Generative and Discriminative Methods for Gene Finding and Functional Annotation

本项目完成的任务分为两个阶段，分别对应基因预测和功能注释两个任务。在第一阶段，基因发现系统的开发和应用。该系统被设计为可扩展的，可扩展的，包括它所建模的基因特征，它所采用的机器学习算法，以及它所学习的实验数据的范围。该项目首先通过将该系统应用于完整的秀丽隐杆线虫基因组来验证该系统，然后对该系统进行重新训练，以完成识别人类DNA中基因的更困难的任务。该项目的第二阶段由两部分组成。首先，将第一阶段用于基因发现系统的软件框架推广到相关蛋白的模型家族。其次，为了从非顺序数据中学习，该项目使用一种称为支持向量机（SVM）的判别学习方法开发了功能分类技术。基于序列的建模系统计算的统计量作为支持向量机系统使用的一组特征。其他特征将来自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评分。

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.