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Machine learning classification models for fetal skeletal development performance prediction using maternal bone metabolic proteins in goats
dc.contributor.author | Liu, Yong | |
dc.contributor.author | Munteanu, Cristian-Robert | |
dc.contributor.author | Yan, Qiongxian | |
dc.contributor.author | Pedreira Souto, Nieves | |
dc.contributor.author | Kang, Jinhe | |
dc.contributor.author | Tang, Shaoxun | |
dc.contributor.author | Zhou, Chuanshe | |
dc.contributor.author | He, Zhixiong | |
dc.contributor.author | Tan, Zhiliang | |
dc.date.accessioned | 2024-06-28T17:32:31Z | |
dc.date.available | 2024-06-28T17:32:31Z | |
dc.date.issued | 2019-10 | |
dc.identifier.citation | Liu Y, Munteanu CR, Yan Q, Pedreira N, Kang J, Tang S, Zhou C, He Z, Tan Z. 2019. Machine learning classification models for fetal skeletal development performance prediction using maternal bone metabolic proteins in goats. PeerJ 7:e7840 https://doi.org/10.7717/peerj.7840 | es_ES |
dc.identifier.issn | 2167-8359 | |
dc.identifier.uri | http://hdl.handle.net/2183/37562 | |
dc.description | Animal Ethics The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers): The Animal Care Committee of Institute of Subtropical Agriculture, Chinese Academy of Sciences (ISA-CAS) provided full approval for this research (No. KYNEAAM-2015-0009). | es_ES |
dc.description | Data Availability The following information was supplied regarding data availability: Python code for machine learning is available in Github and GitLab repositories https://github.com/muntisa/Goat-Bones-Machine-Learning and https://gitlab.com/ muntisa/goat-bones-machine-learning. The raw data is available in a Supplemental File. | es_ES |
dc.description | Supplemental Information Supplemental information for this article can be found online at http://dx.doi.org/10.7717/ peerj.7840#supplemental-information. | es_ES |
dc.description.abstract | [Abstract]: Background In developing countries, maternal undernutrition is the major intrauterine environmental factor contributing to fetal development and adverse pregnancy outcomes. Maternal nutrition restriction (MNR) in gestation has proven to impact overall growth, bone development, and proliferation and metabolism of mesenchymal stem cells in offspring. However, the efficient method for elucidation of fetal bone development performance through maternal bone metabolic biochemical markers remains elusive. Methods We adapted goats to elucidate fetal bone development state with maternal serum bone metabolic proteins under malnutrition conditions in mid- and late-gestation stages. We used the experimental data to create 72 datasets by mixing different input features such as one-hot encoding of experimental conditions, metabolic original data, experimental-centered features and experimental condition probabilities. Seven Machine Learning methods have been used to predict six fetal bone parameters (weight, length, and diameter of femur/humerus). Results The results indicated that MNR influences fetal bone development (femur and humerus) and fetal bone metabolic protein levels (C-terminal telopeptides of collagen I, CTx, in middle-gestation and N-terminal telopeptides of collagen I, NTx, in late-gestation), and maternal bone metabolites (low bone alkaline phosphatase, BALP, in middle-gestation and high BALP in late-gestation). The results show the importance of experimental conditions (ECs) encoding by mixing the information with the serum metabolic data. The best classification models obtained for femur weight (Fw) and length (FI), and humerus weight (Hw) are Support Vector Machines classifiers with the leave-one-out cross-validation accuracy of 1. The rest of the accuracies are 0.98, 0.946 and 0.696 for the diameter of femur (Fd), diameter and length of humerus (Hd, Hl), respectively. With the feature importance analysis, the moving averages mixed ECs are generally more important for the majority of the models. The moving average of parathyroid hormone (PTH) within nutritional conditions (MA-PTH-experim) is important for Fd, Hd and Hl prediction models but its removal for enhancing the Fw, Fl and Hw model performance. Further, using one feature models, it is possible to obtain even more accurate models compared with the feature importance analysis models. In conclusion, the machine learning is an efficient method to confirm the important role of PTH and BALP mixed with nutritional conditions for fetal bone growth performance of goats. All the Python scripts including results and comments are available into an open repository at https://gitlab.com/muntisa/goat-bones-machine-learning. | es_ES |
dc.description.sponsorship | This work was supported by the National Natural Science Foundation of China (Grant No. 31402105, 31702141, 31772631, 31730092, 2018YFD0501900), and the Chinese Academy of Sciences (CAS) Pioneer Hundred Talents Program for joint support. Additional supports were offered by the Consolidation and Structuring of Competitive Research Units_Competitive Reference Groups (ED431C 2018/49), and funded by the Ministry of Education, University and Vocational Training of the Xunta de Galicia endowed with EU FEDER funds. This work was also supported by the Collaborative Project in Genomic Data Integration (CICLOGEN)'' PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER)_``A way to build Europe''. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2018/49 | es_ES |
dc.description.sponsorship | China. National Natural Science Foundation of China; 31402105 | es_ES |
dc.description.sponsorship | China. National Natural Science Foundation of China; 31702141 | es_ES |
dc.description.sponsorship | China. National Natural Science Foundation of China; 31772631 | es_ES |
dc.description.sponsorship | China. National Natural Science Foundation of China; 31730092 | es_ES |
dc.description.sponsorship | China. National Natural Science Foundation of China; 2018YFD0501900 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | PeerJ | es_ES |
dc.relation | info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016/PI17%2F01826/ES/PROYECTO COLABORATIVO DE INTEGRACION DE DATOS GENOMICOS (CICLOGEN). TECNICAS DE DATA MINING Y DOCKING MOLECULAR PARA ANALISIS DE DATOS INTEGRATIVOS EN CANCER DE COLON/ | es_ES |
dc.relation.uri | https://doi.org/10.7717/peerj.7840 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Fetal bone metabolism | es_ES |
dc.subject | Maternal malnutrition | es_ES |
dc.subject | Intrauterine growth retardation | es_ES |
dc.subject | Computational analysis | es_ES |
dc.subject | Machine learning | es_ES |
dc.title | Machine learning classification models for fetal skeletal development performance prediction using maternal bone metabolic proteins in goats | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | PeerJ | es_ES |
UDC.volume | 7 | es_ES |
UDC.issue | e7840 | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 24 | es_ES |
dc.identifier.doi | 10.7717/peerj.7840 |
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