Molecular Applications of Deep Learning in Kidney Diseases
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".
Deadline for manuscript submissions: 30 October 2024 | Viewed by 74
Special Issue Editors
Interests: epidemiology of chronic kidney disease; clinical trials in nephrology; prognostic research in nephrology
Special Issues, Collections and Topics in MDPI journals
Interests: adrenocortical cancer; estrogen-dependent tumors; polyphenols; phyto-estrogens
Interests: clinical nephrology; hemodialysis; kidney transplantation; dialysis; chronic renal failure; transplantation; renal; kidney; chronic kidney failure; peritoneal dialysis; renal disease; kidney disease; fibrosis; transplant immunology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We kindly invite you to contribute to the Special Issue titled “Molecular Applications of Deep Learning in Kidney Diseases” with a potential study among observational studies, reviews and translational research. Current molecular research in nephrology is mainly based on traditional biomarkers that predict the risk of future events, in particular, the impairment of kidney damage with the consequent progression to kidney failure (KF) or cardiovascular (CV) events or death. The most used biomarkers aim to identify a molecular pathway of kidney disease; even more importantly, the molecular patterns of response to the main drugs are the level of kidney function (estimated Glomerular Filtration Rate, eGFR) and the presence of proteins in urine (so-called albuminuria or proteinuria). What has been discovered is that, despite the high prediction power of eGFR and proteinuria and the use of novel drugs to delay the progression of kidney disease and reduce CV risk, a significant number of patients remain at high risk of worse outcomes. This is due to several reasons encompassing the partial (or even absent) response to nephroprotective treatment, the action of other (and not yet discovered) risk factors that trigger kidney damage, and the use of kidney measures that reveal already advanced, and thus potentially untreatable, damage. Deep learning is gaining momentum in the context of nephrology research. Deep learning tools have shown usefulness in improving the comprehension of molecular mechanisms underlying the risk of kidney disease and the prediction of future events and response to therapies. The aim of this Special Issue is to collect data and articles that, considered together, may help researchers and physicians to improve the current and future applications of deep learning for research on kidney disease.
Dr. Michele Provenzano
Prof. Dr. Vincenzo Pezzi
Prof. Dr. Gianluigi Zaza
Guest Editors
Manuscript Submission Information
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Keywords
- kidney biomarkers
- kidney diseases
- predictive biomarkers
- deep learning
- personalized therapy
- digital health
