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

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 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. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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.