Diagnosis of Sepsis by AI-Aided Proteomics Using 2D Electrophoresis Images of Patient Serum Incorporating Transfer Learning for Deep Neural Networks
Open Access
- 23 February 2021
- journal article
- research article
- Published by MDPI AG in Applied Sciences
- Vol. 11 (4), 1967
- https://doi.org/10.3390/app11041967
Abstract
An accuracy of ≥98% was achieved in sepsis diagnosis using serum samples from 30 sepsis patients and 68 healthy individuals and a high-performance two-dimensional polyacrylamide gel electrophoresis (HP-2D-PAGE) method developed here with deep learning and transfer learning algorithms. In this method, small-scale target domain data, which are collected to achieve our objective, are inputted directly into a model constructed with source domain data which are collected from a different domain from the target; target vectors are estimated with the outputted target domain data and applied to refine the model. Recognition performance of small-scale data is improved by reusing all layers, including the output layers of the neural network. Proteomics is generally considered the ultimate bio-diagnostic technique and provides extremely high information density in its two-dimensional electrophoresis images, but extracting the data has posed a basic problem. The present study is expected to solve that problem and will be an important breakthrough for practical utilization and future perspectives of proteomics in clinics after evaluation in clinical settings.Funding Information
- Grants-in-Aid for Scientific Research from the Ministry of Education, Science, Sports and Culture of Japan (C 25462831 and 16K11421, AXA Life Insurance Co.,Ltd., NIMS Nanofabrication Platform in Nanotechnology Platform Project)
This publication has 24 references indexed in Scilit:
- Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock, 2012Intensive Care Medicine, 2013
- Bench-to-bedside review: Future novel diagnostics for sepsis - a systems biology approachCritical Care, 2013
- Gram-negative bacteremia induces greater magnitude of inflammatory response than Gram-positive bacteremiaCritical Care, 2010
- Sepsis biomarkers: a reviewCritical Care, 2010
- Patterns of Correlation of Plasma Ceruloplasmin in SepsisJournal of Surgical Research, 2008
- Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categoriesComputer Vision and Image Understanding, 2007
- Sequential measurement of IL-6 blood levels in patients with systemic inflammatory response syndrome (SIRS)/sepsisCytokine, 2005
- Support-vector networksMachine Learning, 1995
- Automatic classification of two‐dimensional gel electrophoresis pictures by heuristic clustering analysis: A step toward machine learningElectrophoresis, 1988
- MEASUREMENT OF PREALBUMIN AS INDEX OF PROTEIN-CALORIE MALNUTRITIONThe Lancet, 1972