AIMC Topic: Machine Learning

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ChatGPT Combining Machine Learning for the Prediction of Nanozyme Catalytic Types and Activities.

Journal of chemical information and modeling
The design of nanozymes with superior catalytic activities is a prerequisite for broadening their biomedical applications. Previous studies have exerted significant effort in theoretical calculation and experimental trials for enhancing the catalytic...

Cognitive driven gait freezing phase detection and classification for neuro-rehabilitated patients using machine learning algorithms.

Journal of neuroscience methods
BACKGROUND: The significance of diagnosing illnesses associated with brain cognitive and gait freezing phase patterns has led to a recent surge in interest in the study of gait for mental disorders. A more precise and effective way to characterize an...

Diagnosing contact dermatitis using machine learning: A review.

Contact dermatitis
BACKGROUND: Machine learning (ML) offers an opportunity in contact dermatitis (CD) research, where with full clinical picture, may support diagnosis and patch test accuracy.

Thrombomodulin as a potential diagnostic marker of acute myocardial infarction and correlation with immune infiltration: Comprehensive analysis based on multiple machine learning.

Transplant immunology
BACKGROUND: Acute myocardial infarction (AMI) is a global health problem with high mortality. Early diagnosis can prevent the development of AMI and provide valuable information for subsequent treatment. Angiogenesis has been shown to be a critical f...

Integrated biomarker profiling for predicting the response of type 2 diabetes to metformin.

Diabetes, obesity & metabolism
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.

Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning.

Marine environmental research
Oceanic dissolved oxygen (DO) is crucial for oceanic material cycles and marine biological activities. However, obtaining subsurface DO values directly from satellite observations is limited due to the restricted observed depth. Therefore, it is esse...

Identifying patients with undiagnosed small intestinal neuroendocrine tumours in primary care using statistical and machine learning: model development and validation study.

British journal of cancer
BACKGROUND: Neuroendocrine tumours (NETs) are increasing in incidence, often diagnosed at advanced stages, and individuals may experience years of diagnostic delay, particularly when arising from the small intestine (SI). Clinical prediction models c...

Identification of ion channel-related genes as diagnostic markers and potential therapeutic targets for osteoarthritis through bioinformatics and machine learning-based approaches.

Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals
BACKGROUND: Osteoarthritis (OA) is a debilitating joint disorder characterized by the progressive degeneration of articular cartilage. Although the role of ion channels in OA pathogenesis is increasingly recognized, diagnostic markers and targeted th...

Raman Spectra of Amino Acids and Peptides from Machine Learning Polarizabilities.

Journal of chemical information and modeling
Raman spectroscopy is an important tool in the study of vibrational properties and composition of molecules, peptides, and even proteins. Raman spectra can be simulated based on the change of the electronic polarizability with vibrations, which can n...

Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning.

BMC musculoskeletal disorders
BACKGROUND: Prior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromised mobility. Additionally, fractures among the elderly are often considered a common outcome of both...