Preprocessing Pipelines for Reliable Models

Effective raw data is important for predicting reliable and high -performing learning models, especially with complex or domain -specific data sets. This review summarizes five recent studies on data lines in different fields. In the health care system, the EHR-QC pipeline improves electronic health records through automatic standardization. In biochemicals, the RNA-CIR pipeline generally increases the generalization of studies through normalization and improvement of batch effect. Time series research emphasizes the importance of research treatment and practical evaluation. Benchmarking of the computer tool reveals commercial rooms in scalability, efficiency and function. Finally, the Talita pipeline shows noise, modular, reproductive and confident functional extraction for signal-rich data. Overall, effective pipelines should be modular, domain -and should be considered for accuracy, scalability and reliability, guide industrial applications and future research.