AIMC Topic: Machine Learning

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The continuous evolution of biomolecular force fields.

Structure (London, England : 1993)
Biomolecular force fields have continuously evolved to improve their accuracy and broaden their applications in biological and therapeutic discoveries. The rapid adaptation of advanced computational technology, in particular the recent deep learning ...

LumiCharge: Spherical Harmonic Convolutional Networks for Atomic Charge Prediction in Drug Discovery.

The journal of physical chemistry letters
Atomic charge is crucial in drug design for analyzing reactive sites and interactions between ligands and targets. While quantum mechanical methods offer high accuracy, they are generally computationally costly. Conversely, empirical approaches, whil...

Exploring T-cell metabolism in tuberculosis: development of a diagnostic model using metabolic genes.

European journal of medical research
OBJECTIVES: The early diagnosis and immunoregulatory mechanisms of active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remain unclear, and the role of metabolic genes in host-pathogen interactions requires further investigation.

Next-generation cancer therapeutics: unveiling the potential of liposome-based nanoparticles through bioinformatics.

Mikrochimica acta
Cancer remains one of the most deadly diseases in the world, requiring constant growth and improvements in therapeutic strategies. Traditional cancer treatments, such as chemotherapy, radiotherapy, and surgery, have limitations like off-target releas...

A novel Swin transformer based framework for speech recognition for dysarthria.

Scientific reports
Dysarthria frequently occurs in individuals with disorders such as stroke, Parkinson's disease, cerebral palsy, and other neurological disorders. Well-timed detection and management of dysarthria in these patients is imperative for efficiently handli...

Predicting Coronary Heart Disease Using Data Mining and Machine Learning Solutions.

Anais da Academia Brasileira de Ciencias
This research focuses on predicting cardiovascular disease using machine learning classification strategies. The study presents a unique approach by integrating multiple machine learning techniques, leveraging the strengths of Random Forest and Gradi...

Analysis of food safety based on machine learning: A comprehensive review and future prospects.

Food chemistry
Food safety challenges escalate with global population growth and complex supply chains. Traditional analytical methods, though precise, face limitations in speed and adaptability. Machine learning (ML) offers data-driven solutions, excelling in cont...

High-Sensitivity Detection of C-Peptide Biomarker for Diabetes by Solid-State Nanopore Using Machine Learning Identification.

The journal of physical chemistry letters
Accurate and early detection of C-peptide, a stable biomarker indicative of diabetes, is crucial for disease diagnosis, treatment, and prevention. This study explores a novel detection methodology using solid-state nanopore technology coupled with ma...

Artificial Intelligence in cancer epigenomics: a review on advances in pan-cancer detection and precision medicine.

Epigenetics & chromatin
DNA methylation is a fundamental epigenetic modification that regulates gene expression and maintains genomic stability. Consequently, DNA methylation remains a key biomarker in cancer research, playing a vital role in diagnosis, prognosis, and tailo...

AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.

Clinical and experimental medicine
The SARS-CoV-2 RNA virus, with its rapid spread and frequent genetic changes, has posed unparalleled obstacles for public health and treatment efforts. Early diagnosis of the disease and the development of effective treatment strategies are the main ...