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的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 蛋白质二级结构预测提供了对蛋白质功能的深入了解，并且是预测蛋白质3D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中提供最先进的性能。 随着蛋白质数据库规模的增长，使用更丰富的模型来捕获氨基酸和预测标签之间的微妙相关性变得可行。 在这种情况下，导出稀疏模型是有益的，该稀疏模型阻止过度拟合并提供生物学见解。 结果：我们介绍了一种算法稀疏的DBN的参数。使用这种算法，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。 我们还证明了一个上界的测试误差之间的差异稀疏和完全密集的模型。 最后，我们证明，使用模拟数据，该算法是能够恢复真正的稀疏结构具有高精度，并使用真实的数据，稀疏模型识别已知的相关结构相关的不同类别的二级结构元素。

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.