dx.doi.org/10.1038/s43246-021-00219-x]
[50]
Qin T, Zhu Z, Wang XS, Xia J, Wu S. Computational representations of protein–ligand interfaces for structure-based virtual screening. Expert Opin Drug Discov 2021; 16(10): 1175-92.
[http://dx.doi.org/10.1080/17460441.2021.1929921] [PMID: 34011222]
[51]
Zhang H, Saravanan KM, Yang Y, Wei Y, Yi P, Zhang JZH. Generating and screening de novo compounds against given targets using ultrafast deep learning models as core components. Brief Bioinform 2022; 23(4): bbac226.
[http://dx.doi.org/10.1093/bib/bbac226] [PMID: 35724626]
[52]
Zhang H, Liao L, Saravanan KM, Yin P, Wei Y. DeepBindRG: A deep learning based method for estimating effective protein–ligand affinity. PeerJ 2019; 7: e7362.
[http://dx.doi.org/10.7717/peerj.7362] [PMID: 31380152]
[53]
Zhang H, Saravanan KM, Lin J, et al. DeepBindPoc: A deep learning method to rank ligand binding pockets using molecular vector representation. PeerJ 2020; 8: e8864.
[http://dx.doi.org/10.7717/peerj.8864] [PMID: 32292649]
[54]
Zhang H, Zhang T, Saravanan KM, et al. A novel virtual drug screening pipeline with deep-leaning as core component identifies inhibitor of pancreatic alpha-amylase. Proceedings of the 2021 IEEE International Conference on bioinformatics and biomedicine (BIBM). 1(1): 104-1.
[http://dx.doi.org/10.1109/BIBM52615.2021.9669306]
[55]
Jones DT, Kandathil SM. High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features. Bioinformatics 2018; 34(19): 3308-15.
[http://dx.doi.org/10.1093/bioinformatics/bty341] [PMID: 29718112]
[56]
Michel M, Menéndez Hurtado D, Elofsson A. PconsC4: Fast, accurate and hassle-free contact predictions. Bioinformatics 2019; 35(15): 2677-9.
[http://dx.doi.org/10.1093/bioinformatics/bty1036] [PMID: 30590407]
[57]
Källberg M, Wang H, Wang S, et al. Template-based protein structure modeling using the RaptorX web server. Nat Protoc 2012; 7(8): 1511-22.
[http://dx.doi.org/10.1038/nprot.2012.085] [PMID: 22814390]
[58]
Liu L, Yang S, Liu Y, et al. DeepContact: High-throughput quantification of membrane contact sites based on electron microscopy imaging. J Cell Biol 2022; 221(9): e202106190.
[http://dx.doi.org/10.1083/jcb.202106190] [PMID: 35929833]
[59]
Ding W, Mao W, Shao D, Zhang W, Gong H. DeepConPred2: An improved method for the prediction of protein residue contacts. Comput Struct Biotechnol J 2018; 16: 503-10.
[http://dx.doi.org/10.1016/j.csbj.2018.10.009] [PMID: 30505403]
[60]
Adhikari B, Hou J, Cheng J. DNCON2: Improved protein contact prediction using two-level deep convolutional neural networks. Bioinformatics 2018; 34(9): 1466-72.
[http://dx.doi.org/10.1093/bioinformatics/btx781] [PMID: 29228185]
[61]
Ji S, Oruç T, Mead L, et al. DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure. PLoS One 2019; 14(1): e0205214.
[http://dx.doi.org/10.1371/journal.pone.0205214] [PMID: 30620738]
[62]
Wu Q, Peng Z, Anishchenko I, Cong Q, Baker D, Yang J. Protein contact prediction using metagenome sequence data and residual neural networks. Bioinformatics 2020; 36(1): 41-8.
[http://dx.doi.org/10.1093/bioinformatics/btz477] [PMID: 31173061]
[63]
Rives A, Meier J, Sercu T, et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc Natl Acad Sci 2021; 118(15): e2016239118.
[http://dx.doi.org/10.1073/pnas.2016239118] [PMID: 33876751]
[64]
Wuyun Q, Zheng W, Peng Z, Yang J. A large-scale comparative assessment of methods for residue-residue contact prediction. Brief Bioinform 2018; 19(2): 219-30.
[PMID: 27802931]
[65]
Zhang H, Saravanan KM, Yang Y, et al. Deep learning based drug screening for novel coronavirus 2019-nCov. Interdiscip Sci 2020; 12(3): 368-76.
[http://dx.doi.org/10.1007/s12539-020-00376-6] [PMID: 32488835]
[66]
Saravanan KM, Zhang H, Hossain MT, Reza MS, Wei Y. Deep learning-based drug screening for COVID-19 and case studies BT - In silico modeling of drugs against coronaviruses: Computational tools and protocols. Springer US. New York, NY 2021; pp. 631-60.
[67]
Pushpakom S, Iorio F, Eyers PA, et al. Drug repurposing: Progress, challenges and recommendations. Nat Rev Drug Discov 2019; 18(1): 41-58.
[http://dx.doi.org/10.1038/nrd.2018.168] [PMID: 30310233]
[68]
Shanmugam A, Muralidharan N, Velmurugan D, Gromiha MM. Therapeutic targets and computational approaches on drug development for COVID-19. Curr Top Med Chem 2020; 20(24): 2210-20.
[http://dx.doi.org/10.2174/18734294MTA4iMDMc5] [PMID: 32648845]
[69]
Zhou Y, Wang F, Tang J, Nussinov R, Cheng F. Artificial intelligence in COVID-19 drug repurposing. Lancet Digit Health 2020; 2(12): e667-76.
[http://dx.doi.org/10.1016/S2589-7500(20)30192-8] [PMID: 32984792]
[70]
Zhang P, Wei Z, Che C, Jin B. DeepMGT-DTI: Transformer network incorporating multilayer graph information for Drug-Target interaction prediction. Comput Biol Med 2022; 142: 105214.
[http://dx.doi.org/10.1016/j.compbiomed.2022.105214] [PMID: 35030496]
[71]
Wassermann AM, Bajorath J. BindingDB and ChEMBL: Online compound databases for drug discovery. Expert Opin Drug Discov 2011; 6(7): 683-7.
[http://dx.doi.org/10.1517/17460441.2011.579100] [PMID: 22650976]
[72]
Zhang H, Yang Y, Li J, et al. A novel virtual screening procedure identifies Pralatrexate as inhibitor of SARS-CoV-2 RdRp and it reduces viral replication in vitro. PLOS Comput Biol 2020; 16(12): e1008489.
[http://dx.doi.org/10.1371/journal.pcbi.1008489] [PMID: 33382685]
[73]
Zeng X, Song X, Ma T, et al. Repurpose open data to discover therapeutics for COVID-19 using deep learning. J Proteome Res 2020; 19(11): 4624-36.
[http://dx.doi.org/10.1021/acs.jproteome.0c00316] [PMID: 32654489]
[74]
Zhang W, Zhang Y, Min Z, et al. COVID19db: A comprehensive database platform to discover potential drugs and targets of COVID-19 at whole transcriptomic scale. Nucleic Acids Res 2022; 50(D1): D747-57.
[http://dx.doi.org/10.1093/nar/gkab850] [PMID: 34554255]
[75]
Wang Y, Li F, Zhang Y, et al. Databases for the targeted COVID-19 therapeutics. Br J Pharmacol 2020; 177(21): 4999-5001.
[http://dx.doi.org/10.1111/bph.15234] [PMID: 32845521]
[76]
Zhang L, Wang CC, Chen X. Predicting drug–target binding affinity through molecule representation block based on multi-head attention and skip connection. Brief Bioinform 2022; 23(6): bbac468.
[http://dx.doi.org/10.1093/bib/bbac468] [PMID: 36411674]
[77]
Zhang H, Liao L, Cai Y, Hu Y, Wang H. IVS2vec: A tool of inverse virtual screening based on word2vec and deep learning techniques. Methods 2019; 166: 57-65.
[http://dx.doi.org/10.1016/j.ymeth.2019.03.012] [PMID: 30910562]
[78]
Bai Q, Tan S, Xu T, Liu H, Huang J, Yao X. MolAICal: A soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm. Brief Bioinform 2021; 22(3): bbaa161.
[http://dx.doi.org/10.1093/bib/bbaa161] [PMID: 32778891]
[79]
Wang R, Fang X, Lu Y, Yang CY, Wang S. The PDBbind database: Methodologies and updates. J Med Chem 2005; 48(12): 4111-9.
[http://dx.doi.org/10.1021/jm048957q] [PMID: 15943484]
[80]
Irwin JJ, Shoichet BK. ZINC--a free database of commercially available compounds for virtual screening. J Chem Inf Model 2005; 45(1): 177-82.
[http://dx.doi.org/10.1021/ci049714+] [PMID: 15667143]
[81]
Gentile F, Agrawal V, Hsing M, et al. Deep docking: A deep learning platform for augmentation of structure based drug discovery. ACS Cent Sci 2020; 6(6): 939-49.
[http://dx.doi.org/10.1021/acscentsci.0c00229] [PMID: 32607441]
[82]
Saravanan KM, Zhang H, Senthil R, et al. Structural basis for the inhibition of SARS-CoV2 main protease by Indian medicinal plant-derived antiviral compounds. J Biomol Struct Dyn 2022; 40(5): 1970-8.
[http://dx.doi.org/10.1080/07391102.2020.1834457] [PMID: 33073712]
[83]
Nand M, Maiti P, Joshi T, et al. Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis. Sci Rep 2020; 10(1): 20397.
[http://dx.doi.org/10.1038/s41598-020-77524-x] [PMID: 33230180]
[84]
Deng M, Brägelmann J, Schultze JL, Perner S. Web-TCGA: An online platform for integrated analysis of molecular cancer data sets. BMC Bioinformatics 2016; 17(1): 72.
[http://dx.doi.org/10.1186/s12859-016-0917-9] [PMID: 26852330]
[85]
Niu N, Wang L. In vitro human cell line models to predict clinical response to anticancer drugs. Pharmacogenomics 2015; 16(3): 273-85.
[http://dx.doi.org/10.2217/pgs.14.170] [PMID: 25712190]
[86]
Barretina J, Caponigro G, Stransky N, et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012; 483(7391): 603-7.
[http://dx.doi.org/10.1038/nature11003] [PMID: 22460905]
[87]
Yang W, Soares J, Greninger P, et al. Genomics of drug sensitivity in cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells. Nucl Acids Res 2013; 41: D955-61.
[PMID: 23180760]
[88]
Holbeck SL, Camalier R, Crowell JA, et al. The national cancer institute ALMANAC: A comprehensive screening resource for the detection of anticancer drug pairs with enhanced therapeutic activity. Cancer Res 2017; 77(13): 3564-76.
[http://dx.doi.org/10.1158/0008-5472.CAN-17-0489] [PMID: 28446463]
[89]
Nguyen P, Doan P, Rimpilainen T, et al. Synthesis and preclinical validation of novel indole derivatives as a GPR17 agonist for glioblastoma treatment. J Med Chem 2021; 64(15): 10908-18.
[http://dx.doi.org/10.1021/acs.jmedchem.1c00277] [PMID: 34304559]
[90]
Rydzewski NR, Peterson E, Lang JM, et al. Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures. NPJ Genom Med 2021; 6(1): 76.
[http://dx.doi.org/10.1038/s41525-021-00239-z] [PMID: 34548481]
[91]
Suphavilai C, Bertrand D, Nagarajan N. Predicting cancer drug response using a recommender system. Bioinformatics 2018; 34(22): 3907-14.
[http://dx.doi.org/10.1093/bioinformatics/bty452] [PMID: 29868820]
[92]
Zhang H, Chen Y, Li F. Predicting anticancer drug response with deep learning constrained by signaling pathways. Front Bioinformat 2021; 1: 639349.
[http://dx.doi.org/10.3389/fbinf.2021.639349] [PMID: 36303766]
[93]
Choi J, Park S, Ahn J. RefDNN: A reference drug based neural network for more accurate prediction of anticancer drug resistance. Sci Rep 2020; 10(1): 1861.
[http://dx.doi.org/10.1038/s41598-020-58821-x] [PMID: 32024872]
[94]
Kuenzi BM, Park J, Fong SH, et al. Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell 2020; 38(5): 672-684.e6.
[http://dx.doi.org/10.1016/j.ccell.2020.09.014] [PMID: 33096023]
[95]
Hao J, Kim Y, Kim TK, Kang M. PASNet: Pathway-associated sparse deep neural network for prognosis prediction from high-throughput data. BMC Bioinformat 2018; 19(1): 510.
[http://dx.doi.org/10.1186/s12859-018-2500-z] [PMID: 30558539]
[96]
Deng L, Cai Y, Zhang W, Yang W, Gao B, Liu H. Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity. J Chem Inf Model 2020; 60(10): 4497-505.
[http://dx.doi.org/10.1021/acs.jcim.0c00331] [PMID: 32804489]
[97]
Huang ZA, Chen X, Zhu Z, et al. PBHMDA: Path-based human microbe-disease association prediction. Front Microbiol 2017; 8: 233.
[http://dx.doi.org/10.3389/fmicb.2017.00233] [PMID: 28275370]
[98]
Wang CC, Zhao Y, Chen X. Drug-pathway association prediction: From experimental results to computational models. Brief Bioinform 2021; 22(3): bbaa061.
[http://dx.doi.org/10.1093/bib/bbaa061] [PMID: 32393976]
[99]
Lecca P. Machine learning for causal inference in biological networks: Perspectives of this challenge. Front Bioinformat 2021; 1: 746712.
[http://dx.doi.org/10.3389/fbinf.2021.746712] [PMID: 36303798]
[100]
Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights 2020; 1: 14.
[http://dx.doi.org/10.1177/1177932219899051] [PMID: 32076369]
[101]
Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 2021; 13(1): 152.
[http://dx.doi.org/10.1186/s13073-021-00968-x] [PMID: 34579788]
[102]
Sundaram KKM, Bupesh G, Saravanan KM. Instrumentals behind embryo and cancer: A platform for prospective future in cancer research. AIMS Mol Sci 2022; 9(1): 25-45.
[http://dx.doi.org/10.3934/molsci.2022002]
[103]
Chen X, Yan CC, Zhang X, et al. Drug–target interaction prediction: Databases, web servers and computational models. Brief Bioinform 2016; 17(4): 696-712.
[http://dx.doi.org/10.1093/bib/bbv066] [PMID: 26283676]
[104]
Imming P, Sinning C, Meyer A. Drugs, their targets and the nature and number of drug targets. Nat Rev Drug Discov 2006; 5(10): 821-34.
[http://dx.doi.org/10.1038/nrd2132] [PMID: 17016423]
[105]
Cheng F, Liu C, Jiang J, et al. Prediction of drug-target interactions and drug repositioning via network-based inference. PLOS Comput Biol 2012; 8(5): e1002503.
[http://dx.doi.org/10.1371/journal.pcbi.1002503] [PMID: 22589709]
[106]
Song T, Zhang X, Ding M, Rodriguez-Paton A, Wang S, Wang G. DeepFusion: A deep learning based multi-scale feature fusion method for predicting drug-target interactions. Methods 2022; 204: 269-77.
[http://dx.doi.org/10.1016/j.ymeth.2022.02.007] [PMID: 35219861]
[107]
Zhang H, Li J, Saravanan KM, et al. An integrated deep learning and molecular dynamics simulation-based screening pipeline identifies inhibitors of a new cancer drug target TIPE2. Front Pharmacol 2021; 12: 772296.
[http://dx.doi.org/10.3389/fphar.2021.772296] [PMID: 34887765]
[108]
Chen X, Guan NN, Sun YZ, Li JQ, Qu J. MicroRNA-small molecule association identification: From experimental results to computational models. Brief Bioinform 2018; 21: 47-61.
[http://dx.doi.org/10.1093/bib/bby098] [PMID: 30325405]
[109]
Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: Taxonomy, trends and challenges of computational models. Brief Bioinform 2022; 23(5): bbac358.
[http://dx.doi.org/10.1093/bib/bbac358] [PMID: 36056743]
[110]
You ZH, Huang ZA, Zhu Z, et al. PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction. PLOS Comput Biol 2017; 13(3): e1005455.
[http://dx.doi.org/10.1371/journal.pcbi.1005455] [PMID: 28339468]
[111]
Zhu CC, Wang CC, Zhao Y, Zuo M, Chen X. Identification of miRNA–disease associations via multiple information integration with Bayesian ranking. Brief Bioinform 2021; 22(6): bbab302.
[http://dx.doi.org/10.1093/bib/bbab302] [PMID: 34347021]
[112]
Mutharasu G, Murugesan A, Konda Mani S, Yli-Harja O, Kandhavelu M. Transcriptomic analysis of glioblastoma multiforme providing new insights into GPR17 signaling communication. J Biomol Struct Dyn 2022; 40(6): 2586-99.
[http://dx.doi.org/10.1080/07391102.2020.1841029] [PMID: 33140689]
[113]
Chen X, Huang L. Computational model for ncRNA research. Brief Bioinform 2022; 23(6): bbac472.
[http://dx.doi.org/10.1093/bib/bbac472] [PMID: 36274235]
[114]
Chen X, Li TH, Zhao Y, Wang CC, Zhu CC. Deep-belief network for predicting potential miRNA-disease associations. Brief Bioinform 2021; 22(3): bbaa186.
[http://dx.doi.org/10.1093/bib/bbaa186] [PMID: 34020550]
[115]
Zhang L, Chen X, Yin J. Prediction of potential miRNA–disease associations through a novel unsupervised deep learning framework with variational autoencoder. Cells 2019; 8(9): 1040.
[http://dx.doi.org/10.3390/cells8091040] [PMID: 31489920]
[116]
Noor MBT, Zenia NZ, Kaiser MS, Mamun SA, Mahmud M. Application of deep learning in detecting neurological disorders from magnetic resonance images: A survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia. Brain Inform 2020; 7(1): 11.
[http://dx.doi.org/10.1186/s40708-020-00112-2] [PMID: 33034769]
[117]
Shenton ME, Hamoda HM, Schneiderman JS, et al. A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury. Brain Imaging Behav 2012; 6(2): 137-92.
[http://dx.doi.org/10.1007/s11682-012-9156-5] [PMID: 22438191]
[118]
Viard A, Eustache F, Segobin S. History of magnetic resonance imaging: A trip down memory lane. Neuroscience 2021; 474: 3-13.
[http://dx.doi.org/10.1016/j.neuroscience.2021.06.038] [PMID: 34242731]
[119]
Saravanan KM, Zhang H, Zhang H, Xi W, Wei Y. On the conformational dynamics of β-amyloid forming peptides: A computational perspective. Front Bioeng Biotechnol 2020; 8: 532.
[http://dx.doi.org/10.3389/fbioe.2020.00532] [PMID: 32656188]
[120]
Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. Lancet Digit Health 2019; 1(6): e271-97.
[http://dx.doi.org/10.1016/S2589-7500(19)30123-2] [PMID: 33323251]
[121]
Saravanan KM, Kannan M, Meera P, Bharathkumar N, Anand T. E3 ligases: A potential multi-drug target for different types of cancers and neurological disorders. Future Med Chem 2022; 14(3): 187-201.
[http://dx.doi.org/10.4155/fmc-2021-0157] [PMID: 35100004]
[122]
Yin W, Li L, Wu FX. Deep learning for brain disorder diagnosis based on fMRI images. Neurocomputing 2022; 469: 332-45.
[http://dx.doi.org/10.1016/j.neucom.2020.05.113]
[123]
Khan DM, Yahya N, Kamel N, Faye I. Automated diagnosis of major depressive disorder using brain effective connectivity and 3d convolutional neural network. IEEE Access 2021; 9(8835): 46.
[http://dx.doi.org/10.1109/ACCESS.2021.3049427]
[124]
Sadeghi D, Shoeibi A, Ghassemi N, et al. An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 146: 105554.
[http://dx.doi.org/10.1016/j.compbiomed.2022.105554] [PMID: 35569333]
[125]
Lima AA, Mridha MF, Das SC, Kabir MM, Islam MR, Watanobe Y. A comprehensive survey on the detection, classification, and challenges of neurological disorders. Biology 2022; 11(3): 469.
[http://dx.doi.org/10.3390/biology11030469] [PMID: 35336842]
[126]
Eshaghi A, Riyahi-Alam S, Saeedi R, et al. Classification algorithms with multi-modal data fusion could accurately distinguish neuromyelitis optica from multiple sclerosis. Neuroimage Clin 2015; 7: 306-14.
[http://dx.doi.org/10.1016/j.nicl.2015.01.001] [PMID: 25610795]
[127]
Eliezer M, Hamel A-L, Houdart E, et al. Loss of smell in patients with COVID-19. Neurology 2020; 95 e3145 LP-
[http://dx.doi.org/10.1212/WNL.0000000000010806]
[128]
Shafiabadi Hassani N, Talakoob H, Karim H, Mozafari Bazargany MH, Rastad H. Cardiac magnetic resonance imaging findings in 2954 COVID-19 adult survivors: A comprehensive systematic review. J Magn Reson Imaging 2022; 55(3): 866-80.
[http://dx.doi.org/10.1002/jmri.27852] [PMID: 34309139]
[129]
Gulko E, Oleksk ML, Gomes W, et al. MRI brain findings in 126 patients with COVID-19: Initial observations from a descriptive literature review. J Neuroradiol 2020; 41: 2199-203.
[http://dx.doi.org/10.3174/ajnr.A6805] [PMID: 32883670]
[130]
Karatas M, Eriskin L, Deveci M, Pamucar D, Garg H. Big data for healthcare industry 4.0: Applications, challenges and future perspectives. Expert Syst Appl 2022; 200: 116912.
[http://dx.doi.org/10.1016/j.eswa.2022.116912]
[131]
Wang S, Zha Y, Li W, et al. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J 2020; 56(2): 2000775.
[http://dx.doi.org/10.1183/13993003.00775-2020] [PMID: 32444412]
[132]
Xu X, Jiang X, Ma C, et al. A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering 2020; 6(10): 1122-9.
[http://dx.doi.org/10.1016/j.eng.2020.04.010] [PMID: 32837749]
[133]
Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl Intell 2021; 51(2): 854-64.
[http://dx.doi.org/10.1007/s10489-020-01829-7] [PMID: 34764548]
[134]
Jin C, Chen W, Cao Y, et al. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun 2020; 11(1): 5088.
[http://dx.doi.org/10.1038/s41467-020-18685-1] [PMID: 33037212]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy