Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2022.830170/full |
| _version_ | 1818467953882955776 |
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| author | Kyoung Tak Cho Taner Z. Sen Carson M. Andorf |
| author_facet | Kyoung Tak Cho Taner Z. Sen Carson M. Andorf |
| author_sort | Kyoung Tak Cho |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T21:06:20Z |
| format | Article |
| id | doaj.art-09766cf15a494c589ee629b81dfd6a33 |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-04-13T21:06:20Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-09766cf15a494c589ee629b81dfd6a332022-12-22T02:29:57ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122022-05-01510.3389/frai.2022.830170830170Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning ApproachKyoung Tak Cho0Taner Z. Sen1Carson M. Andorf2Department of Computer Science, Iowa State University, Ames, IA, United StatesUSDA-ARS, Crop Improvement and Genetics Research Unit, Albany, CA, United StatesUSDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA, United StatesMachine 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.https://www.frontiersin.org/articles/10.3389/frai.2022.830170/fullmaize geneticsgene expressionprotein abundancemRNA abundancemachine learning |
| spellingShingle | Kyoung Tak Cho Taner Z. Sen Carson M. Andorf Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach Frontiers in Artificial Intelligence maize genetics gene expression protein abundance mRNA abundance machine learning |
| title | Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach |
| title_full | Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach |
| title_fullStr | Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach |
| title_full_unstemmed | Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach |
| title_short | Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach |
| title_sort | predicting tissue specific mrna and protein abundance in maize a machine learning approach |
| topic | maize genetics gene expression protein abundance mRNA abundance machine learning |
| url | https://www.frontiersin.org/articles/10.3389/frai.2022.830170/full |
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