Alternating decision tree algorithm for assessing protein interaction reliability
Min Su Lee, Sangyoon Oh · Vietnam Journal of Computer Science · 2014
This paper presents a machine learning approach for assessing the reliability of protein–protein interactions in a high-throughput dataset. We use an alternating decision tree algorithm to distinguish true interacting protein pairs from noisy high-throughput data using various biological attributes of interacting proteins. The alternating decision tree algorithm is used both for identifying discriminating biological features that could be used for assessing protein interaction reliability and for constructing a classifier to identify true positive interacting pairs. Experimental results show that the proposed approach has a good performance in distinguishing true interacting protein pairs from noisy protein–protein interaction data. Moreover, our alternating decision tree classifier supplemented with domain knowledge may be helpful to understand the biological conditions in connection with interacting protein pairs.