Login Register Cart 0
Generic placeholder image

Current Computer-Aided Drug Design

Editor-in-Chief

ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

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

Identify Diabetes-related Targets based on ForgeNet_GPC

Author(s): Bin Yang, Linlin Wang* and Wenzheng Bao*

Volume 20, Issue 7, 2024

Published on: 03 January, 2024

Page: [1042 - 1054] Pages: 13

DOI: 10.2174/0115734099258183230929173855

Price: $65

Abstract

Background: Research on potential therapeutic targets and new mechanisms of action can greatly improve the efficiency of new drug development.

Aims: Polygenic genetic diseases, such as diabetes, are caused by the interaction of multiple gene loci and environmental factors.

Objective: In this study, a disease target identification algorithm based on protein recognition is proposed.

Materials and Methods: In this method, the related and unrelated targets are collected from literature databases for treating diabetes. The transcribed proteins corresponding to each target are queried in order to construct a protein dataset. Six protein feature extraction algorithms (AAC, CKSAAGP, DDE, DPC, GAAP, and TPC) are utilized to obtain the feature vectors of each protein, which are merged into the full feature vectors.

Results: A novel classifier (forgeNet_GPC) based on forgeNet and Gaussian process classifier (GPC) is proposed to classify the proteins.

Conclusion: In forgeNet_GPC, forgeNet is utilized to select the important features, and GPC is utilized to solve the classification problem. The experimental results reveal that forgeNet_GPC performs better than 22 classifiers in terms of ROC-AUC, PR-AUC, MCC, Youden Index, and Kappa.

« Previous Next »
[1]
Sacks, D.A.; Greenspoon, J.S.; Abu-Fadil, S.; Henry, H.M.; Wolde-Tsadik, G.; Yao, J.F.F. Toward universal criteria for gestational diabetes: The 75-gram glucose tolerance test in pregnancy. Am. J. Obstet. Gynecol., 1995, 172(2), 607-614.
[http://dx.doi.org/10.1016/0002-9378(95)90580-4] [PMID: 7856693]
[2]
Alberti, K.G.M.M.; Zimmet, P.; Shaw, J. Metabolic syndrome-a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet. Med., 2006, 23(5), 469-480.
[http://dx.doi.org/10.1111/j.1464-5491.2006.01858.x] [PMID: 16681555]
[3]
Olawale, F.G.; Ajaja, U.I.; Aninye, I.I.; Nwozo, S.O.; Adaramoye, O.A. Long term effects of streptozotocin induced diabetes mellitus on hepatic, nephrotic and cardiac physiology of female wistar rats. Nige. J. Pharma. Res., 2021, 17(1), 71-80.
[http://dx.doi.org/10.4314/njpr.v17i1.8]
[4]
Yang, H.; Fan, S.; Song, D.; Wang, Z.; Ma, S.; Li, S.; Li, X.; Xu, M.; Xu, M.; Wang, X. Long-term streptozotocin-induced diabetes in rats leads to severe damage of brain blood vessels and neurons via enhanced oxidative stress. Mol. Med. Rep., 2013, 7(2), 431-440.
[http://dx.doi.org/10.3892/mmr.2012.1227] [PMID: 23232924]
[5]
Anderson, E.J.; Kypson, A.P.; Rodriguez, E.; Anderson, C.A.; Lehr, E.J.; Neufer, P.D. Substrate-specific derangements in mitochondrial metabolism and redox balance in the atrium of the type 2 diabetic human heart. J. Am. Coll. Cardiol., 2009, 54(20), 1891-1898.
[http://dx.doi.org/10.1016/j.jacc.2009.07.031] [PMID: 19892241]
[6]
Rizzo, M.; Cianflone, D.; Maranta, F. Treatment of diabetes and heart failure: Facts and hopes. Int. J. Cardiol., 2022, 359, 118-119.
[http://dx.doi.org/10.1016/j.ijcard.2022.04.035] [PMID: 35439587]
[7]
Marchetti, P.; Bugliani, M.; Lupi, R.; Marselli, L.; Masini, M.; Boggi, U.; Filipponi, F.; Weir, G.C.; Eizirik, D.L.; Cnop, M. The endoplasmic reticulum in pancreatic beta cells of type 2 diabetes patients. Diabetologia, 2007, 50(12), 2486-2494.
[http://dx.doi.org/10.1007/s00125-007-0816-8] [PMID: 17906960]
[8]
Khin, P.P.; Lee, J.H.; Jun, H.S. A brief review of the mechanisms of β-Cell dedifferentiation in type 2 diabetes. Nutrients, 2021, 13(5), 1593.
[http://dx.doi.org/10.3390/nu13051593] [PMID: 34068827]
[9]
Buenaventura, T.; Kanda, N.; Douzenis, P.C.; Jones, B.; Bloom, S.R.; Chabosseau, P.; Corrêa, I.R., Jr; Bosco, D.; Piemonti, L.; Marchetti, P.; Johnson, P.R.; Shapiro, A.M.J.; Rutter, G.A.; Tomas, A. A targeted RNAi screen identifies endocytic trafficking factors that control glp-1 receptor signaling in pancreatic β-cells. Diabetes, 2018, 67(3), 385-399.
[http://dx.doi.org/10.2337/db17-0639] [PMID: 29284659]
[10]
Wu, T.; Zhang, S; Xu, J.; Zhang, Y.; Sun, T.; Shao, Y.; Wang, J.; Tang, W.; Chen, F.; Han, X. HRD1, an important player in pancreatic β-Cell failure and therapeutic target for type 2 diabetic mice. American Diabetes Association, 2020, 69(5), 940-953.
[http://dx.doi.org/10.2337/db19-1060]
[11]
D’Addio, F.; Maestroni, A.; Assi, E.; Ben Nasr, M.; Amabile, G.; Usuelli, V.; Loretelli, C.; Bertuzzi, F.; Antonioli, B.; Cardarelli, F.; El Essawy, B.; Solini, A.; Gerling, I.C.; Bianchi, C.; Becchi, G.; Mazzucchelli, S.; Corradi, D.; Fadini, G.P.; Foschi, D.; Markmann, J.F.; Orsi, E.; Škrha, J., Jr; Camboni, M.G.; Abdi, R.; James Shapiro, A.M.; Folli, F.; Ludvigsson, J.; Del Prato, S.; Zuccotti, G.; Fiorina, P. The IGFBP3/TMEM219 pathway regulates beta cell homeostasis. Nat. Commun., 2022, 13(1), 684.
[http://dx.doi.org/10.1038/s41467-022-28360-2] [PMID: 35115561]
[12]
Katz, L.S.; Brill, G.; Zhang, P.; Kumar, A.; Baumel-Alterzon, S.; Honig, L.B.; Gómez-Banoy, N.; Karakose, E.; Tanase, M.; Doridot, L.; Alvarsson, A.; Davenport, B.; Wang, P.; Lambertini, L.; Stanley, S.A.; Homann, D.; Stewart, A.F.; Lo, J.C.; Herman, M.A.; Garcia-Ocaña, A.; Scott, D.K. Maladaptive positive feedback production of ChREBPβ underlies glucotoxic β-cell failure. Nat. Commun., 2022, 13(1), 4423.
[http://dx.doi.org/10.1038/s41467-022-32162-x] [PMID: 35908073]
[13]
Nag, A.; Dhindsa, R.S.; Mitchell, J.; Vasavda, C.; Harper, A.R.; Vitsios, D.; Ahnmark, A.; Bilican, B.; Madeyski-Bengtson, K.; Zarrouki, B.; Zoghbi, A.W.; Wang, Q.; Smith, K.R.; Alegre-Díaz, J.; Kuri-Morales, P.; Berumen, J.; Tapia-Conyer, R.; Emberson, J.; Torres, J.M.; Collins, R.; Smith, D.M.; Challis, B.; Paul, D.S.; Bohlooly-Y, M.; Snowden, M.; Baker, D.; Fritsche-Danielson, R.; Pangalos, M.N.; Petrovski, S. Human genetics uncovers MAP3K15 as an obesity-independent therapeutic target for diabetes. Sci. Adv., 2022, 8(46), eadd5430.
[http://dx.doi.org/10.1126/sciadv.add5430] [PMID: 36383675]
[14]
Wang, K.; Zhang, Z.; Hang, J.; Liu, J.; Guo, F.; Ding, Y.; Li, M.; Nie, Q.; Lin, J.; Zhuo, Y.; Sun, L.; Luo, X.; Zhong, Q.; Ye, C.; Yun, C.; Zhang, Y.; Wang, J.; Bao, R.; Pang, Y.; Wang, G.; Gonzalez, F.J.; Lei, X.; Qiao, J.; Jiang, C. Microbial-host-isozyme analyses reveal microbial DPP4 as a potential antidiabetic target. Science, 2023, 381(6657), eadd5787.
[http://dx.doi.org/10.1126/science.add5787] [PMID: 37535747]
[15]
Hsu, J.T.; Jean, T.C.; Chan, M.A.; Ying, C. Differential display screening for specific gene expression induced by dietary nonsteroidal estrogen. Mol. Reprod. Dev., 1999, 52(2), 141-148.
[http://dx.doi.org/10.1002/(SICI)1098-2795(199902)52:2<141:AID-MRD4>3.0.CO;2-V] [PMID: 9890744]
[16]
McCoubrey, W.K., Jr; Cooklis, M.A.; Maines, M.D. The structure, organization and differential expression of the rat gene encoding biliverdin reductase. Gene, 1995, 160(2), 235-240.
[http://dx.doi.org/10.1016/0378-1119(95)00112-J] [PMID: 7642101]
[17]
Hatfield, G.W.; Hung, S.; Baldi, P. Differential analysis of DNA microarray gene expression data. Mol. Microbiol., 2003, 47(4), 871-877.
[http://dx.doi.org/10.1046/j.1365-2958.2003.03298.x] [PMID: 12581345]
[18]
Rapaport, F.; Khanin, R.; Liang, Y.; Pirun, M.; Krek, A.; Zumbo, P.; Mason, C.E.; Socci, N.D.; Betel, D. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol., 2013, 14(9), R95.
[http://dx.doi.org/10.1186/gb-2013-14-9-r95] [PMID: 24020486]
[19]
Finotello, F.; Di Camillo, B. Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis. Brief. Funct. Genomics, 2015, 14(2), 130-142.
[http://dx.doi.org/10.1093/bfgp/elu035] [PMID: 25240000]
[20]
Das, S.; Rai, S.N. SwarnSeq: An improved statistical approach for differential expression analysis of single-cell RNA-seq data. Genomics, 2021, 113(3), 1308-1324.
[http://dx.doi.org/10.1016/j.ygeno.2021.02.014] [PMID: 33662531]
[21]
Tusher, V.G.; Tibshirani, R.; Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci., 2001, 98(9), 5116-5121.
[http://dx.doi.org/10.1073/pnas.091062498] [PMID: 11309499]
[22]
Yuan, T.; Liu, G.; Ming, Z.; Yi, Z.; Zhou, C. A comparison: Three analysis methods for identifying differentially expressed genes. 2010 2nd International Conference on Signal Processing System., July 2010, vol.3, pp. 2165-2169.
[23]
Li, J.; Witten, D.M.; Johnstone, I.M.; Tibshirani, R. Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics, 2012, 13(3), 523-538.
[http://dx.doi.org/10.1093/biostatistics/kxr031] [PMID: 22003245]
[24]
Zhang, G.; Li, Q.; Chen, Q.; Su, S. Network pharmacology: A new approach for chinese herbal medicine research. Evid. Based Complement. Alternat. Med., 2013, 2013, 1-9.
[http://dx.doi.org/10.1155/2013/621423] [PMID: 23762149]
[25]
Zhang, A.; Sun, H.; Yang, B.; Wang, X. Predicting new molecular targets for rhein using network pharmacology. BMC Syst. Biol., 2012, 6(1), 20.
[http://dx.doi.org/10.1186/1752-0509-6-20] [PMID: 22433437]
[26]
Zhang, Y.; Mao, X.; Guo, Q.; Lin, N.; Li, S. Network pharmacology-based approaches capture essence of chinese herbal medicines. Chin. Herb. Med., 2016, 8(2), 107-116.
[http://dx.doi.org/10.1016/S1674-6384(16)60018-7]
[27]
Roy, S.; Martinez, D.; Platero, H.; Lane, T.; Werner-Washburne, M. Exploiting amino acid composition for predicting protein-protein interactions. PLoS One, 2009, 4(11), e7813.
[http://dx.doi.org/10.1371/journal.pone.0007813] [PMID: 19936254]
[28]
Wang, Y.; Wang, P.; Guo, Y.; Huang, S.; Chen, Y.; Xu, L. prPred: A predictor to identify plant resistance proteins by incorporating k-spaced amino acid (group) pairs. Front. Bioeng. Biotechnol., 2021, 8, 645520.
[http://dx.doi.org/10.3389/fbioe.2020.645520] [PMID: 33553134]
[29]
Saravanan, V.; Gautham, N. Harnessing computational biology for exact linear b-cell epitope prediction: A novel amino acid composition-based feature descriptor. OMICS, 2015, 19(10), 648-658.
[http://dx.doi.org/10.1089/omi.2015.0095] [PMID: 26406767]
[30]
Ding, Y.; Cai, Y.; Zhang, G.; Xu, W. The influence of dipeptide composition on protein thermostability. FEBS Lett., 2004, 569(1-3), 284-288.
[http://dx.doi.org/10.1016/j.febslet.2004.06.009] [PMID: 15225649]
[31]
Kabir, M.; Ahmad, S.; Iqbal, M.; Hayat, M. iNR-2L: A two-level sequence-based predictor developed via Chou’s 5-steps rule and general PseAAC for identifying nuclear receptors and their families. Genomics, 2020, 112(1), 276-285.
[http://dx.doi.org/10.1016/j.ygeno.2019.02.006] [PMID: 30779939]
[32]
Chen, Z.; Zhao, P.; Li, F.; Marquez-Lago, T.T.; Leier, A.; Revote, J.; Zhu, Y.; Powell, D.R.; Akutsu, T.; Webb, G.I.; Chou, K.C.; Smith, A.I.; Daly, R.J.; Li, J.; Song, J. iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data. Brief. Bioinform., 2020, 21(3), 1047-1057.
[http://dx.doi.org/10.1093/bib/bbz041] [PMID: 31067315]
[33]
Kong, Y.; Yu, T. forgeNet: A graph deep neural network model using tree-based ensemble classifiers for feature graph construction. Bioinformatics, 2020, 36(11), 3507-3515.
[http://dx.doi.org/10.1093/bioinformatics/btaa164] [PMID: 32163118]
[34]
Kong, Y.; Yu, T. A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data. Bioinformatics, 2018, 34(21), 3727-3737.
[http://dx.doi.org/10.1093/bioinformatics/bty429] [PMID: 29850911]
[35]
Sun, S.; Zhong, P.; Xiao, H.; Wang, R. Active learning with gaussian process classifier for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens., 2015, 53(4), 1746-1760.
[http://dx.doi.org/10.1109/TGRS.2014.2347343]
[36]
Xiao, G.; Cheng, Q.; Zhang, C. Detecting travel modes using rule-based classification system and gaussian process classifier. IEEE Access, 2019, 7, 116741-116752.
[http://dx.doi.org/10.1109/ACCESS.2019.2936443]
[37]
Rafe, V.; Hosseini, M.; Moghaddam, M.J.; Karimi, R. An efficient approach to breast cancer prediction based on neural network, adaboost and gaussian process. J. Med. Imaging Health Inform., 2015, 5(3), 533-538.
[http://dx.doi.org/10.1166/jmihi.2015.1420]
[38]
Zhao, G.Y.; Xu, Z.W.; Liu, J. Prediction method of seismic-induced sand liquefaction based on the Gauss Process classification. Chi. J. Geo. Haz. Con., 2019, 30(1), 93-99.
[39]
Díaz-Uriarte, R.; Alvarez de Andrés, S. Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 2006, 7(1), 3.
[http://dx.doi.org/10.1186/1471-2105-7-3] [PMID: 16398926]
[40]
Saunders, C.; Stitson, M.O.; Bottou, J.; Scholkopf, L.; Smola, B.A Support vector machine weston, royal holloway. Computer ence, 2002, 1(4), 1-28.
[http://dx.doi.org/10.1007/978-3-642-27733-7_299-3]
[41]
Collins, M.; Schapire, R.E.; Singer, Y. Logistic regression, adaboost and bregman distances. Mach. Learn., 2002, 48(123), 253-285.
[42]
Li, J.; Sun, L.; Zhang, Q.; Zhang, C. Application of native Bayes classifier to text classification. J. Harbin Eng. Uni., 2003, 24(1), 71-74.
[43]
Zhagparov, Z.; Buribayev, Z.; Joldasbayev, S.; Yerkosova, A.; Zhassuzak, M. Building a system for predicting the yield of grain crops based on machine learning using the xgbregressor algorithm. 2021IEEE International Conference on Smart Information Systems and Technologies (SIST), 28-30 April 2021Nur-Sultan, Kazakhstan, pp. 1-5.
[http://dx.doi.org/10.1109/SIST50301.2021.9465938]
[44]
Jiao, F.; Xu, J.; Yu, L.; Schuurmans, D. Protein fold recognition using the gradient boost algorithm. Computational Systems Bioinformatics, 2006, 43-53.
[45]
Hoerl, A.E.; Kennard, R.W. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 2000, 42(1), 80-86.
[http://dx.doi.org/10.1080/00401706.2000.10485983]
[46]
Gardner, W.A. Learning characteristics of stochastic-gradient-descent algorithms: A general study, analysis, and critique. Signal Processing, 1984, 6(2), 113-133.
[http://dx.doi.org/10.1016/0165-1684(84)90013-6]
[47]
Bao, W.; Cui, Q.; Chen, B.; Yang, B. Phage_UniR_LGBM: Phage virion proteins classification with unirep features and lightgbm model. Comput. Math. Methods Med., 2022, 2022, 1-8.
[http://dx.doi.org/10.1155/2022/9470683] [PMID: 35465015]
[48]
Kao, H.J.; Nguyen, V.N.; Huang, K.Y.; Chang, W.C.; Lee, T.Y. SuccSite: Incorporating amino acid composition and informative k-spaced amino acid pairs to identify protein succinylation sites. Genomics Proteomics Bioinformatics, 2020, 18(2), 208-219.
[http://dx.doi.org/10.1016/j.gpb.2018.10.010] [PMID: 32592791]
[49]
Barukab, O.; Ali, F.; Alghamdi, W.; Bassam, Y.; Afzal Khan, S. DBP-CNN: Deep learning-based prediction of DNA-binding proteins by coupling discrete cosine transform with two-dimensional convolutional neural network. Expert Syst. Appl., 2022, 197, 116729.
[http://dx.doi.org/10.1016/j.eswa.2022.116729]
[50]
Yang, L.; Gao, H.; Liu, Z.; Tang, L. Identification of phage virion proteins by using the g-gap tripeptide composition. Lett. Org. Chem., 2019, 16(4), 332-339.
[http://dx.doi.org/10.2174/1570178615666180910112813]

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