Toward an Integrated Machine Learning Model of a Proteomics Experiment
Sign Up to like & getrecommendations! Published in 2023 at "Journal of Proteome Research"
DOI: 10.1021/acs.jproteome.2c00711
Abstract: In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals… read more here.
Keywords: learning model; model proteomics; machine; machine learning ... See more keywords
An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction
Sign Up to like & getrecommendations! Published in 2021 at "Engineering Applications of Computational Fluid Mechanics"
DOI: 10.1080/19942060.2020.1861987
Abstract: Monitoring the water contaminants is of utmost importance in water resource management. Prediction of the total dissolved solid (TDS) is particularly essential for water quality management and plan... read more here.
Keywords: machine learning; integrated machine; noise suppression; learning noise ... See more keywords
Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Personalized Medicine"
DOI: 10.3390/jpm11070597
Abstract: In light of recent advancements in machine learning, personalized medicine using predictive algorithms serves as an essential paradigmatic methodology. Our goal was to explore an integrated machine learning and genome-wide analysis approach which targets the… read more here.
Keywords: machine; genome wide; medicine; machine learning ... See more keywords
Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Personalized Medicine"
DOI: 10.3390/jpm12050756
Abstract: Our study aims to develop an effective integrated machine learning (ML) scheme to predict vascular events and bleeding in patients with nonvalvular atrial fibrillation taking dabigatran and identify important risk factors. This study is a… read more here.
Keywords: machine learning; learning scheme; patients nonvalvular; analysis ... See more keywords