_applied sciences_  
_Article_  
Analysis of Mouse Blood Serum in the Dynamics of U87 Glioblastoma by Terahertz Spectroscopy and Machine Learning  
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Footnote  
A glioblastoma animal model makes it possible to investigate tumor development under controlled conditions. Orthotopic xenotransplantation of human cells into immunodeficient animals is the most common model of glioma [17; 18]. Currently, continuous cell lines such as U87 derived from primary human tumor cells (transplanted intracranially into the mouse brain) are widely used to create such models of human glioma [18; 19].  
The blood serum THz absorption spectra do not have sharp peaks [20; 21]. In this case, differentiation of various groups can be achieved using machine learning (ML) [14; 22; 23; 24; 25; 26]. The ML pipeline includes different methods to remove noise, select informative features, built predictive data models, etc. The sequence of algorithms depends on data peculiarities. Sharp noise peaks in THz spectra can be removed by proper smoothing [27; 28; 29]. The data homogeneity of every class is defined by the presence of outliers. Signal denoising and outlier removal are optional steps, applied when there are specific problems with data, such as data acquisition errors caused by hardware noise, or inhomogeneity of biological samples caused by individual characteristics of glioma development in different animals. Balance among classes influences the effectiveness of the predictive data model. Justification of the choice of appropriate algorithms can be confirmed by external validation procedures, i.e., ML predictive model scoring [30; 31].  
Small volumes and high dimensions of data are the major problems in medical studies. Dimensionality reduction is performed by feature extraction, which involves transformation of the original data to remove redundant data. Many feature-extraction techniques have different benefits; among the most reliable are loadings matrix analysis in PCA (LMPCA), and Shapley values [32; 33]. PCA has proven its effectiveness in multiple studies of THz, IR, and Raman spectra [12; 13; 16; 34]. Yet LMPCA is a linear method, which might not be effective for complex data but gives repeatable results. Shapley values can be used along with arbitrary predictive model-construction algorithms, but they are computationally costly owing to the high dimensionality of data.  
Several ML methods have been used for small volume and high dimensional biomedical data, such as support vector machines (SVMs), gradient boosting (GB), and random forests (RFs) [16]. SVM is a robust algorithm allowing the construction of a non-probabilistic binary linear classifier, which maximizes the margin width between two classes in the feature space. The kernels can be considered as projective functions to map the input data to a higher dimensional space. To retain explicability, the data model should be linear. The linear kernel is the simplest, allowing construction of a direct informative feature-selection procedure based on the distance of the feature vector from the margin. The k-fold or random train-test split cross-validation (CV) model is commonly applied for estimating the performance of a predictive data models. The k-fold CV has reasonable computational load.  
In this work, during experimental U87 glioblastoma development, mouse blood serum was studied by THz-TDS, and a ML-based predictive data model for glioblastoma detection was created.