AIMC Topic: Machine Learning

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Molecular structure of NRG-1 protein and its impact on adult hypertension and heart failure: A new clinical Indicator diagnosis based on advanced machine learning.

International journal of biological macromolecules
The purpose of this study was to investigate the molecular structure of NRG-1 protein and its mechanism of action in adult hypertensive heart failure. The amino acid sequence of NRG-1 protein was analyzed by bioinformatics method. High-throughput seq...

Rapid metabolic profiling and authentication of Cordyceps using ambient ionization mass spectrometry and machine learning.

Analytical and bioanalytical chemistry
Cordyceps sinensis, a symbiotic organism formed between a fungus and an insect, is celebrated for its substantial medicinal benefits and economic significance in traditional Chinese medicine. However, the market for Cordyceps sinensis is rife with co...

[Transformation of free-text radiology reports into structured data].

Radiologie (Heidelberg, Germany)
BACKGROUND: The rapid development of large language models (LLMs) opens up new possibilities for the automated processing of medical texts. Transforming unstructured radiology reports into structured data is crucial for efficient use in clinical deci...

Machine learning for the early prediction of long-term cognitive outcome in autoimmune encephalitis.

Journal of psychosomatic research
BACKGROUND AND OBJECTIVE: Autoimmune encephalitis (AE) is an immune-mediated disease. Some patients experience persistent cognitive deficits despite receiving immunotherapy. We aimed to develop a prediction model for long-term cognitive outcomes in p...

Review on computational methods for the detection and classification of Parkinson's Disease.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: The worldwide estimates reveal two-fold increase in incidence of Parkinson's disease (PD) over 25 years. The two-fold increased incidence and lack of proper treatment uplifted a compelling solicitude, nagging towards accurat...

Evaluation of factors predicting transition from prediabetes to diabetes among patients residing in underserved communities in the United States - A machine learning approach.

Computers in biology and medicine
INTRODUCTION: Over one-third of the population in the United States (US) has prediabetes. Unfortunately, underserved population in the United States face a higher burden of prediabetes compared to urban areas, increasing the risk of stroke and heart ...

Of Lyme disease and machine learning in a One Health world.

American journal of veterinary research
OBJECTIVE: Lyme disease is a vector-borne emerging zoonosis in Ontario driven by human population growth and climate change. Lyme disease is also a prime example of the One Health concept. While little can be done to immediately reverse climate chang...

Data-Driven Insights into Resin Screening for Targeted Per- and Polyfluoroalkyl Substances Removal Using Machine Learning.

Environmental science & technology
In this study, we address the challenge of screening resins and optimizing operation conditions for the removal of 43 perfluoroalkyl and polyfluoroalkyl substances (PFASs), spanning both long- and short-chain fluorocarbon variants, across diverse wat...

AGDIFF: Attention-Enhanced Diffusion for Molecular Geometry Prediction.

Journal of chemical information and modeling
Accurate prediction of molecular geometries is crucial for drug discovery and materials science. Existing fast conformer prediction algorithms often rely on approximate empirical energy functions, resulting in low accuracy. More accurate methods like...

Classifying and fact-checking health-related information about COVID-19 on Twitter/X using machine learning and deep learning models.

BMC medical informatics and decision making
BACKGROUND: Despite recent progress in misinformation detection methods, further investigation is required to develop more robust fact-checking models with particular consideration for the unique challenges of health information sharing. This study a...