Machine learning classification models for fetal skeletal development performance prediction using maternal bone metabolic proteins in goats
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Machine learning classification models for fetal skeletal development performance prediction using maternal bone metabolic proteins in goatsAuthor(s)
Date
2019-10Citation
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
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.
Keywords
Fetal bone metabolism
Maternal malnutrition
Intrauterine growth retardation
Computational analysis
Machine learning
Maternal malnutrition
Intrauterine growth retardation
Computational analysis
Machine learning
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). 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. Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.7840#supplemental-information.
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Rights
Atribución 4.0 Internacional
ISSN
2167-8359