https://www.selleckchem.com/products/azd5305.html The amount of available data, which can facilitate answering scientific research questions, is growing. However, the different formats of published data are expanding as well, creating a serious challenge when multiple datasets need to be integrated for answering a question. This paper presents a semi-automated framework that provides semantic enhancement of biomedical data, specifically gene datasets. The framework involved a concept recognition task using machine learning, in combination with the BioPortal annotator. Compared to using methods which require only the BioPortal annotator for semantic enhancement, the proposed framework achieves the highest results. Using concept recognition combined with machine learning techniques and annotation with a biomedical ontology, the proposed framework can provide datasets to reach their full potential of providing meaningful information, which can answer scientific research questions. Using concept recognition combined with machine learning techniques and annotation with a biomedical ontology, the proposed framework can provide datasets to reach their full potential of providing meaningful information, which can answer scientific research questions. Findings from randomized clinical trials mayhave limited generalizability to patients treated in routine clinical practice. This study examined the effectiveness of first-line palbociclib plus letrozole versus letrozole alone on survival outcomes in patients with hormone receptor-positive (HR+)/human epidermal growth factor receptor-negative (HER2-) metastatic breast cancer (MBC) treated in routine clinical practice in the USA. This was a retrospective observational analysis of electronic health records within the Flatiron Health Analytic Database. A total of 1430 patients with ≥ 3 months of follow-up received palbociclib plus letrozole or letrozole alone in the first-line setting between February 3, 2015, and February 28, 2019