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Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data

机译：使用微阵列表达数据对全人类组织选择性基因进行全基因组预测和分析

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Background Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. Results In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs) and Support Vector Machines (SVMs) were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. Conclusions A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression.
机译：背景技术了解基因如何在特定组织中特异性表达是发育生物学中的一个基本问题。许多组织特异性基因参与复杂的人类疾病的发病机制。但是，组织特异性基因的实验鉴定既费时又困难。组织特异性基因靶标的准确预测可以为生物标志物开发和药物靶标鉴定提供有用的信息。结果在这项研究中，我们开发了一种使用微阵列表达数据预测人类组织特异性基因的机器学习方法。从UniProt数据库收集了不同组织的已知组织特异性基因的列表，并将根据列表从先前编译的数据集中检索到的表达数据用于输入载体编码。随机森林（RF）和支持向量机（SVM）用于构建准确的分类器。发现RF分类器在组织特异性基因预测方面优于SVM模型。结果表明，大脑或肝脏特异性表达的候选基因可以为进一步的实验研究提供有价值的信息。我们的方法还用于识别不同类型组织的组织选择性基因靶标。结论已经开发了一种机器学习方法，用于准确识别组织特异性/选择性表达的候选基因。该方法提供了一种有效的方法来选择一些有趣的基因来开发新的生物医学标记，并提高我们对组织特异性表达的了解。

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