{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T11:06:22Z","timestamp":1791975982515,"version":"4.4.0"},"reference-count":87,"publisher":"American Association for the Advancement of Science (AAAS)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T00:00:00Z","timestamp":1645833600000},"content-version":"vor","delay-in-days":56,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020R1A2C2005612"],"award-info":[{"award-number":["2020R1A2C2005612"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017M3C7A1044816"],"award-info":[{"award-number":["NRF-2017M3C7A1044816"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["csbj.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Comput Struct Biotechnol J"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1016\/j.csbj.2022.02.026","type":"journal-article","created":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T02:41:29Z","timestamp":1646275289000},"page":"1208-1217","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":37,"title":["Identification of piRNA disease associations using deep learning"],"prefix":"10.34133","volume":"20","author":[{"given":"Syed Danish","family":"Ali","sequence":"first","affiliation":[{"name":"Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea"},{"name":"The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hilal","family":"Tayara","sequence":"additional","affiliation":[{"name":"School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kil To","family":"Chong","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea"},{"name":"Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2022,3,7]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"D. Meseure K.D. Alsibai Part 1: The piwi-pirna pathway is an immune-like surveillance process that controls genome integrity by silencing transposable elements in: Chromatin and Epigenetics IntechOpen 2018."},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature04917"},{"key":"e_1_3_3_4_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s42003-017-0001-7","article-title":"Non-coding rna fragments account for the majority of annotated pirnas expressed in somatic non-gonadal tissues","volume":"1","author":"Tosar J.P.","year":"2018","unstructured":"Tosar J.P., Rovira C. and Cayota A.: Non-coding rna fragments account for the majority of annotated pirnas expressed in somatic non-gonadal tissues. Commun Biol 2018; 1: 1.","journal-title":"Commun Biol"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrg2504"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0888-7543(03)00129-0"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrm2632"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-biochem-060614-034258"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature11502"},{"key":"e_1_3_3_10_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2164-15-555","article-title":"pirnaquest: searching the pirnaome for silencers","volume":"15","author":"Sarkar A.","year":"2014","unstructured":"Sarkar A., Maji R.K., Saha S. and Ghosh Z.: pirnaquest: searching the pirnaome for silencers. BMC Genom 2014; 15: 1.","journal-title":"BMC Genom"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1101\/gad.1456106"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature07415"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2009.03.040"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrm3089"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1093\/carcin\/bgx026"},{"key":"e_1_3_3_16_2","doi-asserted-by":"crossref","first-page":"381","DOI":"10.2119\/molmed.2014.00203","article-title":"Piwi-interacting rnas (pirnas) are dysregulated in renal cell carcinoma and associated with tumor metastasis and cancer-specific survival","volume":"21","author":"Li Y.","year":"2015","unstructured":"Li Y., Wu X., Gao H., Jin J.M., Li A.X., Kim Y.S., Pal S.K., Nelson R.A., Lau C.M., Guo C. et al: Piwi-interacting rnas (pirnas) are dysregulated in renal cell carcinoma and associated with tumor metastasis and cancer-specific survival. Mol Med 2015; 21: 381.","journal-title":"Mol Med"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.2217\/epi.15.37"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2017.05.020"},{"key":"e_1_3_3_19_2","first-page":"1","article-title":"The disease-related biological functions of piwi-interacting rnas (pirnas) and underlying molecular mechanisms","volume":"1","author":"Sun T.","year":"2019","unstructured":"Sun T. and Han X.: The disease-related biological functions of piwi-interacting rnas (pirnas) and underlying molecular mechanisms. ExRNA 2019; 1: 1.","journal-title":"ExRNA"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.plabm.2018.e00113"},{"key":"e_1_3_3_21_2","volume-title":"Seminars in Cancer Biology Elsevier","author":"Lin Y.","year":"2020","unstructured":"Lin Y., Zheng J. and Lin D.: Piwi-interacting rnas in human cancer. In: Seminars in Cancer Biology Elsevier. : 2020."},{"key":"e_1_3_3_22_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40478-018-0561-x","article-title":"Sporadic parkinson\u2019s disease derived neuronal cells show disease-specific mrna and small rna signatures with abundant deregulation of pirnas","volume":"6","author":"Schulze M.","year":"2018","unstructured":"Schulze M., Sommer A., Pl\u00f6tz S., Farrell M., Winner B., Grosch J., Winkler J. and Riemenschneider M.J.: Sporadic parkinson\u2019s disease derived neuronal cells show disease-specific mrna and small rna signatures with abundant deregulation of pirnas. Acta neuropathologica communications 2018; 6: 1.","journal-title":"Acta neuropathologica communications"},{"key":"e_1_3_3_23_2","first-page":"1","article-title":"Piwi-interacting rnas (pirnas) as potential biomarkers and therapeutic targets for cardiovascular diseases","author":"Li M.","year":"2020","unstructured":"Li M., Yang Y., Wang Z., Zong T., Fu X., Aung L.H.H., Wang K., Wang J.-X. and Yu T.: Piwi-interacting rnas (pirnas) as potential biomarkers and therapeutic targets for cardiovascular diseases. Angiogenesis 2020; : 1.","journal-title":"Angiogenesis"},{"key":"e_1_3_3_24_2","doi-asserted-by":"crossref","unstructured":"J. Roy A. Sarkar S. Parida Z. Ghosh B. Mallick Small rna sequencing revealed dysregulated pirnas in alzheimer\u2019s disease and their probable role in pathogenesis Molecular BioSystems 13 (3) 565\u2013576.","DOI":"10.1039\/C6MB00699J"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cca.2011.05.015"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1111\/cas.13300"},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkm696"},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkv1265"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gky1043"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gky277"},{"key":"e_1_3_3_31_2","unstructured":"S.D. Ali W. Alam H. Tayara K. Chong Identification of functional pirnas using a convolutional neural network IEEE\/ACM Transactions on Computational Biology and Bioinformatics."},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12539-021-00428-5"},{"key":"e_1_3_3_33_2","doi-asserted-by":"crossref","DOI":"10.1093\/database\/baz052","article-title":"pirdisease v1. 0: a manually curated database for pirna associated diseases","author":"Muhammad A.","year":"2019","unstructured":"Muhammad A., Waheed R., Khan N.A., Jiang H. and Song X.: pirdisease v1. 0: a manually curated database for pirna associated diseases. Database 2019; .","journal-title":"Database"},{"key":"e_1_3_3_34_2","doi-asserted-by":"crossref","first-page":"bbaa058","DOI":"10.1093\/bib\/bbaa058","article-title":"ipidi-pul: identifying piwi-interacting rna-disease associations based on positive unlabeled learning","volume":"22","author":"Wei H.","year":"2021","unstructured":"Wei H., Xu Y. and Liu B.: ipidi-pul: identifying piwi-interacting rna-disease associations based on positive unlabeled learning. Briefings Bioinform 2021; 22: bbaa058.","journal-title":"Briefings Bioinform"},{"key":"e_1_3_3_35_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiolchem.2020.107361","article-title":"ipida-shn: Identification of piwi-interacting rna-disease associations by selecting high quality negative samples","volume":"88","author":"Wei H.","year":"2020","unstructured":"Wei H., Ding Y. and Liu B.: ipida-shn: Identification of piwi-interacting rna-disease associations by selecting high quality negative samples. Comput Biol Chem 2020; 88: 107361.","journal-title":"Comput Biol Chem"},{"key":"e_1_3_3_36_2","first-page":"227","article-title":"Predicting human disease-associated pirnas based on multi-source information and random forest","author":"Zheng K.","year":"2020","unstructured":"Zheng K., You Z.-H., Wang L., Li H.-Y. and Ji B.-Y.: Predicting human disease-associated pirnas based on multi-source information and random forest. International Conference on Intelligent Computing, Springer 2020; : 227.","journal-title":"International Conference on Intelligent Computing, Springer"},{"key":"e_1_3_3_37_2","first-page":"239","article-title":"Inferring disease-associated piwi-interacting rnas via graph attention networks","author":"Zheng K.","year":"2020","unstructured":"Zheng K., You Z.-H., Wang L., Wong L. and Chen Z.-H.: Inferring disease-associated piwi-interacting rnas via graph attention networks. International Conference on Intelligent Computing, Springer 2020; : 239.","journal-title":"International Conference on Intelligent Computing, Springer"},{"key":"e_1_3_3_38_2","unstructured":"K. Zheng Z.-H. You L. Wang L. Wong Z.-H. Zhan Sprda: a matrix completion approach based on the structural perturbation to infer disease-associated piwi-interacting rnas bioRxiv."},{"key":"e_1_3_3_39_2","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.patrec.2013.06.010","article-title":"A bagging svm to learn from positive and unlabeled examples","volume":"37","author":"Mordelet F.","year":"2014","unstructured":"Mordelet F. and Vert J.-P.: A bagging svm to learn from positive and unlabeled examples. Pattern Recogn Lett 2014; 37: 201.","journal-title":"Pattern Recogn Lett"},{"key":"e_1_3_3_40_2","first-page":"179","volume-title":"Third IEEE International Conference on Data Mining IEEE","author":"Liu B.","year":"2003","unstructured":"Liu B., Dai Y., Li X., Lee W.S. and Yu P.S.: Building text classifiers using positive and unlabeled examples. In: Third IEEE International Conference on Data Mining IEEE. : 2003: 179."},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_3_3_42_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-020-01297-6","article-title":"Combining structured and unstructured data for predictive models: a deep learning approach","volume":"20","author":"Zhang D.","year":"2020","unstructured":"Zhang D., Yin C., Zeng J., Yuan X. and Zhang P.: Combining structured and unstructured data for predictive models: a deep learning approach. BMC Med Inform Decis Making 2020; 20: 1.","journal-title":"BMC Med Inform Decis Making"},{"key":"e_1_3_3_43_2","first-page":"2493","article-title":"Natural language processing (almost) from scratch","volume":"12","author":"Collobert R.","year":"2011","unstructured":"Collobert R., Weston J., Bottou L., Karlen M., Kavukcuoglu K. and Kuksa P.: Natural language processing (almost) from scratch. J Mach Learn Res 2011; 12: 2493.","journal-title":"J Mach Learn Res"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2205597"},{"key":"e_1_3_3_45_2","doi-asserted-by":"crossref","first-page":"2220","DOI":"10.1109\/ACCESS.2017.2782260","article-title":"Vehicle detection and counting in high-resolution aerial images using convolutional regression neural network","volume":"6","author":"Tayara H.","year":"2017","unstructured":"Tayara H., Soo K.G. and Chong K.T.: Vehicle detection and counting in high-resolution aerial images using convolutional regression neural network. IEEE Access 2017; 6: 2220.","journal-title":"IEEE Access"},{"key":"e_1_3_3_46_2","doi-asserted-by":"crossref","first-page":"189","DOI":"10.3389\/fpls.2021.591333","article-title":"Dam: Hierarchical adaptive feature selection using convolution encoder decoder network for strawberry segmentation","volume":"12","author":"Ilyas T.","year":"2021","unstructured":"Ilyas T., Umraiz M., Khan A. and Kim H.: Dam: Hierarchical adaptive feature selection using convolution encoder decoder network for strawberry segmentation. Front Plant Sci 2021; 12: 189.","journal-title":"Front Plant Sci"},{"key":"e_1_3_3_47_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41438-019-0151-5","article-title":"Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production","volume":"6","author":"Bauer A.","year":"2019","unstructured":"Bauer A., Bostrom A.G., Ball J., Applegate C., Cheng T., Laycock S., Rojas S.M., Kirwan J. and Zhou J.: Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production. Horticulture Res 2019; 6: 1.","journal-title":"Horticulture Res"},{"key":"e_1_3_3_48_2","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.3390\/electronics9101602","article-title":"Ced-net: crops and weeds segmentation for smart farming using a small cascaded encoder-decoder architecture","volume":"9","author":"Khan A.","year":"2020","unstructured":"Khan A., Ilyas T., Umraiz M., Mannan Z.I. and Kim H.: Ced-net: crops and weeds segmentation for smart farming using a small cascaded encoder-decoder architecture. Electronics 2020; 9: 1602.","journal-title":"Electronics"},{"key":"e_1_3_3_49_2","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1021\/ci500747n","article-title":"Deep neural nets as a method for quantitative structure\u2013activity relationships","volume":"55","author":"Ma J.","year":"2015","unstructured":"Ma J., Sheridan R.P., Liaw A., Dahl G.E. and Svetnik V.: Deep neural nets as a method for quantitative structure\u2013activity relationships. J Chem Inform Modeling 2015; 55: 263.","journal-title":"J Chem Inform Modeling"},{"key":"e_1_3_3_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3049142"},{"key":"e_1_3_3_51_2","doi-asserted-by":"crossref","first-page":"W140","DOI":"10.1093\/nar\/gkaa275","article-title":"Musitedeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization","volume":"48","author":"Wang D.","year":"2020","unstructured":"Wang D., Liu D., Yuchi J., He F., Jiang Y., Cai S., Li J. and Xu D.: Musitedeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization. Nucl Acids Res 2020; 48: W140.","journal-title":"Nucl Acids Res"},{"key":"e_1_3_3_52_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12918-017-0390-8","article-title":"Selecting high-quality negative samples for effectively predicting protein-rna interactions","volume":"11","author":"Cheng Z.","year":"2017","unstructured":"Cheng Z., Huang K., Wang Y., Liu H., Guan J. and Zhou S.: Selecting high-quality negative samples for effectively predicting protein-rna interactions. BMC Syst Biol 2017; 11: 1.","journal-title":"BMC Syst Biol"},{"key":"e_1_3_3_53_2","unstructured":"H. Tayara K. Chong Improved predicting of the sequence specificities of rna binding proteins by deep learning IEEE\/ACM transactions on computational biology and bioinformatics."},{"key":"e_1_3_3_54_2","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.omtn.2019.03.010","article-title":"ipseu-cnn: identifying rna pseudouridine sites using convolutional neural networks","volume":"16","author":"Tahir M.","year":"2019","unstructured":"Tahir M., Tayara H. and Chong K.T.: ipseu-cnn: identifying rna pseudouridine sites using convolutional neural networks. Mol Therapy-Nucl Acids 2019; 16: 463.","journal-title":"Mol Therapy-Nucl Acids"},{"key":"e_1_3_3_55_2","doi-asserted-by":"crossref","first-page":"8491","DOI":"10.1109\/ACCESS.2021.3049146","article-title":"Prediction of rna 5-hydroxymethylcytosine modifications using deep learning","volume":"9","author":"Ali S.D.","year":"2021","unstructured":"Ali S.D., Kim J.H., Tayara H. and Chong K.: Prediction of rna 5-hydroxymethylcytosine modifications using deep learning. IEEE Access 2021; 9: 8491.","journal-title":"IEEE Access"},{"key":"e_1_3_3_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3002995"},{"key":"e_1_3_3_57_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx257"},{"key":"e_1_3_3_58_2","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.3390\/genes11121529","article-title":"pcpromoter-cnn: A cnn-based prediction and classification of promoters","volume":"11","author":"Shujaat M.","year":"2020","unstructured":"Shujaat M., Wahab A., Tayara H. and Chong K.T.: pcpromoter-cnn: A cnn-based prediction and classification of promoters. Genes 2020; 11: 1529.","journal-title":"Genes"},{"key":"e_1_3_3_59_2","first-page":"213","volume-title":"2021 International Conference on Artificial Intelligence (ICAI) IEEE","author":"Ali S.D.","year":"2021","unstructured":"Ali S.D. and Chong K.T.: Identification of human promoter using convolutional neural network. In: 2021 International Conference on Artificial Intelligence (ICAI) IEEE. : 2021: 213."},{"key":"e_1_3_3_60_2","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.3389\/fgene.2019.01071","article-title":"Snnrice6ma: a deep learning method for predicting dna n6-methyladenine sites in rice genome","volume":"10","author":"Yu H.","year":"2019","unstructured":"Yu H. and Dai Z.: Snnrice6ma: a deep learning method for predicting dna n6-methyladenine sites in rice genome. Front Genet 2019; 10: 1071.","journal-title":"Front Genet"},{"key":"e_1_3_3_61_2","doi-asserted-by":"crossref","first-page":"178577","DOI":"10.1109\/ACCESS.2019.2958618","article-title":"iim-cnn: Intelligent identifier of 6ma sites on different species by using convolution neural network","volume":"7","author":"Wahab A.","year":"2019","unstructured":"Wahab A., Ali S.D., Tayara H. and Chong K.T.: iim-cnn: Intelligent identifier of 6ma sites on different species by using convolution neural network. IEEE Access 2019; 7: 178577.","journal-title":"IEEE Access"},{"key":"e_1_3_3_62_2","unstructured":"Z. Abbas H. Tayara K. Chong Zayyunet a unified deep learning model for the identification of epigenetic modifications using raw genomic sequences IEEE\/ACM Transactions on Computational Biology and Bioinformatics."},{"key":"e_1_3_3_63_2","doi-asserted-by":"crossref","first-page":"898","DOI":"10.3390\/genes11080898","article-title":"Dna6ma-mint: Dna-6ma modification identification neural tool","volume":"11","author":"Rehman M.U.","year":"2020","unstructured":"Rehman M.U. and Chong K.T.: Dna6ma-mint: Dna-6ma modification identification neural tool. Genes 2020; 11: 898.","journal-title":"Genes"},{"key":"e_1_3_3_64_2","unstructured":"G. Yu Y. Yang Y. Yan M. Guo X. Zhang J. Wang Deepida: predicting isoform-disease associations by data fusion and deep neural networks IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"e_1_3_3_65_2","doi-asserted-by":"crossref","first-page":"5656","DOI":"10.1093\/bioinformatics\/btaa1077","article-title":"Improving circrna\u2013disease association prediction by sequence and ontology representations with convolutional and recurrent neural networks","volume":"36","author":"Lu C.","year":"2020","unstructured":"Lu C., Zeng M., Wu F.-X., Li M. and Wang J.: Improving circrna\u2013disease association prediction by sequence and ontology representations with convolutional and recurrent neural networks. Bioinformatics 2020; 36: 5656.","journal-title":"Bioinformatics"},{"key":"e_1_3_3_66_2","unstructured":"M. Zeng C. Lu Z. Fei F. Wu Y. Li J. Wang M. Li Dmflda: A deep learning framework for predicting incrna\u2013disease associations IEEE\/ACM transactions on computational biology and bioinformatics."},{"key":"e_1_3_3_67_2","first-page":"483","article-title":"Deep learning for imbalanced multimedia data classification, in, IEEE international symposium on multimedia (ISM)","volume":"2015","author":"Yan Y.","year":"2015","unstructured":"Yan Y., Chen M., Shyu M.-L. and Chen S.-C.: Deep learning for imbalanced multimedia data classification, in, IEEE international symposium on multimedia (ISM). IEEE 2015; 2015: 483.","journal-title":"IEEE"},{"key":"e_1_3_3_68_2","doi-asserted-by":"crossref","first-page":"919","DOI":"10.2466\/pms.1998.87.3.919","article-title":"Factorial designs and dummy coding","volume":"87","author":"Berry K.J.","year":"1998","unstructured":"Berry K.J., Mielke P.W. and Iyer H.K.: Factorial designs and dummy coding. Perceptual Motor Skills 1998; 87: 919.","journal-title":"Perceptual Motor Skills"},{"key":"e_1_3_3_69_2","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1109\/TCBB.2013.146","article-title":"Improved and promising identification of human micrornas by incorporating a high-quality negative set","volume":"11","author":"Wei L.","year":"2014","unstructured":"Wei L., Liao M., Gao Y., Ji R., He Z. and Zou Q.: Improved and promising identification of human micrornas by incorporating a high-quality negative set. IEEE\/ACM Trans Comput Biol Bioinform (TCBB) 2014; 11: 192.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform (TCBB)"},{"key":"e_1_3_3_70_2","doi-asserted-by":"crossref","first-page":"2640","DOI":"10.1093\/bioinformatics\/bts504","article-title":"Positive-unlabeled learning for disease gene identification","volume":"28","author":"Yang P.","year":"2012","unstructured":"Yang P., Li X.-L., Mei J.-P., Kwoh C.-K. and Ng S.-K.: Positive-unlabeled learning for disease gene identification. Bioinformatics 2012; 28: 2640.","journal-title":"Bioinformatics"},{"key":"e_1_3_3_71_2","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1007\/s10994-020-05877-5","article-title":"Learning from positive and unlabeled data: A survey","volume":"109","author":"Bekker J.","year":"2020","unstructured":"Bekker J. and Davis J.: Learning from positive and unlabeled data: A survey. Mach Learn 2020; 109: 719.","journal-title":"Mach Learn"},{"key":"e_1_3_3_72_2","first-page":"761","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Shrivastava A.","year":"2016","unstructured":"Shrivastava A., Gupta A. and Girshick R.: Training region-based object detectors with online hard example mining, in. In: Proceedings of the IEEE conference on computer vision and pattern recognition. : 2016: 761."},{"key":"e_1_3_3_73_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2020.07.010"},{"key":"e_1_3_3_74_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx496"},{"key":"e_1_3_3_75_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ab.2007.07.006","article-title":"Recent progress in protein subcellular location prediction","volume":"370","author":"Chou K.-C.","year":"2007","unstructured":"Chou K.-C. and Shen H.-B.: Recent progress in protein subcellular location prediction. Anal Biochem 2007; 370: 1.","journal-title":"Anal Biochem"},{"key":"e_1_3_3_76_2","doi-asserted-by":"crossref","unstructured":"Z.-H. You Y.-K. Lei L. Zhu J. Xia B. Wang Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis in: BMC bioinformatics Vol. 14 Springer 2013 pp. 1\u201311.","DOI":"10.1186\/1471-2105-14-S8-S10"},{"key":"e_1_3_3_77_2","doi-asserted-by":"publisher","DOI":"10.1101\/gr.200535.115"},{"key":"e_1_3_3_78_2","volume-title":"Rectified linear units improve restricted boltzmann machines","author":"Nair V.","year":"2010","unstructured":"Nair V. and Hinton G.E.: Rectified linear units improve restricted boltzmann machines. in: Icml 2010."},{"key":"e_1_3_3_79_2","unstructured":"M. Tsang D. Cheng Y. Liu Detecting statistical interactions from neural network weights arXiv preprint arXiv:1705.04977."},{"key":"e_1_3_3_80_2","first-page":"448","volume-title":"International conference on machine learning PMLR","author":"Ioffe S.","year":"2015","unstructured":"Ioffe S. and Szegedy C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning PMLR. : 2015: 448."},{"key":"e_1_3_3_81_2","first-page":"3","volume-title":"Proceedings of the European conference on computer vision (ECCV)","author":"Wu Y.","year":"2018","unstructured":"Wu Y. and He K.: Group normalization. In: Proceedings of the European conference on computer vision (ECCV). : 2018: 3."},{"key":"e_1_3_3_82_2","unstructured":"Goodfellow I. Bengio Y. Courville A. and Bengio Y.: : MIT press Cambridge 2016."},{"key":"e_1_3_3_83_2","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava N.","year":"2014","unstructured":"Srivastava N., Hinton G., Krizhevsky A., Sutskever I. and Salakhutdinov R.: Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 2014; 15: 1929.","journal-title":"J Mach Learn Res"},{"key":"e_1_3_3_84_2","unstructured":"D.P. Kingma J. Ba Adam: A method for stochastic optimization arXiv preprint arXiv:1412.6980."},{"key":"e_1_3_3_85_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","article-title":"A tutorial on the cross-entropy method","volume":"134","author":"De Boer P.-T.","year":"2005","unstructured":"De Boer P.-T., Kroese D.P., Mannor S. and Rubinstein R.Y.: A tutorial on the cross-entropy method. Ann Oper Res 2005; 134: 19.","journal-title":"Ann Oper Res"},{"key":"e_1_3_3_86_2","doi-asserted-by":"crossref","unstructured":"G. Tsoumakas I. Katakis I. Vlahavas Mining multi-label data in: Data mining and knowledge discovery handbook Springer pp. 667\u2013685.","DOI":"10.1007\/978-0-387-09823-4_34"},{"key":"e_1_3_3_87_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.omtn.2020.05.023"},{"key":"e_1_3_3_88_2","doi-asserted-by":"publisher","DOI":"10.1093\/carcin\/bgv105"}],"container-title":["Computational and Structural Biotechnology Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2001037022000666?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2001037022000666?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/spj.science.org\/doi\/pdf\/10.1016\/j.csbj.2022.02.026","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T12:58:59Z","timestamp":1779022739000},"score":1,"resource":{"primary":{"URL":"https:\/\/spj.science.org\/doi\/10.1016\/j.csbj.2022.02.026"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":87,"alternative-id":["10.1016\/j.csbj.2022.02.026"],"URL":"https:\/\/doi.org\/10.1016\/j.csbj.2022.02.026","relation":{},"ISSN":["2001-0370"],"issn-type":[{"value":"2001-0370","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Identification of piRNA disease associations using deep learning","name":"articletitle","label":"Article Title"},{"value":"Computational and Structural Biotechnology Journal","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.csbj.2022.02.026","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.","name":"copyright","label":"Copyright"}]}}