A gram distribution kernel applied to glycan classification and motif extraction.
A gram distribution kernel applied to glycan classification and motif extraction.
复制标题
DOI：
10.11234/gi1990.17.2_25
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
T. Kuboyama;Kouichi Hirata;Kiyoko F. Aoki-Kinoshita;H. Kashima;H. Yasuda
中科院分区：
文献类型：
--
作者：
T. Kuboyama;Kouichi Hirata;Kiyoko F. Aoki-Kinoshita;H. Kashima;H. Yasuda
我们提出了一种新的通用树核，并将其应用于聚糖结构分析。我们的内核通过计算嵌入在树中的所有可能长度q的公共q长度子串（树q-gram）的数量来测量两个标记树之间的相似性。我们使用支持向量机（SVM）将我们的树核应用于聚糖结构数据的分类和特定特征提取。我们的研究结果表明，我们的内核优于Hizukuri等人的分层三聚体内核。这是很好地定制聚糖数据，而我们没有调整我们的内核聚糖特定的属性。此外，我们使用经过训练的SVM从各种类型的聚糖数据中提取特定特征。结果表明，我们的内核更灵活，能够从聚糖数据中找到更广泛的子结构。
We propose a novel general-purpose tree kernel and apply it to glycan structure analysis. Our kernel measures the similarity between two labeled trees by counting the number of common q-length substrings (tree q-grams) embedded in the trees for all possible lengths q. We apply our tree kernel using a support vector machine (SVM) to classification and specific feature extraction from glycan structure data. Our results show that our kernel outperforms the layered trimer kernel of Hizukuri et al. which is well tailored to glycan data while we do not adjust our kernel to glycan-specific properties. In addition, we extract specific features from various types of glycan data using our trained SVM. The results show that our kernel is more flexible and capable of finding a wider variety of substructures from glycan data.