# Plant Synthetic Promoters
## 6 Machine Learning and Deep Learning Support to Synthetic Promoter Preparation  
The fast and accurate prediction of promoter strength remains challenging, resulting in time- and labor-consuming promoter construction and characterization. This challenge is mainly due to the shortfall of suitable high-throughput analytical methodology and the lack of a large promoter library with sufficient dynamic range, gradient strength, and clear sequence profiles to be used for machine learning (ML) [46]. ML approaches offer models that characterize the properties between synthetic promoter structure and functions [46; 241]. This information broadens our understanding of gene regulation and points out new principles for later testing [242]. ML models such as linear regression and random forests combine input data, such as sequences of promoters, distribution of cis-active elements,  
Figure 6: The first generation of dCas9-dependent artificial trans-factors (ATFs). The deactivated form of the Cas9 protein (dCas9) is fused to the transcriptional activator domains VP64, ERF2, or EDLL to create ATFs. The ATF upregulates the expression of reporter genes via specific guide RNA (gRNA). Figure 7: The second generation of dCas9-dependent artificial trans-factors (ATFs) contains the modified gRNA with two aptamer loops to allow the attachment of the viral MS2 protein. The MS2 protein is bound by activators (VPR) or other components acting as repressors or epigenetic regulators; this can increase the scope of regulatory functions compared to the first generation of dCas9-dependent artificial trans-factors (ATFs). DNA structural properties, position weight matrices, or k-mer frequencies, to obtain output information as the expression patterns of the corresponding synthetic promoters [46; 48; 50]. To better predict the synthetic promoter strength, the improved ML-based approach known as EVMP (extended vision mutant priority) system was developed. The EVMP uses better mutation information through the equivalent transformation of synthetic promoters into base promoters which are input into BaseEncoder and corresponding \(k\)-mer mutations, which are entered into VarEncoder. Therefore, the EVMP can significantly improve the prediction accuracy of promoter strength [243]. Advanced ML algorithms produce highly accurate models of gene expression, uncovering novel regulatory features in nucleotide sequences involving multiple cis-regulatory regions across whole genes and DNA structural properties. Among them are deep neural networks (DNN), containing multiple hidden layers, each learning an informative representation of DNA regulatory sequences [50]. The ML systems work with the large libraries of synthetic promoter variants and corresponding expression data [46]. The input/output data is then split into training and testing parts for building the model and the final validation. The ML model's accuracy generally increases when a greater number of promoter sequences and corresponding RNA-seq, ATAC-seq, and self-transcribing active regulatory region sequencing (STARRSeq) data are applied [85; 244]. However, such ML approaches cannot properly predict the properties of synthetic promoter sequences in the context of other regulatory elements such as the position of enhancers, 3\({}^{\prime}\) and 5\({}^{\prime}\)UTRs, DNA methylation or histone acetylation status [50; 245; 246]. These details should be included in the more advanced form of ML, known as deep learning (DL), which uses multi-layer perceptron (MLP), convolutional neural networks (CNN) and generative adversarial networks (GAN) to analyze complex patterns and relationships in data [49; 85; 247; 248; 249; 250]. DL models implemented in over 20,000 mRNA datasets in seven model organisms from bacteria to humans indicated that the expression of a gene is controlled not by a single regulatory motif or region, but by the entire gene regulatory structure and specific combinations of regulatory elements [49].