LEARNING SPARSE MODELS FOR A DYNAMIC BAYESIAN NETWORK CLASSIFIER OF PROTEIN SECO

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 蛋白质二级结构预测提供了对蛋白质功能的洞察，是预测蛋白质三维结构的有价值的初步步骤。动态贝叶斯网络(DBN)已被证明在二级结构预测方面提供了最先进的性能。随着蛋白质数据库规模的增长，使用更丰富的模型来努力捕捉氨基酸和预测标签之间的微妙关联变得可行。在这种情况下，导出稀疏模型是有益的，这些模型可以阻止过度拟合，并提供生物学见解。 结果：提出了一种稀疏DBN参数的算法。使用该算法，我们可以自动删除高达80%的DBN参数，同时保持相同的预测精度。我们还证明了稀疏模型和完全稠密模型之间检验误差差的一个上界。最后，我们使用模拟数据证明了该算法能够高精度地恢复真正的稀疏结构，并且使用真实数据证明了稀疏模型能够识别与不同类别的二级结构元素相关的已知结构。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Protein secondary structure prediction provides insight into protein function and is a valuable preliminary step for predicting the 3D structure of a protein. Dynamic Bayesian networks (DBNs) have been shown to provide state-of-the-art performance in secondary structure prediction. As the size of the protein database grows, it becomes feasible to use a richer model in an effort to capture subtle correlations among the amino acids and the predicted labels. In this context, it is beneficial to derive sparse models that discourage over-fitting and provide biological insight. Results: We introduce an algorithm for sparsifying the parameters of a DBN. Using this algorithm, we can automatically remove up to 80% of the parameters of a DBN while maintaining the same level of predictive accuracy. We also prove an upper bound for the test error difference between the sparse and fully dense models. Finally, we demonstrate, using simulated data, that the algorithm is able to recover true sparse structures with high accuracy, and using real data, that the sparse model identifies known correlation structure related to different classes of secondary structure elements.