Integrating evidence for evaluation of potential novel protein-coding genes using Bayesian networks

Evaluating the quality of potential new protein-coding genes that have been predicted by directly searching mass spectrometry against genome sequence is a very challenging task. Many machine learning techniques such as neural networks, decision trees, and support vector machines have been applied to this task. All of these techniques learn a model from a training dataset and predict the quality of potential novel protein-coding genes using various evidential features as inputs. The quality and quantity of the training dataset significantly affect the performance of the learned models. In biological research, data collected is often incomplete and with very few data points. It is desirable to have methods that are robust to noisy data and low sample-size. Furthermore, the models learned by these machine learning techniques typically do not reveal the conditional (in)dependence relations among the evidential features. Gaining insight into the relationships among features is important for biological domains .In this paper, we describe methods for learning Bayesian networks for modeling the conditional (in)dependence relations among features of protein-coding genes and calculating confidence scores for potential novel genes based on their evidential features. Bootstrap methods are applied to assess the confidence measure on the arcs of the learned network structures and to identify a set of robust arcs in order to construct a final model for future predictions. We tested the Bayesian network model learned from our method using a training experimental dataset. The results show that the method significantly improved the accuracy of the learned model in predicting potential novel genes.