Generative and Discriminative Methods for Gene Finding and Functional Annotation

该项目所完成的任务分为两个阶段，分别对应于基因预测和功能注释两个任务。 在第一阶段，开发和应用基因发现系统。 该系统被设计为在其建模的基因特征、其采用的机器学习算法以及其学习的实验数据范围方面可扩展和可扩展。 该项目首先通过将其应用于完整的秀丽隐杆线虫基因组来验证该系统，然后重新训练该系统以识别人类DNA中的基因这一更困难的任务。 该项目的第二阶段包括两个部分。 首先，从第一阶段的基因发现系统所使用的软件框架被推广到相关蛋白质的模型家族。 其次，为了从非连续数据中学习，该项目使用称为支持向量机（SVM）的判别学习方法开发功能分类技术。 由基于序列的建模系统计算的统计数据用作由SVM系统使用的一组特征。 其他功能将来自DNA微阵列实验，每个基因的上游启动子区域，系统发育概况和已知蛋白质家族的相似性得分。

The tasks accomplished by this project are divided into two phases corresponding to the two tasks of gene prediction and functional annotation. In the first phase, a gene-finding system is developed and applied. This system is designed to be scalable and extensible with respect to the gene features it models, the machine learning algorithms it employs, and the range of experimental data from which it learns. This project first validates the system by applying it to the complete C.elegans genome, and then retrains the system for the more difficult task of recognizing genes in human DNA. The second phase of this project consists of two parts. First, the software framework used for the gene finding system from phase one is generalized to model families of related proteins. Second, in order to learn from non-sequential data, the project develops functional classification techniques using a discriminative learning method called support vector machines (SVM's). The statistics calculated by the sequence-based modeling system functions as one set of features used by the SVM system. Additional features will come from DNA microarray experiments, the upstream promoter region of each gene, phylogenetic profiles and similarity scores to known protein families.