Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_117_библиотеки_им_акад_М_И_Перельмана

The classication performance of ML algorithms on microbiome data is nega­tively affected by its sparsity, dominant features, and skewed nature [12–14]. Consequently, preprocessing techniques such as data normalization play a vital role in microbiome data classication. However, the normalization of microbiome data remains a challenge [15] and represents an ongoing area of research. Conventional data normalization methods such as minimum–maximum (min–max), Z-score nor­malization (ZSN), and median and median absolute deviation normalization (MMADN) are considered unsuitable for normalizing gene sequence-based data [16]. This is because gene sequence-based data, such as microbiome data, exhibit compositional characteristics, with a constant sum and restricted to nonnegative values [17]. To address this limitation, normalization methods specically tailored to gene sequence-based data have been developed, including trimmed mean of M-values (TMM), relative log expression (RLE), and rarefying, aiming to enhance data classication [18]. However, these methods also have limitations, such as the inability to handle the excessive number of zeros commonly found in sequence data [13].

Another signicant area of microbiome samples classication is data normaliza­tion, a preprocessing technique that identies and removes systematic variability [16]. While the effectiveness of a normalization method depends on the characteris­tics of the target dataset, datasets used in ML tasks differ in terms of underlying features. Therefore, there has been a substantial amount of research on the applica­tion of data normalization methods on various types of datasets. Manor and Borenstein [15] proposed a normalization method that applied ML methods on single-copy genes to correct marked biases within and across human microbiome­based samples, and to obtain measures that have biological meanings, which are also accurate. Though the method corrects spurious variations across samples and produces accurate downstream comparative analysis with meaningful abundance measures, it is limited to identifying bacterial and archaeal organisms only and does not cover fungal and viral organisms. Gloor etal. [17] demonstrated that current methods for compositional data-based analysis can be easily adopted for analysis of high throughput sequence data. Their method produced good results because it accounted for the compositional nature of microbiome data. However, the study was only based on 16S rRNA data and did not consider shotgun sequence data. Kaul etal. [25] conducted a study that addresses the challenges associated with sparsity in microbiome data. They proposed a method that identies three types of zero val­ues found in microbiome data and conducted hypothesis testing on relative taxa abundance in more than one experimental group. Although the method was able to improve the false positive discovery rate, experiments were based on simulated data that may not reproduce the same level of performance on real data.