Article

Identifying Proteomic Prognostic Markers for Alzheimer’s Disease with Survival Machine Learning: the Framingham Heart Study

2024-09-23

Abstract excerpt

<h4>Background</h4> Protein abundance levels, sensitive to both physiological changes and external interventions, are useful for assessing the Alzheimer’s disease (AD) risk and treatment efficacy. However, identifying proteomic prognostic markers for AD is challenging by their high dimensionality and inherent correlations. <h4>Methods</h4> Our study analyzed 1128 plasma proteins, measured by the SOMAscan platform,...

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Literature Corpus work: 6db4daa5-e88c-5e62-8113-797899815740
DOI: 10.1101/2024.09.21.24314123

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