Targeted deep learning classification and feature extraction for clinical diagnosis.

ABSTRACT: Protein biomarkers can be used to characterize symptom classes, which describe the metabolic or immunodeficient state of patients during the progression of a specific disease. Recent literature has shown that machine learning methods can complement traditional clinical methods in identifying biomarkers. However, many machine learning frameworks only apply narrowly to a specific archetype or subset of diseases. In this paper, we propose a feature extractor which can discover protein biomarkers for a wide variety of classification problems. The feature extractor uses a special type of deep learning model, which discovers a latent space that allows for optimal class separation and enhanced class cluster identity. The extracted biomarkers can then be used to train highly accurate supervised le

SUBMITTER: Tsai Y 

PROVIDER: S-EPMC10590983 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature