首页|High-dimensional Biomarker Identification for Scalable and InterpretableDisease Prediction via Machine Learning Models
High-dimensional Biomarker Identification for Scalable and InterpretableDisease Prediction via Machine Learning Models
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – According to news reporting based on a preprint abstract, our journalists obtained thefollowing quote sourced from bi orxiv.org:“Omics data generated from high-throughput technologies and clinical features jo intly impact manycomplex human diseases. Identifying key biomarkers and clinica l risk factors is essential for understandingdisease mechanisms and advancing e arly disease diagnosis and precision medicine.“However, the high-dimensionality and intricate associations between disease out comes and omicsprofiles present significant analytical challenges.“To address these, we propose an ensemble data-driven biomarker identification t ool, Hybrid FeatureScreening (HFS), to construct a candidate feature set for do wnstream advanced machine learningmodels. The pre-screened candidate features f rom HFS are further refined using a computationally efficientpermutation-based feature importance test, forming the comprehensive High-dimensional FeatureImpo rtance Test (HiFIT) framework. Through extensive numerical simulations and real- world applications,we demonstrate HiFITs superior performance in both outcome p rediction and feature importanceidentification. An R package implementing HiFIT is available on GitHub.”
BioinformaticsBiotechnologyBiotechno logy - BioinformaticsCyborgsEmerging TechnologiesInformation TechnologyM achine LearningRisk and Prevention
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