AIMC Topic: Machine Learning
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Active Learning of Atomic Size Gas/Solid Potential Energy Surfaces via Physics Aware Models.
Journal of chemical information and modeling
Aug 12, 2025
We propose an active learning (AL) framework to develop classical force fields (FFs) that accurately model the potential energy surfaces (PES) of gas/solid atomic-scale complexes. A central challenge is integrating AL with flexible, computationally e...
Screening, Validation, and Machine Learning-Based Evaluation of Serum Protein Biomarkers for Esophageal Squamous Cell Carcinoma Based on Single-Cell Subtype-Specific Genes.
Journal of proteome research
Aug 12, 2025
Cellular heterogeneity of epithelial cells and fibroblasts is critical in esophageal squamous cell carcinoma development (ESCC). Identifying dysregulated subtype-specific genes in these cells is essential for early diagnosis and treatment. In this st...
Machine learning-based ensemble of Global climate models and trend analysis for projecting extreme precipitation indices under future climate scenarios.
Environmental monitoring and assessment
Aug 12, 2025
Monitoring changes in climatic extremes is vital, as they influence current and future climate while significantly impacting ecosystems and society. This study examines trends in extreme precipitation indices over an Indian tropical river basin, anal...
Current imaging applications, radiomics, and machine learning modalities of CNS demyelinating disorders and its mimickers.
Journal of neurology
Aug 12, 2025
Distinguishing among neuroinflammatory demyelinating diseases of the central nervous system can present a significant diagnostic challenge due to substantial overlap in clinical presentations and imaging features. Collaboration between specialists, n...
Domain Knowledge Inclusive Monotonic Neural Network Guides Patient-Specific Induction of General Anesthesia Dosing.
A&A practice
Aug 12, 2025
BACKGROUND: Postinduction hypotension is a well-known risk factor for adverse postoperative outcomes. Anesthesiologists estimate anesthetic dosages based on a patient's chart and domain knowledge. Machine learning is increasingly applied in predictin...
Screening for Parkinson's disease using "computer vision".
PloS one
Aug 12, 2025
BACKGROUND: Identifying bradykinesia is crucial for diagnosing Parkinson's disease (PD). Traditionally, the finger-tapping test has been used, relying on subjective assessments by physicians. Computer vision offers a non-contact and cost-effective al...
A machine learning approach for estimating forage maize yield and quality in NW Spain.
PloS one
Aug 12, 2025
Crop models simulate crop growth and development according to different climatic, soil and crop management conditions. The CSM-CERES-Maize model (DSSAT) was adapted to simulate forage maize yields by calibrating the genetic parameters of six cultivar...
Automated machine learning profiling with MAP-HR for quantifying homologous recombination foci in patient samples.
NAR cancer
Aug 11, 2025
Accurate visualization and quantification of homologous recombination (HR)-associated foci in readily available patient samples are critical for identifying patients with HR deficiency (HRD) when they present for care to guide polyADP ribose polymera...
Circulating cell-free RNA signatures for the characterization and diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome.
Proceedings of the National Academy of Sciences of the United States of America
Aug 11, 2025
People living with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) experience heterogeneous and debilitating symptoms that lack sufficient biological explanation, compounded by the absence of accurate, noninvasive diagnostic tools. To add...
Building simplified cancer subtyping and prediction models with glycan gene signatures.
Cell reports methods
Aug 11, 2025
We identified a gene panel comprising 71 glycosyltransferases (GTs) that alter glycan patterns on cancer cells as they become more virulent. When these cancer-pattern GTs (CPGTs) were run through an algorithm trained on The Cancer Genome Atlas, they ...
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