Appropriate indexing of resources is necessary for their efficient search, discovery and utilization. Relying solely on manual effort is time-consuming, costly and error prone. On the other hand, the special nature, volume and broadness of biomedical literature pose barriers for automated methods. We argue that current word embedding algorithms can be efficiently used to support the task of biomedical text classification. Both deep- and shallow network approaches are implemented and evaluated. Large datasets of biomedical citations and full texts are harvested for their metadata and used for training and testing. The ontology representation of Medical Subject Headings provides machine-readable labels and specifies the dimensionality of the problem space. These automated approaches are still far from entirely substituting human experts, yet they can be useful as a mechanism for validation and recommendation. Dataset balancing, distributed processing and training parallelization in GPUs, all play an important part regarding the effectiveness and performance of proposed methods.