Clinical-oriented tacrolimus dosing algorithms in kidney transplant based on genetic algorithm and deep forest

The proposed approach can provide a potential reference to algorithm-based automatic pipeline methods for drug dosing prediction and analysis in clinical practice.

Authors

Institutions

Publication Details

Journal
Frontiers in Pharmacology
Published
2025-08-28
DOI
https://doi.org/10.3389/fphar.2025.1656197
Citations
2
Primary Topic
Renal Transplantation Outcomes and Treatments
Type
article
Field-Weighted Citation Impact
3.97

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Clinical-oriented tacrolimus dosing algorithms in kidney transplant based on genetic algorithm and deep forest

X. J. Wang, Q Li, Jianliang Min, Yue Lu et al.
2 citations
Frontiers in Pharmacology
Renal Transplantation Outcomes and Treatments
3.97
article

Clinical-oriented tacrolimus dosing algorithms in kidney transplant based on genetic algorithm and deep forest

X. J. Wang, Q Li, Jianliang Min, Yue Lu, Guodong Chen, Weijie Lai
article en
2 citations

Abstract

The proposed approach can provide a potential reference to algorithm-based automatic pipeline methods for drug dosing prediction and analysis in clinical practice.

Frontiers in PharmacologyVol. 16
Sun Yat-sen University (CN), Jiaying University (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Sun Yat-sen University Cancer Center (CN)
Sun Yat-sen University, Natural Science Foundation of Guangdong Province, Jiaying University
Openalex Percentile: Top 6%
Renal Transplantation Outcomes and Treatments
3.97
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.