Best Practices of Feature Selection in Multi-Omics Data

Today, there is an increase in data in many areas. With this increase, the number and variety of the variables to be evaluated also increases. The increase in data and variables became a situation that needed to be solved among world problems. In addition, although there is a perception that having too much data in the scientific field, having too much information, correct information, or sufficient information may not be possible. However, it should not be forgotten that there is valuable information in a relatively large amount of data. It should be clear that it can be beneficial to have much data to extract this helpful information. However, performing data analyses to obtain and process this information can be difficult. In addition, its existence is a problem called the curse of data dimensionality (Verkeysen M. and François D., 2005). High-dimensional data sets, where these problems are most common, are used successfully in multiple fields such as genetics, pharmacology, toxicology, nutrition, and genetics. The use of these high-dimensional data allows one to examine biology systems, cellular metabolism, and disease etiologies in more detail. However, the number of samples (n) of these data is considerably lower than the number of variables (p) and the heterogeneity of the data, the missing observations in the data as a result of the use of high-output technology, limits the use of traditional methods that can be used in this field. Therefore, there is a need for the clinical understanding of the biological system based on research and machine learning, and statistical learning methods to analyze this clinical information statistically (Hastie et al., 2009). Several studies are show that machine learning methods are used and applied successfully in studies carried out in this field. Some of these studies are listed in Table 1.