AIMC Topic: Machine Learning

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Groundwater quality and risk in the Ganga River Basin: an integrated machine learning appraisal.

Environmental geochemistry and health
Groundwater supports the livelyhoods of hundreds of millions across the Ganga River Basin (GRB), yet its quality is increasingly stressed by geogenic and anthropogenic factors. Using a high-density 2022-dataset from 3417 wells, this study integrates ...

Construction and temporal external validation of interpretable machine-learning models for predicting tigecycline-associated hypofibrinogenemia.

European journal of clinical pharmacology
BACKGROUND AND PURPOSE: There is a paucity of available clinical tools with which to accurately predict the risk of tigecycline-associated hypofibrinogenemia, an adverse reaction with a high incidence and serious consequences. This study aimed to dev...

Machine Learning for Separating Dopamine and Octopamine Electrochemical Signals in Drosophila.

Analytical chemistry
, the fruit fly, uses the neurotransmitters dopamine and octopamine to mediate learning, enabling adaptive behaviors such as reward seeking and punishment avoidance. Their colocalization in the mushroom bodies makes it challenging to study their indi...

Machine learning-powered single-molecule cancer diagnosis using DNA origami tags.

Science advances
Single-molecule detection (SMD) holds considerable promise in biomedical research. Although atomic force microscopy (AFM) provides an important technique with nanoscale resolution for SMD, its broader application is limited by labeling challenges and...

Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data.

Journal of chemical information and modeling
Machine-learned interatomic models represent a significant advancement in simulation methods, extending the predictive ability of first-principles methods to previously inaccessible length and time scales. However, the data-driven nature of these mod...

What Drives Microplastic Exposure in Human Blood and Feces? Machine Learning Reveals Potential Key Influencing Factors.

Environmental science & technology
Microplastics are pervasive environmental pollutants, making human exposure unavoidable. Although previous studies have detected microplastics in human blood and feces, these investigations were limited by small sample sizes and key contributors to m...

AlphaFold-RandomWalk and AlphaFold-Ensemble: Sampling Alternative Protein Conformations with Perturbed Versions of AlphaFold.

Journal of chemical information and modeling
The ability of proteins to adopt multiple conformations is fundamental to their biological function. With the advent of AlphaFold, machine learning (ML)-based methods have extended their capabilities to more broadly sample this intrinsic conformation...

How perceived stress and social support shape non-communicable disease risks beyond traditional factors: a machine learning perspective.

BMC public health
BACKGROUND: Psychosocial factors such as perceived stress and social support have been increasingly recognized as significant contributors to non-communicable diseases (NCDs). However, their predictive value in comparison to traditional risk factors ...

Predictive Value of Machine Learning in Knee Osteoarthritis Progression: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: Machine learning (ML) has been investigated for its predictive value in knee osteoarthritis (KOA) progression. However, systematic evidence on the effectiveness of ML is still lacking, posing a challenge to precision prevention.