蛋白质二级结构预测是公认的生物信息学领域的国际性难题。以基于内在认知机理的知识发现理论(knowledge discovery theory based on inner cognitive mechanism,KDTICM)理论的扩展性研究与数据库中的知识发现(knowledge discovery in d...蛋白质二级结构预测是公认的生物信息学领域的国际性难题。以基于内在认知机理的知识发现理论(knowledge discovery theory based on inner cognitive mechanism,KDTICM)理论的扩展性研究与数据库中的知识发现(knowledge discovery in database*,KDD*)模型为基础,提出一种基于结构序列的多分类算法——SAC(structuralassociation classification),可以有效地解决蛋白质二级结构预测问题。该算法借助设定支持度阈值的精化知识库的方法,其预测准确率能够超过85%。以该算法为核心,构建了一个蛋白质二级预测模型——复合金字塔模型。实验证明,在RS126、CB513I、LP数据集上的预测准确率均超过80%,超过目前已知的国际主流水平。展开更多
This paper proposed a novel multi-view interactive behavior recognition method based on local self-similarity descriptors and graph shared multi-task learning. First, we proposed the composite interactive feature repr...This paper proposed a novel multi-view interactive behavior recognition method based on local self-similarity descriptors and graph shared multi-task learning. First, we proposed the composite interactive feature representation which encodes both the spatial distribution of local motion of interest points and their contexts. Furthermore, local self-similarity descriptor represented by temporal-pyramid bag of words(BOW) was applied to decreasing the influence of observation angle change on recognition and retaining the temporal information. For the purpose of exploring latent correlation between different interactive behaviors from different views and retaining specific information of each behaviors, graph shared multi-task learning was used to learn the corresponding interactive behavior recognition model. Experiment results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA, i3Dpose dataset and self-built database for interactive behavior recognition.展开更多
文摘蛋白质二级结构预测是公认的生物信息学领域的国际性难题。以基于内在认知机理的知识发现理论(knowledge discovery theory based on inner cognitive mechanism,KDTICM)理论的扩展性研究与数据库中的知识发现(knowledge discovery in database*,KDD*)模型为基础,提出一种基于结构序列的多分类算法——SAC(structuralassociation classification),可以有效地解决蛋白质二级结构预测问题。该算法借助设定支持度阈值的精化知识库的方法,其预测准确率能够超过85%。以该算法为核心,构建了一个蛋白质二级预测模型——复合金字塔模型。实验证明,在RS126、CB513I、LP数据集上的预测准确率均超过80%,超过目前已知的国际主流水平。
基金Project(51678075)supported by the National Natural Science Foundation of ChinaProject(2017GK2271)supported by Hunan Provincial Science and Technology Department,China
文摘This paper proposed a novel multi-view interactive behavior recognition method based on local self-similarity descriptors and graph shared multi-task learning. First, we proposed the composite interactive feature representation which encodes both the spatial distribution of local motion of interest points and their contexts. Furthermore, local self-similarity descriptor represented by temporal-pyramid bag of words(BOW) was applied to decreasing the influence of observation angle change on recognition and retaining the temporal information. For the purpose of exploring latent correlation between different interactive behaviors from different views and retaining specific information of each behaviors, graph shared multi-task learning was used to learn the corresponding interactive behavior recognition model. Experiment results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA, i3Dpose dataset and self-built database for interactive behavior recognition.
