Login Register Cart 0
Generic placeholder image

Current Genomics

Editor-in-Chief

ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

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

A Study and Analysis of Disease Identification using Genomic Sequence Processing Models: An Empirical Review

Author(s): Sony K. Ahuja, Deepti D. Shrimankar* and Aditi R. Durge

Volume 24, Issue 4, 2023

Published on: 24 November, 2023

Page: [207 - 235] Pages: 29

DOI: 10.2174/0113892029269523231101051455

Price: $65

Open Access Journals Promotions 2
Abstract

Human gene sequences are considered a primary source of comprehensive information about different body conditions. A wide variety of diseases including cancer, heart issues, brain issues, genetic issues, etc. can be pre-empted via efficient analysis of genomic sequences. Researchers have proposed different configurations of machine learning models for processing genomic sequences, and each of these models varies in terms of their performance & applicability characteristics. Models that use bioinspired optimizations are generally slower, but have superior incrementalperformance, while models that use one-shot learning achieve higher instantaneous accuracy but cannot be scaled for larger disease-sets. Due to such variations, it is difficult for genomic system designers to identify optimum models for their application-specific & performance-specific use cases. To overcome this issue, a detailed survey of different genomic processing models in terms of their functional nuances, application-specific advantages, deployment-specific limitations, and contextual future scopes is discussed in this text. Based on this discussion, researchers will be able to identify optimal models for their functional use cases. This text also compares the reviewed models in terms of their quantitative parameter sets, which include, the accuracy of classification, delay needed to classify large-length sequences, precision levels, scalability levels, and deployment cost, which will assist readers in selecting deployment-specific models for their contextual clinical scenarios. This text also evaluates a novel Genome Processing Efficiency Rank (GPER) for each of these models, which will allow readers to identify models with higher performance and low overheads under real-time scenarios.

Keywords: Genome processing, machine learning, disease, deep learning, gene network, classification.

« Previous Next »
Graphical Abstract

[1]
Tu, JJ; Ou-Yang, L; Hu, X; Zhang, XF Inferring gene network rewiring by combining gene expression and gene mutation data IEEE/ACM Trans Comput Biol Bioinforma, 2019, 16(3), 1042-1048.
[http://dx.doi.org/10.1109/TCBB.2018.2834529]
[2]
Tenekeci, S; Isik, Z Integrative biological network analysis to identify shared genes in metabolic disorders. IEEE/ACM Trans Comput Biol Bioinforma., 2022, 19(1), 522-530.
[http://dx.doi.org/10.1109/TCBB.2020.2993301]
[3]
Yang, K.; Wang, R.; Liu, G.; Shu, Z.; Wang, N.; Zhang, R.; Yu, J.; Chen, J.; Li, X.; Zhou, X. HerGePred: Heterogeneous network embedding representation for disease gene prediction. IEEE J. Biomed. Health Inform., 2019, 23(4), 1805-1815.
[http://dx.doi.org/10.1109/JBHI.2018.2870728] [PMID: 31283472]
[4]
Yu, L; Gao, L. Human pathway-based disease network. IEEE/ACM Trans Comput Biol Bioinforma., 2019, 16(4), 1240-1249.
[http://dx.doi.org/10.1109/TCBB.2017.2774802]
[5]
Luo, P; Tian, LP; Ruan, J; Wu, FX Disease gene prediction by integrating PPI networks, clinical RNA-Seq data and OMIM data. IEEE/ACM Trans Comput Biol Bioinforma., 2019, 16(1), 222-232.
[6]
Ni, P; Wang, J; Zhong, P; Li, Y; Wu, FX; Pan, Y Constructing disease similarity networks based on disease module theory. IEEE/ACM Trans Comput Biol Bioinforma., 2020, 17(3), 906-915.
[http://dx.doi.org/10.1109/TCBB.2018.2817624]
[7]
Yang, K; Zheng, Y; Lu, K; Chang, K; Wang, N; Shu, Z PDGNet: Predicting disease genes using a deep neural network with multiview features. IEEE/ACM Trans Comput Biol Bioinforma., 2022, 19(1), 575-584.
[8]
Chen, H.; Zhang, Z.; Li, G. Relating disease-gene interaction network with disease-associated ncRNAs. IEEE Access, 2019, 7, 133521-133528.
[http://dx.doi.org/10.1109/ACCESS.2019.2941955]
[9]
Shang, H; Liu,, ZP Prioritizing type 2 diabetes genes by weighted PageRank on bilayer heterogeneous networks. IEEE/ACM Trans Comput Biol Bioinforma., 2021, 18(1), 336-346.
[10]
Kamal, M.S.; Northcote, A.; Chowdhury, L.; Dey, N.; Crespo, R.G.; Herrera-Viedma, E. Alzheimer’s patient analysis using image and gene expression data and explainable-AI to present associated genes. IEEE Trans. Instrum. Meas., 2021, 70, 1-7.
[http://dx.doi.org/10.1109/TIM.2021.3107056]
[11]
Zhao, X; Yang, Y; Yin, M. MHRWR: Prediction of lncRNAdisease associations based on multiple heterogeneous networks. IEEE/ACM Trans Comput Biol Bioinforma., 2021, 18(6), 2577-2585.
[12]
Bin, Y.; Zhu, Q.; Li, M.; Xia, J. Comprehensive analysis of alzheimer’s disease biologically candidate causal genes revealed by function association study with GWAS. IEEE Access, 2019, 7, 114236-114245.
[http://dx.doi.org/10.1109/ACCESS.2019.2935515]
[13]
Malhotra, AG; Singh, S; Jha, M; Pandey, KM A parametric targetability evaluation approach for vitiligo proteome extracted through integration of gene ontologies and protein interaction topologies. IEEE/ACM Trans Comput Biol Bioinforma., 2019, 16(6), 1830-1842.
[http://dx.doi.org/10.1109/TCBB.2018.2835459]
[14]
Sikandar, M.; Sohail, R.; Saeed, Y.; Zeb, A.; Zareei, M.; Khan, M.A.; Khan, A.; Aldosary, A.; Mohamed, E.M. Analysis for disease gene association using machine learning. IEEE Access, 2020, 8, 160616-160626.
[http://dx.doi.org/10.1109/ACCESS.2020.3020592]
[15]
Moni, M.A.; Islam, M.B.; Rahman, M.R.; Rashed-Al-Mahfuz, M.; Awal, M.A.; Islam, S.M.S.; Mollah, M.N.H.; Quinn, J.M.W. Network-based computational approach to identify delineating common cell pathways influencing type 2 diabetes and diseases of bone and joints. IEEE Access, 2020, 8, 1486-1497.
[http://dx.doi.org/10.1109/ACCESS.2019.2962091]
[16]
Qin, R; Duan, L; Zheng, H; Li-Ling, J; Song, K; Zhang, Y An ontology-independent representation learning for similar disease detection based on multi-layer similarity network. IEEE/ACM Trans Comput Biol Bioinforma., 2021, 18(1), 183-193.
[17]
Xie, J; Zhao, C; Sun, J; Li, J; Yang, F; Wang, J Prediction of essential genes in comparison states using machine learning. IEEE/ACM Trans Comput Biol Bioinforma., 2021, 18(5), 1784-1792.
[http://dx.doi.org/10.1109/TCBB.2020.3027392]
[18]
Grani, G; Madeddu, L; Velardi, P. A network-based analysis of disease modules from a taxonomic perspective. IEEE J Biomed Heal informatics., 2022, 26(4), 1773-1781.
[http://dx.doi.org/10.1109/JBHI.2021.3106787]
[19]
Luo, H.; Wang, D.; Liu, J.; Ju, Y.; Jin, Z. A framework integrating heterogeneous databases for the completion of gene networks. IEEE Access, 2019, 7, 168859-168869.
[http://dx.doi.org/10.1109/ACCESS.2019.2954994]
[20]
Petti, M; Bizzarri, D; Verrienti, A; Falcone, R; Farina, L Connectivity significance for disease gene prioritization in an expanding universe. IEEE/ACM Trans Comput Biol Bioinforma., 2020, 17(6), 2155-2161.
[http://dx.doi.org/10.1109/TCBB.2019.2938512]
[21]
Kawichai, T.; Suratanee, A.; Plaimas, K. Meta-path based gene ontology profiles for predicting drug-disease associations. IEEE Access, 2021, 9, 41809-41820.
[http://dx.doi.org/10.1109/ACCESS.2021.3065280]
[22]
Krittanawong, C.; Johnson, K.W.; Choi, E.; Kaplin, S.; Venner, E.; Murugan, M.; Wang, Z.; Glicksberg, B.S.; Amos, C.I.; Schatz, M.C.; Tang, W.H.W. Artificial intelligence and cardiovascular genetics. Life, 2022, 12(2), 279.
[http://dx.doi.org/10.3390/life12020279] [PMID: 35207566]
[23]
Caballé, N.C.; Castillo-Sequera, J.L.; Gómez-Pulido, J.A.; Gómez-Pulido, J.M.; Polo-Luque, M.L. Machine learning applied to diagnosis of human diseases: A systematic review. Appl. Sci., 2020, 10(15), 1-27.
[24]
Xiang, J.; Kong, L.; Xu, J.; Yu, L.; Liu, S.; Liu, Z. Construction of PARPi Resistance-related Competing Endogenous RNA Network. Curr. Genomics, 2022, 23(4), 262-274.
[http://dx.doi.org/10.2174/1389202923666220527114108] [PMID: 36777878]
[25]
Neelaveni, J.; Geetha Devasana, M.S. 2020.
[26]
Piñero, J.; Ramírez-Anguita, J.M.; Saüch-Pitarch, J.; Ronzano, F.; Centeno, E.; Sanz, F.; Furlong, L.I. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res., 2020, 48(D1), D845-D855.
[PMID: 31680165]
[27]
Wang, X.; Yang, Y.; Tan, X.; Mao, X.; Wei, D.; Yao, Y.; Jiang, P.; Mo, D.; Wang, T.; Yan, F. Identification of tRNA-derived fragments expression profile in breast cancer tissues. Curr. Genomics, 2019, 20(3), 199-213.
[http://dx.doi.org/10.2174/1389202920666190326145459] [PMID: 31929727]
[28]
Jiang, X.; Zhao, J.; Qian, W.; Song, W.; Lin, G.N. A generative adversarial network model for disease gene prediction with RNA-seq data. IEEE Access, 2020, 8, 37352-37360.
[http://dx.doi.org/10.1109/ACCESS.2020.2975585]
[29]
Kim, MS; Kim, D; Kim, JR Stage-dependent gene expression profiling in colorectal cancer. IEEE/ACM Trans Comput Biol Bioinforma., 2019, 16(5), 1685-1692.
[http://dx.doi.org/10.1109/TCBB.2018.2814043]
[30]
Ghulam, A.; Lei, X.; Guo, M.; Bian, C. Disease-pathway association prediction based on random walks with restart and pageRank. IEEE Access, 2020, 8, 72021-72038.
[http://dx.doi.org/10.1109/ACCESS.2020.2987071]
[31]
Schlosser, P; Knaus, J; Schmutz, M; Dohner, K; Plass, C; Bullinger, L L Netboost: Boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and huntington’s disease. IEEE/ACM Trans Comput Biol Bioinforma., 2021, 18(6), 2635-2648.
[32]
Xu, T; Ou-Yang, L; Yan, H; Zhang, XF Time-varying differential network analysis for revealing network rewiring over cancer progression. IEEE/ACM Trans Comput Biol Bioinforma., 2021, 18(4), 1632-1642.
[http://dx.doi.org/10.1109/TCBB.2019.2949039]
[33]
Nassif, A.B.; Talib, M.A.; Nasir, Q.; Afadar, Y.; Elgendy, O. Breast cancer detection using artificial intelligence techniques: A systematic literature review. Artif. Intell. Med., 2022, 127, 102276.
[http://dx.doi.org/10.1016/j.artmed.2022.102276]
[34]
Jiang, H.; Yang, M.; Chen, X.; Li, M.; Li, Y.; Wang, J. miRTMC: A miRNA target prediction method based on matrix completion algorithm. IEEE J. Biomed. Health Inform., 2020, 24(12), 3630-3641.
[http://dx.doi.org/10.1109/JBHI.2020.2987034] [PMID: 32287029]
[35]
Chakrabarty, B; Das, D; Bulusu, G; Roy, A Network-based analysis of fatal comorbidities of COVID-19 and potential therapeutics. IEEE/ACM Trans Comput Biol Bioinforma., 2021, 18(4), 1271-1280.
[36]
Zhang, Y.; Lei, X.; Fang, Z.; Pan, Y. CircRNA-disease associations prediction based on metapath2vec++ and matrix factorization. Big Data Mining and Analytics, 2020, 3(4), 280-291.
[http://dx.doi.org/10.26599/BDMA.2020.9020025]
[37]
Tian, Y.; Su, X.; Su, Y.; Zhang, X. EMODMI: A multi-objective optimization based method to identify disease modules. IEEE Trans. Emerg. Top. Comput. Intell., 2021, 5(4), 570-582.
[http://dx.doi.org/10.1109/TETCI.2020.3014923]
[38]
Hennings-Yeomans, PH; Cooper, GF Improving the prediction of clinical outcomes from genomic data using multiresolution analysis IEEE/ACM Trans Comput Biol Bioinforma., 2012, 9(5), 1442-1450.
[http://dx.doi.org/10.1109/TCBB.2012.80]
[39]
Atta-Ur-Rahman. Nasir, M.U.; Gollapalli, M.; Zubair, M.; Saleem, M.A.; Mehmood, S.; Khan, M.A.; Mosavi, A. Advance genome disorder prediction model empowered with deep learning. IEEE Access, 2022, 10, 70317-70328.
[http://dx.doi.org/10.1109/ACCESS.2022.3186998]
[40]
Guo, W.; Zeng, T.; Huang, T.; Cai, Y.D. Disease cluster detection and functional characterization. IEEE Access, 2020, 8, 141958-141966.
[http://dx.doi.org/10.1109/ACCESS.2020.3013666]
[41]
Alzubi, R.; Ramzan, N.; Alzoubi, H.; Amira, A. A hybrid feature selection method for complex diseases SNPs. IEEE Access, 2018, 6, 1292-1301.
[http://dx.doi.org/10.1109/ACCESS.2017.2778268]
[42]
Wang, P.; Chen, Y.; Lü, J.; Wang, Q.; Yu, X. Graphical features of functional genes in human protein interaction network. IEEE Trans. Biomed. Circuits Syst., 2016, 10(3), 707-720.
[http://dx.doi.org/10.1109/TBCAS.2015.2487299] [PMID: 26841412]
[43]
Yang, C.H.; Chuang, L.Y.; Lin, Y.D. Epistasis analysis using an improved fuzzy c-means-based entropy approach. IEEE Trans. Fuzzy Syst., 2020, 28(4), 718-730.
[http://dx.doi.org/10.1109/TFUZZ.2019.2914629]
[44]
Fabijańska, A.; Grabowski, S. Viral genome deep classifier. IEEE Access, 2019, 7, 81297-81307.
[http://dx.doi.org/10.1109/ACCESS.2019.2923687]
[45]
Fergus, P; Montanez, CC; Abdulaimma, B; Lisboa, P; Chalmers, C; Pineles, B Utilizing deep learning and genome wide association studies for epistatic-driven preterm birth classification in african-american women. IEEE/ACM Trans Comput Biol Bioinforma., 2020, 17(2), 668-678.
[46]
Karim, MR; Cochez, M; Zappa, A; Sahay, R; Rebholz-Schuhmann, D; Beyan, O Convolutional embedded networks for population scale clustering and bio-ancestry inferencing. IEEE/ACM Trans Comput Biol Bioinforma., 2022, 19(1), 369-382.
[http://dx.doi.org/10.1109/TCBB.2020.2994649]
[47]
Lee, CY; Zeng, JH; Lee, SY; Lu, RB; Kuo, PH SNP data science for classification of bipolar disorder I and bipolar disorder II. IEEE/ACM Trans Comput Biol Bioinforma., 2021, 18(6), 2862-2869.
[48]
Shrimankar, D.D.; Durge, A.R.; Sawarkar, A.D. Heuristic analysis of genomic sequence processing models for high efficiency prediction: A statistical perspective. Curr. Genomics, 2022, 23(5), 299-317.
[http://dx.doi.org/10.2174/1389202923666220927105311] [PMID: 36778194]
[49]
Whata, A.; Chimedza, C. Deep learning for SARS COV-2 genome sequences. IEEE Access, 2021, 9, 59597-59611.
[http://dx.doi.org/10.1109/ACCESS.2021.3073728] [PMID: 34812391]
[50]
Metsis, V; Makedon, F; Shen, D; Huang, H. DNA copy number selection using robust structured sparsity-inducing norms. IEEE/ACM Trans Comput Biol Bioinforma., 2014, 11(1), 168-181.
[http://dx.doi.org/10.1109/TCBB.2013.141]
[51]
Dlamini, G.S.; Müller, S.J.; Meraba, R.L.; Young, R.A.; Mashiyane, J.; Chiwewe, T.; Mapiye, D.S. Classification of COVID-19 and other pathogenic sequences: A dinucleotide frequency and machine learning approach. IEEE Access, 2020, 8, 195263-195273.
[http://dx.doi.org/10.1109/ACCESS.2020.3031387] [PMID: 34976561]
[52]
Zhu, L; Hofestadt, R; Ester, M Tissue-specific subcellular localization prediction using multi-label markov random fields. IEEE/ACM Trans Comput Biol Bioinforma., 2019, 16(5), 1471-1482.
[http://dx.doi.org/10.1109/TCBB.2019.2897683]
[53]
Hind, J; Lisboa, P; Hussain, AJ; Al-Jumeily, D A novel approach to detecting epistasis using random sampling regularisation. IEEE/ACM Trans Comput Biol Bioinforma., 2020, 17(5), 1535-1545.
[54]
Montañez, C.A.C.; Fergus, P.; Chalmers, C.; Malim, N.H.A.H.; Abdulaimma, B.; Reilly, D.; Falciani, F. SAERMA: Stacked autoencoder rule mining algorithm for the interpretation of epistatic interactions in GWAS for extreme obesity. IEEE Access, 2020, 8, 112379-112392.
[http://dx.doi.org/10.1109/ACCESS.2020.3002923]
[55]
Shang, J.; Wang, X.; Wu, X.; Sun, Y.; Ding, Q.; Liu, J.X.; Zhang, H. A review of ant colony optimization based methods for detecting epistatic interactions. IEEE Access, 2019, 7, 13497-13509.
[http://dx.doi.org/10.1109/ACCESS.2019.2894676]
[56]
Sarkar, E.; Chielle, E.; Gürsoy, G.; Mazonka, O.; Gerstein, M.; Maniatakos, M. Fast and scalable private genotype imputation using machine learning and partially homomorphic encryption. IEEE Access, 2021, 9, 93097-93110.
[http://dx.doi.org/10.1109/ACCESS.2021.3093005] [PMID: 34476144]
[57]
Wu, Q.; Ye, Y.; Liu, Y.; Ng, M.K. SNP selection and classification of genome-wide SNP data using stratified sampling random forests. IEEE Trans. Nanobiosci., 2012, 11(3), 216-227.
[http://dx.doi.org/10.1109/TNB.2012.2214232] [PMID: 22987127]
[58]
Davi, C.; Pastor, A.; Oliveira, T.; Neto, F.B.L.; Braga-Neto, U.; Bigham, A.W.; Bamshad, M.; Marques, E.T.A.; Acioli-Santos, B. Severe dengue prognosis using human genome data and machine learning. IEEE Trans. Biomed. Eng., 2019, 66(10), 2861-2868.
[http://dx.doi.org/10.1109/TBME.2019.2897285] [PMID: 30716030]
[59]
Lupski, J.R.; Liu, P.; Stankiewicz, P.; Carvalho, C.M.B.; Posey, J.E. Clinical genomics and contextualizing genome variation in the diagnostic laboratory. Expert Rev. Mol. Diagn., 2020, 20(10), 995-1002.
[http://dx.doi.org/10.1080/14737159.2020.1826312] [PMID: 32954863]
[60]
Seaby, E.G.; Ennis, S. Challenges in the diagnosis and discovery of rare genetic disorders using contemporary sequencing technologies. Brief. Funct. Genomics, 2020, 19(4), 243-258.
[http://dx.doi.org/10.1093/bfgp/elaa009] [PMID: 32393978]
[61]
Baldridge, D.; Wangler, M.F.; Bowman, A.N.; Yamamoto, S.; Schedl, T.; Pak, S.C.; Postlethwait, J.H.; Shin, J.; Solnica-Krezel, L.; Bellen, H.J.; Westerfield, M. Model organisms contribute to diagnosis and discovery in the undiagnosed diseases network: current state and a future vision. Orphanet J. Rare Dis., 2021, 16(1), 206.
[http://dx.doi.org/10.1186/s13023-021-01839-9] [PMID: 33962631]
[62]
Wang, L.; Balmat, T.J.; Antonia, A.L.; Constantine, F.J.; Henao, R.; Burke, T.W. An atlas connecting shared genetic architecture of human diseases and molecular phenotypes provides insight into COVID-19 susceptibility. medRxiv, 2020.
[http://dx.doi.org/10.1101/2020.12.20.20248572]
[63]
Seaby, E.G.; Thomas, N.S.; Webb, A.; Brittain, H.; Taylor Tavares, A.L.; Baralle, D. Targeting de novo loss-of-function variants in constrained disease genes improves diagnostic rates in the 100,000 Genomes Project. Hum. Genet., 2022, 142(3), 351-362.
[PMID: 36477409]
[64]
Stockdale, J.E.; Liu, P.; Colijn, C. The potential of genomics for infectious disease forecasting. Nat. Microbiol., 2022, 7(11), 1736-1743.
[http://dx.doi.org/10.1038/s41564-022-01233-6] [PMID: 36266338]
[65]
Marwaha, S.; Knowles, J.W.; Ashley, E.A. A guide for the diagnosis of rare and undiagnosed disease: beyond the exome. Genome Med., 2022, 14(1), 23.
[http://dx.doi.org/10.1186/s13073-022-01026-w] [PMID: 35220969]
[66]
Odgis, J.A.; Gallagher, K.M.; Rehman, A.U.; Marathe, P.N.; Bonini, K.E.; Sebastin, M.; Di Biase, M.; Brown, K.; Kelly, N.R.; Ramos, M.A.; Thomas-Wilson, A.; Guha, S.; Okur, V.; Ganapathi, M.; Elkhoury, L.; Edelmann, L.; Zinberg, R.E.; Abul-Husn, N.S.; Diaz, G.A.; Greally, J.M.; Suckiel, S.A.; Jobanputra, V.; Horowitz, C.R.; Kenny, E.E.; Wasserstein, M.P.; Gelb, B.D. Detection of mosaic variants using genome sequencing in a large pediatric cohort. Am. J. Med. Genet. A., 2023, 191(3), 699-710.
[http://dx.doi.org/10.1002/ajmg.a.63062] [PMID: 36563179]
[67]
Desingu, P.A.; Nagarajan, K. Detection of beak and feather disease virus in India and its implications. Transbound. Emerg. Dis., 2022, 69(6), e3469-e3478.
[http://dx.doi.org/10.1111/tbed.14749] [PMID: 36316791]
[68]
Alzubi, R.; Ramzan, N.; Alzoubi, H.; Katsigiannis, S. SNPs-based hypertension disease detection via machine learning techniques. 2018 24th International Conference on Automation and Computing (ICAC) 2018., 06-07 September 2018Newcastle Upon Tyne, UK
[http://dx.doi.org/10.23919/IConAC.2018.8748972]
[69]
Perera, S.; Hewage, K.; Gunarathne, C.; Navarathna, R.; Herath, D.; Ragel, R.G. Detection of novel biomarker genes of alzheimer’s disease using gene expression data. In: 2020 Moratuwa Engineering Research Conference (MERCon); , 2020, pp. 1-6.
[70]
Mohanty, A.; Prusty, A.R.; Cherukuri, R.C. Cancer tumor detection using genetic mutated data and machine learning models. 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP), 2022, pp. 1-6.
[http://dx.doi.org/10.1109/ICICCSP53532.2022.9862476]
[71]
Ahmed, H.; Soliman, H.; Elmogy, M. Early detection of alzheimer’s disease based on single nucleotide polymorphisms (SNPs) analysis and machine learning techniques. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), 2020, pp. 1-6.
[http://dx.doi.org/10.1109/ICDABI51230.2020.9325640]
[72]
Harikrishnan, N.B.; Pranay, S.Y.; Nagaraj, N. Classification of SARS-CoV-2 viral genome sequences using Neurochaos Learning. Med. Biol. Eng. Comput., 2022, 60(8), 2245-2255.
[http://dx.doi.org/10.1007/s11517-022-02591-3] [PMID: 35668230]
[73]
Ahmed, Z.; Zeeshan, S.; Mendhe, D.; Dong, X. Human gene and disease associations for clinical‐genomics and precision medicine research. Clin. Transl. Med., 2020, 10(1), 297-318.
[http://dx.doi.org/10.1002/ctm2.28] [PMID: 32508008]
[74]
Atallah, R.; Al-Mousa, A. Heart disease detection using machine learning majority voting ensemble method. 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS), 2019.09-11 October 2019Amman, Jordan
[http://dx.doi.org/10.1109/ICTCS.2019.8923053]
[75]
Jo, T. Deep learning-based identification of genetic variants: application to Alzheimer’s disease classification. Brief. Bioinform., 2022, 23(2), bbac022.
[76]
Das, B. A deep learning model for identification of diabetes type 2 based on nucleotide signals. Neural Comput. Appl., 2022, 34(15), 12587-12599. [Internet]
[http://dx.doi.org/10.1007/s00521-022-07121-8]
[77]
Sardar, A.; Rashid, K.; Abduljabbar, H.N.; Alhayani, B. Coronavirus disease (COVID - 19) cases analysis using machine - learning applications. Appl. Nanosci., 2021, (0123456789) [Internet]..
[http://dx.doi.org/10.1007/s13204-021-01868-7]
[78]
Khodaei, A.; Shams, P.; Sharifi, H.; Mozaffari-Tazehkand, B. Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods. Biomed. Signal Process. Control, 2023, 80(P1), 104192.
[http://dx.doi.org/10.1016/j.bspc.2022.104192] [PMID: 36168586]

Rights & Permissions Print Cite
Article Metrics
25
3
Wayfinder Image
Open Access Journals Promotions
© 2024 Bentham Science Publishers | Privacy Policy