AIMC Topic: Machine Learning

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Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase F-Florbetaben PET and clinical features.

Scientific reports Jul 1, 2025
This study developed machine learning models to predict Aβ positivity in Alzheimer's disease by integrating early-phase F-Florbetaben PET and clinical data to improve diagnostic accuracy. Furthermore, the study explored machine learning models to pre...
Aged, 80 and over Stilbenes Brain Cognitive Dysfunction Alzheimer Disease Female Humans Cognition Male Aniline Compounds Aged Machine Learning Amyloid beta-Peptides Positron-Emission Tomography Middle Aged
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Personalized prediction model generated with machine learning for kidney function one year after living kidney donation.

Scientific reports Jul 1, 2025
Living kidney donors typically experience approximately a 30% reduction in kidney function after donation, although the degree of reduction varies among individuals. This study aimed to develop a machine learning (ML) model to predict serum creatinin...
Creatinine Nephrectomy Glomerular Filtration Rate Kidney Function Tests Living Donors Humans Machine Learning Kidney Transplantation Female Male Adult Middle Aged Kidney
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Integrative machine learning and molecular simulation approaches identify GSK3β inhibitors for neurodegenerative disease therapy.

Scientific reports Jul 1, 2025
Neurodegenerative diseases (NDDs), including Alzheimer's disease (AD) and Parkinson's disease (PD), are a growing global health concern, especially among the elderly, posing significant challenges to well-being and survival. GSK3β, a serine/threonine...
Machine Learning Molecular Docking Simulation Neurodegenerative Diseases Molecular Dynamics Simulation Protein Kinase Inhibitors Glycogen Synthase Kinase 3 beta Humans
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Uncovering memorization effect in the presence of spurious correlations.

Nature communications Jul 1, 2025
Machine learning models often rely on simple spurious features - patterns in training data that correlate with targets but are not causally related to them, like image backgrounds in foreground classification. This reliance typically leads to imbalan...
Neurons Humans Memory Neural Networks, Computer Machine Learning
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Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data.

Nature communications Jul 1, 2025
During cell-cell communication (CCC), pathways activated by different ligand-receptor pairs may have crosstalk with each other. While multiple methods have been developed to infer CCC networks and their downstream response using single-cell RNA-seq d...
Cell Communication Signal Transduction Transcription Factors Machine Learning Humans Single-Cell Analysis Gene Expression Profiling RNA-Seq Transcriptome Gene Regulatory Networks
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A supervised machine learning approach with feature selection for sex-specific biomarker prediction.

NPJ systems biology and applications Jul 1, 2025
Biomarkers are crucial in aiding in disease diagnosis, prognosis, and treatment selection. Machine learning (ML) has emerged as an effective tool for identifying novel biomarkers and enhancing predictive modelling. However, sex-based bias in ML algor...
Supervised Machine Learning Sex Factors Waist Circumference Body Mass Index Blood Pressure Uric Acid Algorithms Biomarkers Female Male Middle Aged Blood Glucose Humans Machine Learning
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ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment.

Nature communications Jul 1, 2025
Multi-species acute toxicity assessment forms the basis for chemical classification, labelling and risk management. Existing deep learning methods struggle with diverse experimental conditions, imbalanced data, and scarce target data, hindering their...
Deep Learning Toxicity Tests, Acute Humans Machine Learning Animals
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Data-driven protease engineering by DNA-recording and epistasis-aware machine learning.

Nature communications Jul 1, 2025
Protein engineering has recently seen tremendous transformation due to machine learning (ML) tools that predict structure from sequence at unprecedented precision. Predicting catalytic activity, however, remains challenging, restricting our capabilit...
Machine Learning Protein Engineering Peptide Hydrolases Epistasis, Genetic Escherichia coli DNA Substrate Specificity
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Charting γ-secretase substrates by explainable AI.

Nature communications Jul 1, 2025
Proteases recognize substrates by decoding sequence information-an essential cellular process elusive when recognition motifs are absent. Here, we unravel this problem for γ-secretase, an intramembrane-cleaving protease associated with Alzheimer's di...
Substrate Specificity Humans Machine Learning Algorithms Protein Binding Alzheimer Disease Amyloid Precursor Protein Secretases
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Enhanced role of the entorhinal cortex in adapting to increased working memory load.

Nature communications Jul 1, 2025
In daily life, we frequently encounter varying demands on working memory (WM), yet how the brain adapts to high WM load remains unclear. To address this question, we recorded intracranial EEG from hippocampus, entorhinal cortex (EC), and lateral temp...
Female Young Adult Adult Humans Machine Learning Hippocampus Entorhinal Cortex Male Temporal Lobe Electroencephalography Memory, Short-Term
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