AIMC Topic: Machine Learning

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Predictive modeling approach using machine learning-integrated design of experiments in quality by design for optimizing resveratrol-loaded polymeric nanoparticle formulation.

International journal of pharmaceutics
This study aimed to explore the potential of a Machine learning (ML)-integrated Quality by design (QbD) process to formulate resveratrol (RES)-loaded polymeric nanoparticles (RES-PNPs) for potential use in transdermal drug delivery. The RES-PNPs were...

Integrating artificial intelligence and physiologically based pharmacokinetic modeling to predict in vitro and in vivo fate of amorphous solid dispersions.

Journal of controlled release : official journal of the Controlled Release Society
Amorphous solid dispersions (ASDs) have emerged as a pivotal strategy in enhancing the dissolution profiles of poorly water-soluble drugs. Although the apparent dissolution rate (both molecular and colloidal drugs) within ASDs has been determined in ...

FTIR spectroscopy imaging coupled with machine learning reveals biochemical changes in the brains of diabetic mice.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Diabetic encephalopathy is a progressive complication of type 2 diabetes, yet its region-specific biochemical changes remain unclear. In this study, we applied Fourier Transform Infrared Microspectroscopy (FTIRM) to assess metabolic alterations in th...

Data-driven cognitive subtypes in major depressive disorder: Grey matter atrophy in the left fusiform gyrus and cerebellum.

Journal of affective disorders
BACKGROUND: This study aims to apply a semi-supervised machine learning approach for classifying major depressive disorder (MDD) patients into more homogeneous cognitive subtypes based on multidimensional cognitive profiles, and to perform multimodal...

Escherichia coli O157:H7 survival and transfer dynamics on cold chain packaging materials: An integrated experimental-machine learning framework.

International journal of food microbiology
This study presents a comprehensive investigation of Escherichia coli O157:H7 survival and transfer on six cold chain packaging materials through experimental characterization and machine learning modeling. Survival experiments revealed significant m...

Establishing Clinically Distinct Patient Treatment Subgroups Following Anterior Cruciate Ligament Reconstruction: A Machine Learning Clustering Analysis.

The American journal of sports medicine
BACKGROUND: Treatment decisions in patients with anterior cruciate ligament (ACL) injuries are influenced by multiple factors, such as the desire to return to sports or symptomatic instability. Identifying the differential treatment effect of ACL rec...

Quantum Descriptor-Based Machine-Learning Modeling of Thermal Hazard of Cyclic Sulfamidates.

Journal of chemical information and modeling
Cyclic sulfamidates are commonly used building blocks in organic synthesis. Correct classification of their thermal criticality is crucial for the safe use of these compounds in process development and scale-up. In this study, building on our earlier...

Machine learning in understanding environmental variability of vibriosis in coastal waters.

Applied and environmental microbiology
comprise ecologically significant bacteria that thrive in warm, moderately saline water, and their incidence and proliferation are strongly influenced by environmental factors. In recent years, . infections have been reported more frequently and ove...

Machine Learning on the Impacts of Mutations in the SARS-CoV-2 Spike RBD on Binding Affinity to Human ACE2 Based on Deep Mutational Scanning Data.

Biochemistry
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to accumulate mutations in the spike receptor-binding domain (RBD) region, leading to the emergence of new variants that potentially change the binding affinity for the human angi...

JointDiffusion: Joint representation learning for generative, predictive, and self-explainable AI in healthcare.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Joint machine learning models that allow synthesizing and classifying data often offer uneven performance between those tasks or are unstable to train. In this work, we depart from a set of empirical observations that indicate the usefulness of inter...