{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T05:34:40Z","timestamp":1731648880620,"version":"3.28.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"36","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19010-4","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T05:01:31Z","timestamp":1712898091000},"page":"84251-84273","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Triplet encoded sequence based membrane protein classification using BiLSTM"],"prefix":"10.1007","volume":"83","author":[{"given":"S.","family":"Gomathi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. Nithish","family":"Ram","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N. Ani Brown","family":"Mary","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"key":"19010_CR1","doi-asserted-by":"publisher","first-page":"2259","DOI":"10.1007\/s11033-019-04680-3","volume":"46","author":"K Jayapriya","year":"2019","unstructured":"Jayapriya K, Mary NAB (2019) Employing a novel 2-gram subgroup intra pattern (2GSIP) with stacked auto encoder for membrane protein classification. Mol Biol Rep 46:2259\u20132272. https:\/\/doi.org\/10.1007\/s11033-019-04680-3","journal-title":"Mol Biol Rep"},{"key":"19010_CR2","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.ymeth.2018.04.018","volume":"147","author":"Nopnithi Thonghin","year":"2018","unstructured":"Thonghin Nopnithi, VasileiosKargas Jack Clews, Robert, (2018) Cryo-electron microscopy of membrane proteins. Journal of Methods 147:176\u2013186","journal-title":"Journal of Methods"},{"key":"19010_CR3","doi-asserted-by":"crossref","unstructured":"Golmohammadi SK, Kurgan L, Crowley B and Reformat M (2007) Classification of cell membrane. FBIT 07 Proceedings of the 2007 Frontiers in the convergence of Bioscience and information technology, pp.153-158","DOI":"10.1109\/FBIT.2007.21"},{"key":"19010_CR4","first-page":"137","volume":"34","author":"C Chou","year":"1999","unstructured":"Chou C, Elrod DW (1999) Prediction of membrane protein types and subcellular locations, Proteins: Proteins: Structure. Function, and Genetics 34:137\u201353","journal-title":"Function, and Genetics"},{"issue":"3","key":"19010_CR5","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1016\/j.bbrc.2005.08.160","volume":"336","author":"H Liu","year":"2005","unstructured":"Liu H, Wang M, Chou K-C (2005) Low-frequency Fourier spectrum for predicting membrane protein types. Biochemical and Biophysical Research Communications 336(3):737\u2013739","journal-title":"Biochemical and Biophysical Research Communications"},{"key":"19010_CR6","doi-asserted-by":"publisher","unstructured":"Sandaruwan PD, Wannige CT\u00a0 An improved deep learning model for hierarchical classification of protein families. PLoS ONE 16(10): e0258625. https:\/\/doi.org\/10.1371\/journal.pone.0258625","DOI":"10.1371\/journal.pone.0258625"},{"key":"19010_CR7","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.jtbi.2015.07.034","volume":"384","author":"Farman Ali","year":"2005","unstructured":"Ali Farman, Haya Maqsood (2005) Classification of membrane protein types using Voting Feature Interval in combination with Chou\u05f3s Pseudo Amino Acid Composition. Journal of Theoretical Biology 384:78\u201383","journal-title":"Journal of Theoretical Biology"},{"key":"19010_CR8","unstructured":"Nazar Z, El-Hajj W (2010) Predicting membrane protein type using inter-domain linker knowledge. In: BIOCOMP, pp 209\u2013214"},{"key":"19010_CR9","doi-asserted-by":"publisher","first-page":"D440","DOI":"10.1093\/nar\/gkx1109","volume":"46","author":"G Pandy-Szekeres","year":"2018","unstructured":"Pandy-Szekeres G, Munk C, Tsonkov TM, Mordalski S, Harpsoe K, Hauser AS, Bojarski AJ, Gloriam DE (2018) GPCRdb in 2018: Adding GPCR structure models and ligands. Nucleic Acids Res. 46:D440\u2013D446","journal-title":"Nucleic Acids Res."},{"key":"19010_CR10","doi-asserted-by":"publisher","first-page":"2852","DOI":"10.3390\/s21082852","volume":"21","author":"PN Srinivasu","year":"2021","unstructured":"Srinivasu PN, SivaSai JG, Ijaz MF, Bhoi AK, Kim W, Kang JJ (2021) Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors 21:2852. https:\/\/doi.org\/10.3390\/s21082852","journal-title":"Sensors"},{"issue":"606","key":"19010_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ab.2020.113845","volume":"1","author":"AS Alphonse","year":"2020","unstructured":"Alphonse AS, Mary NA, Starvin MS (2020) Classification of membrane protein using Tetra Peptide Pattern. Analytical Biochemistry. 1(606)","journal-title":"Analytical Biochemistry."},{"key":"19010_CR12","doi-asserted-by":"publisher","unstructured":"Ani Brown Mary N, Robert Singh A,\u00a0 Athisayamani S (2020) Banana leaf diseased image classification using novel HEAP auto encoder (HAE) deep learning. Multimed Tools Appl 79, 30601\u201330613. https:\/\/doi.org\/10.1007\/s11042-020-09521-1","DOI":"10.1007\/s11042-020-09521-1"},{"key":"19010_CR13","doi-asserted-by":"publisher","unstructured":"Ani Brown Mary N, Dejey Dharma Coral reef image\/video classification employing novel octa-angled pattern for triangular sub region and pulse coupled convolutional neural network (PCCNN). Multimed Tools Appl 77, 31545\u201331579 (2018). https:\/\/doi.org\/10.1007\/s11042-018-6148-5","DOI":"10.1007\/s11042-018-6148-5"},{"key":"19010_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-021-03612-z","author":"Y Kumar","year":"2022","unstructured":"Kumar Y, Koul A, Singla R et al (2022) Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Human Comput. https:\/\/doi.org\/10.1007\/s12652-021-03612-z","journal-title":"J Ambient Intell Human Comput"},{"key":"19010_CR15","doi-asserted-by":"publisher","first-page":"2988","DOI":"10.3390\/s22082988","volume":"22","author":"A Vulli","year":"2022","unstructured":"Vulli A, Srinivasu PN, Sashank MSK, Shafi J, Choi J, Ijaz MF (2022) Fine-Tuned DenseNet-169 for breast cancer metastasis prediction using FastAI and 1-Cycle Policy. Sensors 22:2988","journal-title":"Sensors"},{"key":"19010_CR16","doi-asserted-by":"publisher","unstructured":"Srinivasu PN, Ijaz MF, Shafi J & Wozniak M, Radha S (2022) 6G driven fast computational networking framework for healthcare applications. In: IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2022.3203061","DOI":"10.1109\/ACCESS.2022.3203061"},{"key":"19010_CR17","doi-asserted-by":"crossref","unstructured":"Kumar M, Verma K, Kumar A, Ijaz MF, Rawat DB (2022) ANAF-IoMT: a novel architectural framework for IoMT enabled smart healthcare system by enhancing security based on RECC-VC. IEEE Trans Industrial Inform","DOI":"10.1109\/TII.2022.3181614"},{"issue":"9","key":"19010_CR18","doi-asserted-by":"publisher","first-page":"3449","DOI":"10.3390\/s22093449","volume":"22","author":"NR Pradhan","year":"2022","unstructured":"Pradhan NR, Singh AP, Verma S, Kavita Kaur N, Roy DS, Shafi J, Wozniak M, Ijaz MF (2022) A novel blockchain-based healthcare system design and performance benchmarking on a multi-hosted testbed. Sensors 22(9):3449","journal-title":"Sensors"},{"key":"19010_CR19","doi-asserted-by":"publisher","unstructured":"Ali S, El-Sappagh S, Ali F, Imran M, Abuhmed T (2022) Multitask deep learning for cost-effective prediction of patient's length of stay and readmission state using multimodal physical activity sensory data. IEEE J Biomed Health Inform. https:\/\/doi.org\/10.1109\/JBHI.2022.3202178","DOI":"10.1109\/JBHI.2022.3202178"},{"issue":"5","key":"19010_CR20","doi-asserted-by":"publisher","first-page":"3603","DOI":"10.1007\/s00521-021-06631-1","volume":"34","author":"N El-Rashidy","year":"2022","unstructured":"El-Rashidy N, Abuhmed T, Alarabi L, El-Bakry HM, Abdelrazek S, Ali F, El-Sappagh S (2022) Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning. Neural Comput Appl 34(5):3603\u20133632","journal-title":"Neural Comput Appl"},{"key":"19010_CR21","doi-asserted-by":"publisher","unstructured":"Parashar J, Kushwah VS, Rai M (2023) Determination Human Behavior Prediction Supported by Cognitive Computing-Based Neural Network. In: Kumar R, Verma AK, Sharma TK, Verma OP, Sharma S. (eds) Soft Computing: Theories and Applications. Lecture Notes in Networks and Systems, vol 627. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-19-9858-4_36.","DOI":"10.1007\/978-981-19-9858-4_36"},{"key":"19010_CR22","doi-asserted-by":"publisher","unstructured":"Dey\u00a0RK, Das AK (2023) Modified term frequency-inverse document frequency based deep hybrid framework for sentiment analysis. Multimed Tools Appl. 82:32967-32990.https:\/\/doi.org\/10.1007\/s11042-023-14653-1","DOI":"10.1007\/s11042-023-14653-1"},{"key":"19010_CR23","doi-asserted-by":"publisher","unstructured":"Dey RK, Das AK (2022) A Simple Strategy for Handling 'NOT' Can Improve the Performance of Sentiment Analysis. In: Das AK, Nayak J, Naik B, Vimal S, Pelusi D (eds) Computational Intelligence in Pattern Recognition. CIPR 2022. Lecture Notes in Networks and Systems, vol 480. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-19-3089-8_25","DOI":"10.1007\/978-981-19-3089-8_25"},{"issue":"1","key":"19010_CR24","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1002\/(SICI)1097-0134(19990101)34:1<137::AID-PROT11>3.0.CO;2-O","volume":"34","author":"KC Chou","year":"1999","unstructured":"Chou KC, Elrod DW (1999) Prediction of membrane protein types and subcellular locations, Proteins: Struct. Funct. Bioinfor. 34(1):137\u2013153","journal-title":"Funct. Bioinfor."},{"issue":"2","key":"19010_CR25","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.bbrc.2007.06.027","volume":"360","author":"KC Chou","year":"2007","unstructured":"Chou KC, Shen HB (2007) MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem. Biophys. Res. Commun. 360(2):339\u2013345","journal-title":"Biochem. Biophys. Res. Commun."},{"issue":"3","key":"19010_CR26","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1002\/prot.1035","volume":"43","author":"KC Chou","year":"2001","unstructured":"Chou KC (2001) Prediction of protein cellular attributes using pseudo-amino acid composition, Proteins: Struct. Funct. Bioinfor. 43(3):246\u2013255","journal-title":"Funct. Bioinfor."},{"issue":"4","key":"19010_CR27","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1109\/TCBB.2015.2474407","volume":"13","author":"S Wan","year":"2015","unstructured":"Wan S, Mak MW, Kung SY (2015) Mem-mEN: predicting multi-functional types of membrane proteins by interpretable elastic nets. IEEE ACM Trans. Comput. Biol. Bioinf 13(4):706\u2013718","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinf"},{"key":"19010_CR28","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.jtbi.2013.11.017","volume":"344","author":"Guo-Sheng Han","year":"2014","unstructured":"Han Guo-Sheng, Zu-Guo Yu, Anh Vo (2014) A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou\u2019s PseAAC. Journal of Theoretical Biology 344:31\u201339","journal-title":"Journal of Theoretical Biology"},{"key":"19010_CR29","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.dib.2016.05.024","volume":"8","author":"S Wan","year":"2016","unstructured":"Wan S, Mak MW, Kung SY (2016) Benchmark data for identifying multi-functional types of membrane proteins. Data. Brief. 8:105\u2013107","journal-title":"Data. Brief."},{"issue":"25","key":"19010_CR30","first-page":"1","volume":"20","author":"L Guo","year":"2019","unstructured":"Guo L, Wang S, Li M, Cao Z (2019) Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning. BMC Bioinf. 20(25):1\u20137","journal-title":"BMC Bioinf."},{"issue":"383","key":"19010_CR31","first-page":"257","volume":"28","author":"H Wang","year":"2020","unstructured":"Wang H, Ding Y, Tang J, Guo F (2020) Identification of membrane protein types via multivariate information fusion with Hilbert-Schmidt independence criterion. Neurocomputing. 28(383):257\u201369","journal-title":"Neurocomputing."},{"issue":"7","key":"19010_CR32","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1016\/j.cell.2012.04.012","volume":"149","author":"TA Hopf","year":"2012","unstructured":"Hopf TA, Colwell LJ, Sheridan R, Rost B, Sander C, Marks DS (2012) Three-dimensional structures of membrane proteins from genomic sequencing. Cell. 149(7):1607\u201321","journal-title":"Cell."},{"key":"19010_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2022.107680","volume":"6","author":"Y He","year":"2022","unstructured":"He Y, Wang S (2022) SE-BLTCNN: A Channel Attention Adapted Deep Learning Model Based on PSSM for Membrane Protein Classification. Computational Biology and Chemistry. 6","journal-title":"Computational Biology and Chemistry."},{"issue":"2","key":"19010_CR34","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.jtbi.2009.09.027","volume":"262","author":"T Wang","year":"2010","unstructured":"Wang T, Xia T, Hu XM (2010) Geometry preserving projections algorithm for predicting membrane protein types. J. Theor. Biol. 262(2):208\u2013213","journal-title":"J. Theor. Biol."},{"issue":"1","key":"19010_CR35","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/1472-6807-2-9","volume":"2","author":"S Anishetty","year":"2002","unstructured":"Anishetty S, Pennathur G, Anishetty R (2002) Tripeptide analysis of protein structures. BMC Struct. Biol. 2(1):9","journal-title":"BMC Struct. Biol."},{"issue":"4","key":"19010_CR36","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1016\/j.jtbi.2003.08.015","volume":"226","author":"YD Cai","year":"2004","unstructured":"Cai YD, Ricardo PW, Jen CH, Chou KC (2004) Application of SVM to predict membrane protein types. J. Theor. Biol. 226(4):373\u2013376","journal-title":"J. Theor. Biol."},{"issue":"1","key":"19010_CR37","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.jtbi.2004.07.023","volume":"232","author":"M Wang","year":"2005","unstructured":"Wang M, Yang J, Xu ZJ, Chou KC (2005) SLLE for predicting membrane protein types. Journal of Theoretical Biology. 232(1):7\u201315","journal-title":"Journal of Theoretical Biology."},{"issue":"442","key":"19010_CR38","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.jtbi.2018.01.008","volume":"7","author":"M Arif","year":"2018","unstructured":"Arif M, Hayat M, Jan Z (2018) iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou\u2019s pseudo amino acid composition. Journal of Theoretical Biology. 7(442):11\u201321","journal-title":"Journal of Theoretical Biology."},{"issue":"2","key":"19010_CR39","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.bbrc.2007.06.027","volume":"360","author":"KC Chou","year":"2007","unstructured":"Chou KC, Shen HB (2007) MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochemical and biophysical research communications. 360(2):339\u201345","journal-title":"Biochemical and biophysical research communications."},{"issue":"4","key":"19010_CR40","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1016\/j.jtbi.2004.02.019","volume":"228","author":"YD Cai","year":"2004","unstructured":"Cai YD, Zhou GP, Jen CH, Lin SL, Chou KC (2004) Identify catalytic triads of serine hydrolases by support vector machines. J. Theor. Biol. 228(4):551\u2013557","journal-title":"J. Theor. Biol."},{"key":"19010_CR41","doi-asserted-by":"crossref","unstructured":"Golmohammadi SK, Kurgan L, Crowley B, Reformat M (2007) Classification of cell membrane proteins, IEEE. Frontiers In Ihe Convergence Of Bioscience And Information Technologies, pp 153\u2013158","DOI":"10.1109\/FBIT.2007.21"},{"issue":"1","key":"19010_CR42","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.bbrc.2005.06.087","volume":"334","author":"HB Shen","year":"2005","unstructured":"Shen HB, Chou KC (2005) Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types. Biochem. Biophys. Res. Commun. 334(1):288\u2013292","journal-title":"Biochem. Biophys. Res. Commun."},{"issue":"4","key":"19010_CR43","doi-asserted-by":"publisher","first-page":"289","DOI":"10.2174\/157016461104150121115154","volume":"11","author":"X Zhao","year":"2014","unstructured":"Zhao X, Zou Q, Liu B, Liu X (2014) Exploratory predicting protein folding model with random forest and hybrid features. Current Proteomics. 11(4):289\u201399","journal-title":"Current Proteomics."},{"issue":"292","key":"19010_CR44","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.jtbi.2011.09.026","volume":"7","author":"M Hayat","year":"2012","unstructured":"Hayat M, Khan A (2012) MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM. Journal of theoretical biology. 7(292):93\u2013102","journal-title":"Journal of theoretical biology."},{"issue":"5","key":"19010_CR45","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1016\/j.compbiomed.2012.01.012","volume":"42","author":"J Wang","year":"2012","unstructured":"Wang J, Li Y, Wang Q, You X, Man J, Wang C, Gao X (2012) ProClusEnsem: predicting membrane protein types by fusing different modes of pseudo amino acid composition. Computers in biology and medicine. 42(5):564\u201374","journal-title":"Computers in biology and medicine."},{"key":"19010_CR46","doi-asserted-by":"crossref","unstructured":"Mary,\u00a0 Ani Brown N,\u00a0 Dharma D (2017) Coral reef image classification employing improved LDP for feature extraction. J Visual Comm Image Representation 49:225-242","DOI":"10.1016\/j.jvcir.2017.09.008"},{"key":"19010_CR47","doi-asserted-by":"crossref","unstructured":"Alphonse, Sherly A, Ani Brown Mary N (2023) Classification of anti-oxidant proteins using novel physiochemical and conjoint-quad (PCQ) feature composition. Multimedia Tools Appl 1-27","DOI":"10.1007\/s11042-023-17498-w"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19010-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19010-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19010-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T13:20:22Z","timestamp":1731590422000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19010-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,12]]},"references-count":47,"journal-issue":{"issue":"36","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["19010"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19010-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,4,12]]},"assertion":[{"value":"25 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All applicable international, national, and\/or institutional guidelines for the care and use of animals were followed. This manuscript does not contain any studies with human participants or animals performed by any of the three authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"I understand that my contribution will be confidential and that there will be no personal identification in the data that I agree to allow to be used in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Authors should make sure to also seek consent from individuals to publish their data prior to submitting their manuscript to this journal.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"No conflict of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}