The goal of Biomedical relation extraction is to uncover high-quality relations from life science literature with diverse applications in the fields of Biology and Medicine. In the last decade, several methods can be found in published literature ranging from binary to complex relation extraction. In this work, we present a binary relation extraction system that relies on sentence level dependency features. We use a novel approach to map dependency tree based rules to feature vectors that can be used to train a classifier. We build a SVM classifier using these feature vectors and our experimental results show that it outperforms simple co-occurrence and rule-based systems. Through our experiments, using two 'real-world' examples, we quantify the positive impact of improved relation extraction on Literature Based Discovery.