BackgroundBiomedical　semantic　indexing　is　important　for　information　retrieval　and　many　other　research　fields　in　bioinformatics.　It　annotates　biomedical　citations　with　Medical　Subject　Headings.　In　face　of　unbalanced　category　distribution　in　the　training　data,　sampling　methods　are　difficult　to　apply　for　semantic　indexing　task.ResultsIn　this　paper,　we　present　a　novel　deep　serial　multi-task　learning　model.　The　primary　task　treats　the　biomedical　semantic　indexing　as　a　multi-label　text　classification　issue　that　considers　the　relations　of　the　labels.　The　auxiliary　task　is　a　regression　task　that　predicts　the　MeSH　number　of　the　citation　and　provides　hints　for　the　network　to　make　it　converge　faster.　The　experimental　results　on　the　BioASQ-Task5A　open　dataset　show　that　our　model　outperforms　the　state-of-the-art　solution　MTI,　proposed　by　the　US　National　Library　of　Medicine.　Further,　it　not　only　achieves　the　highest　precision　among　all　the　solutions　in　BioASQ-Task5A　but　also　has　faster　convergence　speed　compared　with　some　naive　deep　learning　methods.ConclusionsRather　than　parallel　in　an　ordinary　multi-task　structure,　the　tasks　in　our　model　are　serial　and　tightly　coupled.　It　can　achieve　satisfied　performance　without　any　handcrafted　feature.