Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach

Abstract

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.

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“…The application of AI has brought about transformative advancements across each of these domains. For example, tissue-specific gene expression, in maize, based on protein sequences and deoxyribose nucleic acid (DNA) promoter was achieved through ML models with an accuracy of up to 95% ( Cho et al, 2022 ). Similarly, ML models of “ortholog contrasts” and “gene-family guided splitting” have also been reported for predicting the messenger ribonucleic acid (mRNA) expression ( Washburn et al, 2019 ).…”