The classical methods for the classification problem include hypothesis test with the Benjamini–Hochberg method, hidden Markov chain model, and support vector machine. One major application of the classification problem is gene expression analysis, for example, detecting the host genes having interaction with pathogen. The classical methods can be applied and have a good performance when the number of genes having interaction with the pathogen is not sparse with respect to the candidate genes. However, conditional random field (CRF), with an appropriate design, can be applied and have good performance even when it is sparse. In this work, we proposed a modified CRF with a baseline to reduce the number of parameters in CRF. Moreover, we show an application of CRF with the least absolute shrinkage and selection operator (LASSO) to classifying barley genes of its reaction to the pathogen.

中文翻译：

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具有最小绝对收缩和选择算子的条件随机场根据真菌感染影响的表达水平对大麦基因进行分类

分类问题的经典方法包括使用 Benjamini-Hochberg 方法的假设检验、隐马尔可夫链模型和支持向量机。分类问题的一个主要应用是基因表达分析，例如检测与病原体相互作用的宿主基因。当与病原体相互作用的基因数量相对于候选基因不稀疏时，可以应用经典方法并且具有良好的性能。然而，条件随机场（CRF）经过适当的设计，即使在稀疏的情况下也可以应用并具有良好的性能。在这项工作中，我们提出了一种带有基线的修改 CRF，以减少 CRF 中的参数数量。而且，