biostudies-literatureUnknown13Shi S<b>Background:</b> Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predicting the risk of sepsis. By combining bioinformatics with machine learning methods, we have attempted to overcome current challenges in predicting disease risk using transcriptomic data. <b>Methods:</b> High-throughput sequencing transcriptomic data processing and gene annotation were performed using R software. Machine learning models were constructed, and model performance was evaluated by machine learning methods in Python. The models were visualized and interpreted using the Shapley AFrontiers in genetics979529https://www.ebi.ac.uk/biostudies/studies/S-EPMC9490444biostudies-literatureAn application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data.PMC9490444Pan XZheng JShi SLin WLin XZhang LWang XZhuang YfalseAn application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data.<b>Background:</b> Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predicting the risk of sepsis. By combining bioinformatics with machine learning methods, we have attempted to overcome current challenges in predicting disease risk using transcriptomic data. <b>Methods:</b> High-throughput sequencing transcriptomic data processing and gene annotation were performed using R software. Machine learning models were constructed, and model performance was evaluated by machine learning methods in Python. The models were visualized and interpreted using the Shapley A2022-01-01T00:00:00Z20222025-04-21T21:28:59.393Z2025-04-05T18:25:06.566ZS-EPMC94904443615997910.3389/fgene.2022.979529