AIMC Topic: Metformin
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Predicting Metformin Efficacy in Improving Insulin Sensitivity Among Women With Polycystic Ovary Syndrome and Insulin Resistance: A Machine Learning Study.
Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists
Aug 5, 2024
OBJECTIVE: Metformin is clinically effective in treating polycystic ovary syndrome (PCOS) with insulin resistance (IR), while its efficacy varies among individuals. This study aims to develop a machine learning model to predict the efficacy of metfor...
A transparent machine learning algorithm uncovers HbA1c patterns associated with therapeutic inertia in patients with type 2 diabetes and failure of metformin monotherapy.
International journal of medical informatics
Jul 15, 2024
AIMS: This study aimed to identify and categorize the determinants influencing the intensification of therapy in Type 2 Diabetes (T2D) patients with suboptimal blood glucose control despite metformin monotherapy.
Artificial intelligence derived categorizations significantly improve HOMA IR/β indicators: Combating diabetes through cross-interacting drugs.
Computers in biology and medicine
Jul 4, 2024
Improvements in the homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis model assessment of beta-cell function (HOMA-β) significantly reduce the risk of disabling diabetic pathies. Nanoparticle (AuNP-AgNP)-metformin are conce...
Causal prior-embedded physics-informed neural networks and a case study on metformin transport in porous media.
Water research
Jun 22, 2024
This study introduces a novel approach to transport modelling by integrating experimentally derived causal priors into neural networks. We illustrate this paradigm using a case study of metformin, a ubiquitous pharmaceutical emerging pollutant, and i...
Integrated biomarker profiling for predicting the response of type 2 diabetes to metformin.
Diabetes, obesity & metabolism
Jun 3, 2024
AIM: To explore biomarkers that can predict the response of type 2 diabetes (T2D) patients to metformin at an early stage to provide better treatment for T2D.
Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications.
International journal of molecular sciences
Sep 8, 2022
In this study, we provide a systems biology method to investigate the carcinogenic mechanism of oral squamous cell carcinoma (OSCC) in order to identify some important biomarkers as drug targets. Further, a systematic drug discovery method with a dee...
Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System.
BMC pharmacology & toxicology
Jul 13, 2022
BACKGROUND: With diabetes incidence growing globally and metformin still being the first-line for its treatment, metformin's toxicity and overdose have been increasing. Hence, its mortality rate is increasing. For the first time, we aimed to study th...
Simultaneous ultra-trace quantitative colorimetric determination of antidiabetic drugs based on gold nanoparticles aggregation using multivariate calibration and neural network methods.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Mar 12, 2020
In this study, a simple and rapid method was investigated for the simultaneous ultra-trace colorimetric determination of Metformin (MET) and Sitagliptin (STG) based on the aggregation of gold nanoparticles (AuNPs). The Morphology and size distributio...
Predicting diabetes second-line therapy initiation in the Australian population via time span-guided neural attention network.
PloS one
Oct 18, 2019
INTRODUCTION: The first line of treatment for people with Diabetes mellitus is metformin. However, over the course of the disease metformin may fail to achieve appropriate glycemic control, and a second-line therapy may become necessary. In this pape...
Using Unlabeled Data to Discover Bivariate Causality with Deep Restricted Boltzmann Machines.
IEEE/ACM transactions on computational biology and bioinformatics
Nov 5, 2018
An important question in microbiology is whether treatment causes changes in gut flora, and whether it also affects metabolism. The reconstruction of causal relations purely from non-temporal observational data is challenging. We address the problem ...
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