Taiyi: a bilingual fine-tuned large language model for diverse biomedical tasks

OBJECTIVE

Most existing fine-tuned biomedical large language models (LLMs) focus on enhancing performance in monolingual biomedical question answering and conversation tasks. To investigate the effectiveness of the fine-tuned LLMs on diverse biomedical natural language processing (NLP) tasks in different languages, we present Taiyi, a bilingual fine-tuned LLM for diverse biomedical NLP tasks.

MATERIALS AND METHODS

We first curated a comprehensive collection of 140 existing biomedical text mining datasets (102 English and 38 Chinese datasets) across over 10 task types. Subsequently, these corpora were converted to the instruction data used to fine-tune the general LLM. During the supervised fine-tuning phase, a 2-stage strategy is proposed to optimize the model performance across various tasks.

RESULTS

Experimental results on 13 test sets, which include named entity recognition, relation extraction, text classification, and question answering tasks, demonstrate that Taiyi achieves superior performance compared to general LLMs. The case study involving additional biomedical NLP tasks further shows Taiyi's considerable potential for bilingual biomedical multitasking.

CONCLUSION

Leveraging rich high-quality biomedical corpora and developing effective fine-tuning strategies can significantly improve the performance of LLMs within the biomedical domain. Taiyi shows the bilingual multitasking capability through supervised fine-tuning. However, those tasks such as information extraction that are not generation tasks in nature remain challenging for LLM-based generative approaches, and they still underperform the conventional discriminative approaches using smaller language models.

Deep-belief network for predicting potential miRNA-disease associations

MicroRNA (miRNA) plays an important role in the occurrence, development, diagnosis and treatment of diseases. More and more researchers begin to pay attention to the relationship between miRNA and disease. Compared with traditional biological experiments, computational method of integrating heterogeneous biological data to predict potential associations can effectively save time and cost. Considering the limitations of the previous computational models, we developed the model of deep-belief network for miRNA-disease association prediction (DBNMDA). We constructed feature vectors to pre-train restricted Boltzmann machines for all miRNA-disease pairs and applied positive samples and the same number of selected negative samples to fine-tune DBN to obtain the final predicted scores. Compared with the previous supervised models that only use pairs with known label for training, DBNMDA innovatively utilizes the information of all miRNA-disease pairs during the pre-training process. This step could reduce the impact of too few known associations on prediction accuracy to some extent. DBNMDA achieves the AUC of 0.9104 based on global leave-one-out cross validation (LOOCV), the AUC of 0.8232 based on local LOOCV and the average AUC of 0.9048 ± 0.0026 based on 5-fold cross validation. These AUCs are better than other previous models. In addition, three different types of case studies for three diseases were implemented to demonstrate the accuracy of DBNMDA. As a result, 84% (breast neoplasms), 100% (lung neoplasms) and 88% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by recent literature. Therefore, we could conclude that DBNMDA is an effective method to predict potential miRNA-disease associations.