Data_Sheet_1_Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach.PDF

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.

机器学习与建模方法已被广泛应用于蛋白质序列分类任务，涵盖蛋白质功能预测、结构预测、表达量预测以及亚细胞定位预测等多个研究方向。近年来已有多项研究成功预测了特定基因是否可转录为信使RNA（messenger RNA, mRNA），甚至潜在可翻译为蛋白质，但由于并非所有基因都会在所有条件与组织中表达，因此预测特定条件/组织特异性基因表达仍是尚未解决的关键挑战。为填补这一研究空白，本研究仅基于DNA启动子序列与蛋白质序列，开发了一种机器学习方法，用于预测玉米23种不同组织中的组织特异性基因表达情况。针对分类标签，本研究为mRNA丰度与蛋白质丰度定义了高、低两种表达水平，并通过两阶段策略系统探索多种方法与k-mer序列（k-mer）的组合方式，对分类器进行优化。第一阶段中，我们针对每种组织构建了马尔可夫模型（Markov model）分类器，并基于模型的预测结果构建特征向量；第二阶段则将该特征向量作为输入，送入贝叶斯网络（Bayesian network）以完成最终分类。实验结果表明，针对单组织的基因表达预测任务，本方法的分类准确率最高可达95%，表现优异。本方法仅依赖序列信息即可在无法获取高成本实验数据的研究场景中发挥作用，同时还可为解析基因的功能、进化与调控特征提供有价值的科学见解。