AIMC Topic: Machine Learning

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Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis.

BMC public health
Cardiovascular disease (CVD) is a primary cause of death in India, accounting for a significant portion of the global CVD burden. This study looks at statistics on heart disease mortality from the Institute for Health Metrics and Evaluation (IHME) fr...

High-frequency monitoring enables machine learning-based forecasting of acute child malnutrition for early warning.

Proceedings of the National Academy of Sciences of the United States of America
The number of acutely food insecure people worldwide has doubled since 2017, increasing demand for early warning systems (EWS) that can predict food emergencies. Advances in computational methods, and the growing availability of near-real time remote...

Using Machine Learning to Fast-Track Peptide Nanomaterial Discovery.

ACS nano
Peptides can serve as building blocks for supramolecular materials because of their unique ability to self-assemble, offering potential applications in drug delivery, tissue engineering, and nanotechnology. In this review, we describe peptide self-as...

Comment on "Differential Diagnosis of Urinary Cancers by Surface-Enhanced Raman Spectroscopy and Machine Learning".

Analytical chemistry
This article discusses current research on the detection of urinary cancers using surface-enhanced Raman spectral analysis of human serum by Song et al., which was published in the Analytical Chemistry journal. Despite the high performance of the sug...

Machine Learning Reveals Key Adsorption Mechanisms for Oxyanions Based on Combination of Experimental and Published Literature Data.

Environmental science & technology
The development of new adsorbents for water treatment often involves complex adsorption mechanisms, whose individual contributions are unclear, thereby limiting the understanding of adsorption driving forces, making it difficult to achieve precise de...

Segment-Weighting Similarity-Based Fragment-Learning Model for Single-Cell Raman Spectral Analysis.

Analytical chemistry
Raman spectroscopy provides intrinsic biochemical profiles of all cellular biomolecules in a segmented manner, promising nondestructive and label-free phenotyping at the single-cell level. However, current analytical methods rarely utilize spectral b...

NepoIP/MM: Toward Accurate Biomolecular Simulation with a Machine Learning/Molecular Mechanics Model Incorporating Polarization Effects.

Journal of chemical theory and computation
Machine learning force fields offer the ability to simulate biomolecules with quantum mechanical accuracy while significantly reducing computational costs, attracting a growing amount of attention in biophysics. Meanwhile, by leveraging the efficienc...

Development of an Interpretable Machine Learning Model for Neurotoxicity Prediction of Environmentally Related Compounds.

Environmental science & technology
The rising prevalence of nervous system disorders has become a significant global health challenge, with environmental pollutants identified as key contributors. However, the large number of environmental related compounds, combined with the low effi...

Machine Learning-Assisted Prediction and Exploration of the Homogeneous Oxidation of Mercury in Coal Combustion Flue Gas.

Environmental science & technology
Mercury emission from coal combustion flue gas is a significant environmental concern due to its detrimental effects on ecosystems and human health. Elemental mercury (Hg) is the dominant species in flue gas and is hard to immobilize. Therefore, it i...

Decoding the Structure-Activity Relationship of the Dopamine D3 Receptor-Selective Ligands Using Machine and Deep Learning Approaches.

Journal of chemical information and modeling
Dysfunctions of the dopamine D2 and D3 receptors (D2 and D3) are implicated in neuropsychiatric conditions such as Parkinson's disease, schizophrenia, and substance use disorders (SUDs). Evidence indicates that D3-selective ligands can effectively mo...