# A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
## 1 Introduction  
The yield and nutritional quality of a crop are impacted by the different stresses experienced by plants during growth. Such stresses can be broadly classified into two groups, biotic and environmental (abiotic) [1]. Plants usually respond to stress through complicated molecular mechanisms, such as changes in the transcriptome and regulatory networks [2]. In severe cases, irreversible damage and plant death could be observed if the stress exceeds the plant's tolerance threshold [2]. These threshold limits are encoded in and determined by the plant's genetic makeup. Advances in high-throughput gene expression technologies, such as microarray platforms, have offered a new pathway for the identification of key genes involved in plant responses to specific stress conditions [3]. Considering plants are capable of activating stress-specific and general stress response networks to adapt to various stressors [2], identifying genes that play a general role in stress response can facilitate the development of stress-tolerant cultivars through genetic engineering [4]. Stress factors such as water/nutrient deficiencies/excesses, sub-optimal environmental conditions, pathogens, and so on in plants may elicit a gene expression pattern [5]. To improve plant adaptability to changing environments, it is critical to understand the underlying mechanisms for stress responses in plants. While a large amount of gene expression information is now available via public databases, merging data from independent transcriptome analysis (meta-analysis) remains challenging because of differences in experimental procedures and analysis methods [6]. Building on this, researchers such as [7] generated a large dataset of gene expression profiles in _Arabidopsis thaliana_ subjected to various stresses (including salt, temperature, high-light (HL), and flagellin (FLG)). The authors investigated transcriptional regulatory networks during single and combined stresses, such as weighted gene co-expression network analysis (WGCNA), transcription factors (TFs), TF binding motifs and sequence logos, and network component analysis (NCA). However, the gene signature responsible for plant stress tolerance was not explicitly investigated. Adding another dimension, several studies have elucidated transcriptome changes in response to isolated biotic and abiotic stresses [8; 9; 10]. [PERSON] et al. [9] especially highlighted a significant point: transcriptome responses to a singular stress could not always predict the alterations in relation to combined stresses. This is a crucial revelation because plants in the field frequently encounter multiple stresses concurrently. Identifying genes responsive to these combined stresses is thus paramount. Yet, there is a complication as obtaining such gene expression data experimentally is both labor-intensive and costly. This is where machine learning (ML) offers a beacon of hope, emerging as a formidable tool for biomarker discovery. Not only does it streamline the process, but it also uncovers gene relationships that traditional methods might overlook. Considering that such knowledge could contribute to gene analysis and the development of stress-tolerant plants, the present study proposes applying ML algorithms to the gene expression data [7] generated in _Arabidopsis thaliana_. We aim to use state-of-the-art ML approaches, including feature selection techniques, to identify the genes or gene clusters responsible for stress tolerance. Feature selection algorithms in ML have previously been used to find optimum features and generate gene signatures [11]. Some of the well-known feature selection techniques include information gain (IG), ReliefF, and least absolute shrinkage and selection operator (LASSO) [12; 13]. [PERSON] et al. [14] successfully applied LASSO methods to gene co-expression networks to identify key genes associated with salt stress in rice. Information gain and ReliefF have also performed well in identifying feature dependencies [15; 16].