Advancing Chinese biomedical text mining with community challenges.

OBJECTIVE: This study aims to review the recent advances in community challenges for biomedical text mining in China. METHODS: We collected information of evaluation tasks released in community challenges of biomedical text mining, including task description, dataset description, data source, task type and related links. A systematic summary and comparative analysis were conducted on various biomedical natural language processing tasks, such as named entity recognition, entity normalization, attribute extraction, relation extraction, event extraction, text classification, text similarity, knowledge graph construction, question answering, text generation, and large language model evaluation. RESULTS: We identified 39 evaluation tasks from 6 community challenges that spanned from 2017 to 2023. Our analysis revealed the diverse range of evaluation task types and data sources in biomedical text mining. We explored the potential clinical applications of these community challenge tasks from a translational biomedical informatics perspective. We compared with their English counterparts, and discussed the contributions, limitations, lessons and guidelines of these community challenges, while highlighting future directions in the era of large language models. CONCLUSION: Community challenge evaluation competitions have played a crucial role in promoting technology innovation and fostering interdisciplinary collaboration in the field of biomedical text mining. These challenges provide valuable platforms for researchers to develop state-of-the-art solutions.

PPCRKB: a risk factor knowledge base of postoperative pulmonary complications.

Postoperative pulmonary complications (PPCs) are highly heterogeneous disorders with diverse risk factors frequently occurring after surgical interventions, resulting in significant financial burdens, prolonged hospitalization and elevated mortality rates. Despite the existence of multiple studies on PPCs, a comprehensive knowledge base that can effectively integrate and visualize the diverse risk factors associated with PPCs is currently lacking. This study aims to develop an online knowledge platform on risk factors for PPCs (Postoperative Pulmonary Complications Risk Factor Knowledge Base, PPCRKB) that categorizes and presents the risk and protective factors associated with PPCs, as well as to facilitate the development of individualized prevention and management strategies for PPCs based on the needs of each investigator. The PPCRKB is a novel knowledge base that encompasses all investigated potential risk factors linked to PPCs, offering users a web-based platform to access these risk factors. The PPCRKB contains 2673 entries, 915 risk factors that have been categorized into 11 distinct groups. These categories include habit and behavior, surgical factors, anesthetic factors, auxiliary examination, environmental factors, clinical status, medicines and treatment, demographic characteristics, psychosocial factors, genetic factors and miscellaneous factors. The PPCRKB holds significant value for PPC research. The inclusion of both quantitative and qualitative data in the PPCRKB enhances the ability to uncover new insights and solutions related to PPCs. It could provide clinicians with a more comprehensive perspective on research related to PPCs in future. Database URL: http://sysbio.org.cn/PPCs.

PON-All: Amino Acid Substitution Tolerance Predictor for All Organisms.

Genetic variations are investigated in human and many other organisms for many purposes (e.g., to aid in clinical diagnosis). Interpretation of the identified variations can be challenging. Although some dedicated prediction methods have been developed and some tools for human variants can also be used for other organisms, the performance and species range have been limited. We developed a novel variant pathogenicity/tolerance predictor for amino acid substitutions in any organism. The method, PON-All, is a machine learning tool trained on human, animal, and plant variants. Two versions are provided, one with Gene Ontology (GO) annotations and another without these details. GO annotations are not available or are partial for many organisms of interest. The methods provide predictions for three classes: pathogenic, benign, and variants of unknown significance. On the blind test, when using GO annotations, accuracy was 0.913 and MCC 0.827. When GO features were not used, accuracy was 0.856 and MCC 0.712. The performance is the best for human and plant variants and somewhat lower for animal variants because the number of known disease-causing variants in animals is rather small. The method was compared to several other tools and was found to have superior performance. PON-All is freely available at http://structure.bmc.lu.se/PON-All and http://8.133.174.28:8999/.