AIMC Topic: Machine Learning

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When Simulations Meet Machine Learning: Redefining Molecular Docking for Protein-Glycosaminoglycan Systems.

Journal of computational chemistry
Glycosaminoglycans (GAGs) are linear, negatively charged carbohydrates that modulate enzymatic activity in the extracellular matrix. Their high flexibility and specificity in protein-GAG interactions pose challenges for both experimental and computat...

Predicting treatment outcome in congenital adrenal hyperplasia using urine steroidomics and machine learning.

European journal of endocrinology
OBJECTIVE: Treatment monitoring of individuals with congenital adrenal hyperplasia (CAH) remains unsatisfactory. Comprehensive 24 h urine steroid profiling provides detailed insight into adrenal steroid pathways. We investigated whether 24 h urine st...

Machine learning-assisted ratiometric fluorescence sensor array for recognition of multiple quinolones antibiotics.

Food chemistry
Developing analytical methods for simultaneous detection of multiple antibiotic residues is crucial for environmental protection and human health. In this study, a dual lanthanide fluorescence probe (GDP-Eu-Tb) based on nucleotides has been designed....

Machine learning-assisted aroma profile prediction in Jiang-flavor baijiu.

Food chemistry
The complex flavor of Jiang-flavor Baijiu (JFB) arises from the interaction of hundreds of compounds at both physicochemical and sensory levels, making accurate perception challenging. Modern machine learning techniques offer precise and scientific a...

Machine Learning-Assisted Multicolor Fluorescence Assay for Visual Data Acquisition and Intelligent Inspection of Multiple Food Hazards Regardless of Matrix Interference.

ACS sensors
Regarding the significant health risks of pesticide residue in foods, while current sensors still suffer from limited efficiency and stability, as well as difficulties in qualitative identification and quantitative detection of mixtures, development ...

Use of machine learning techniques to predict poor survival after hematopoietic cell transplantation for myelofibrosis.

Blood
With the incorporation of effective therapies for myelofibrosis (MF), accurately predicting outcomes after allogeneic hematopoietic cell transplantation (allo-HCT) is crucial for determining the optimal timing for this procedure. Using data from 5183...

Prediction of remaining surgery duration based on machine learning methods and laparoscopic annotation data.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: The operating room is a fast-paced and demanding environment. Among the various factors involved in its optimization, predicting surgery duration is critical for scheduling and resource organization, ultimately resulting in improved quali...

Journey from image acquisition to biological insight: handling and analyzing large volumes of light-sheet imaging data.

Microscopy (Oxford, England)
Recent advancements in imaging technologies have enabled the acquisition of high-quality, voluminous, multidimensional image data. Among these, light-sheet microscopy stands out for its ability to capture dynamic biological processes over extended pe...

Comparative Analysis of Automated vs. Expert-Designed Machine Learning Models in Age-Related Macular Degeneration Detection and Classification.

Turkish journal of ophthalmology
OBJECTIVES: To compare the effectiveness of expert-designed machine learning models and code-free automated machine learning (AutoML) models in classifying optical coherence tomography (OCT) images for detecting age-related macular degeneration (AMD)...

Deep mechanism design: Learning social and economic policies for human benefit.

Proceedings of the National Academy of Sciences of the United States of America
Human society is coordinated by mechanisms that control how prices are agreed, taxes are set, and electoral votes are tallied. The design of robust and effective mechanisms for human benefit is a core problem in the social, economic, and political sc...