Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach - Cho, Kyoung Tak; Sen, Taner Z.; Andorf, Carson M. (Frontiers in Artificial Intelligence, 2022)
Machine learning and modeling approaches have been used to classify protein sequences for a broad set of tasks including predicting protein function, structure, expression, and localization. Some recent studies have successfully predicted whether a given gene is expressed as mRNA or even translated to proteins potentially, but given that not all genes are expressed in every condition and tissue, the challenge remains to predict condition-specific expression. To address this gap, we developed a machine learning approach to predict tissue-speciﬁc gene expression across 23 diﬀerent tissues in maize, solely based on DNA promoter and protein sequences. For class labels, we deﬁned high and low expression levels for mRNA and protein abundance and optimized classifiers by systematically exploring various methods and combinations of k-mer sequences in a two-phase approach. In the ﬁrst phase, we developed Markov model classiﬁers for each tissue and built a feature vector based on the predictions. In the second phase, the feature vector was used as an input to a Bayesian network for ﬁnal classiﬁcation. Our results show that these methods can achieve high classiﬁcation accuracy of up to 95% for predicting gene expression for individual tissues. By relying on sequence alone, our method works in settings where costly experimental data are unavailable and reveals useful insights into the functional, evolutionary, and regulatory characteristics of genes.