Current research of bio-text mining mainly focuses on event extractions. Biological networks present much richer and meaningful information to biologists than events. Bio-entity coreference resolution (CR) is a very important method to complete a bio-event's attributes and interconnect events into bio-networks. Though general CR methods have been studies for a long time, they could not produce a practically useful result when applied to a special domain. Therefore, bio-entity CR needs attention to better assist biological network extraction. In this article, we present two methods for bio-entity CR. The first is a rule-based method, which creates a set of syntactic rules or semantic constraints for CR. It obtains a state-of-the-art performance (an F1-score of 62.0%) on the community supported dataset. We also present a machine learning-based method, which takes use of a recurrent neural network model, a long-short term memory network. It automatically learns global discriminative representations of all kinds of coreferences without hand-crafted features. The model outperforms the previously best machine leaning-based method.