AIMC Topic: Machine Learning

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Leveraging multithreading on edge computing for smart healthcare based on intelligent multimodal classification approach.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jul 1, 2025
Medical digitization has been intensively developed in the last decade, leading to paving the path for computer-aided medical diagnosis research. Thus, anomaly detection based on machine and deep learning techniques has been extensively employed in h...
Humans Diagnosis, Computer-Assisted Machine Learning Deep Learning Algorithms Decision Support Systems, Clinical
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AI-Accelerated Identification of Novel Antimicrobial Peptides for Inhibiting .

Journal of agricultural and food chemistry Jul 1, 2025
Fusarium head blight caused by threatens global wheat production, causing substantial yield reduction and mycotoxin accumulation. This study harnessed machine learning to accelerate the discovery of antifungal peptides targeting this phytopathogen. ...
Machine Learning Triticum Fungicides, Industrial Molecular Dynamics Simulation Fusarium Plant Diseases Antimicrobial Peptides
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A Machine Learning Approach to Molecular Initiating Event Prediction Using High-Throughput Transcriptomic Chemical Screening Data.

Journal of chemical information and modeling Jul 1, 2025
Improved scalability of high-throughput RNA-sequencing technologies has contributed to their proposed use in regulatory contexts for chemical hazard identification. However, the high dimensionality and size of these transcriptomic data sets present a...
Computational Biology MCF-7 Cells High-Throughput Screening Assays Machine Learning Gene Expression Profiling Transcriptome Humans
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Photoacoustic-Integrated Multimodal Approach for Colorectal Cancer Diagnosis.

ACS biomaterials science & engineering Jul 1, 2025
Colorectal cancer remains a major global health challenge, emphasizing the need for advanced diagnostic tools that enable early and accurate detection. Photoacoustic (PA) spectroscopy, a hybrid technique combining optical absorption with acoustic res...
Multimodal Imaging Photoacoustic Techniques Colorectal Neoplasms Nanoparticles Humans Machine Learning
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MoveMentor-examining the effectiveness of a machine learning and app-based digital assistant to increase physical activity in adults: protocol for a randomised controlled trial.

Trials Jul 1, 2025
BACKGROUND: Physical inactivity is prevalent, leading to a high burden of disease and large healthcare costs. Thus, there is a need for affordable, effective and scalable interventions. However, interventions that are affordable and scalable are bese...
Mobile Applications Humans Health Promotion Adult Machine Learning Randomized Controlled Trials as Topic Exercise
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Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methods.

Health and quality of life outcomes Jul 1, 2025
BACKGROUND: Preference-based measures of health-related quality of life (HRQoL), such as the Short Form Six-Dimension (SF-6D) is essential for health economic evaluations. However, these measures are rarely included in clinical trials for lung cancer...
China Surveys and Questionnaires Lung Neoplasms Regression Analysis Psychometrics Algorithms Female Aged Humans Machine Learning Middle Aged Adult Quality of Life Male
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Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES.

BMC medical informatics and decision making Jul 1, 2025
OBJECTIVE: Using 2005-2018 NHANES data, this study examined the association between the visceral fat metabolism score (METS-VF) and heart failure (HF) prevalence in U.S. adults, leveraging machine learning (LightGBM/XGBoost) and SHAP for classficatio...
Intra-Abdominal Fat Boosting Machine Learning Algorithms Male Middle Aged United States Heart Failure Nutrition Surveys Female Aged Adult Humans Machine Learning
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Expert-augmented machine learning for predicting extubation readiness in the pediatric intensive care unit.

BMC medical informatics and decision making Jul 1, 2025
BACKGROUND: Determining extubation readiness in pediatric intensive care units (PICU) is challenging. We used expert-augmented machine learning (EAML), a method that combines machine learning with human expert knowledge, to predict successful extubat...
Airway Extubation Child Intensive Care Units, Pediatric Electronic Health Records Female Infant Child, Preschool Male Humans Machine Learning
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GraphGIM: rethinking molecular graph contrastive learning via geometry image modeling.

BMC biology Jul 1, 2025
BACKGROUND: Learning molecular representations is crucial for accurate drug discovery. Using graphs to represent molecules is a popular solution, and many researchers have used contrastive learning to improve the generalization of molecular graph rep...
Machine Learning Drug Discovery
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Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis.

BMC endocrine disorders Jul 1, 2025
BACKGROUND: Predicting pituitary adenoma (PA) recurrence after surgical resection is critical for guiding clinical decision-making, and machine learning (ML) based models show great promise in improving the accuracy of these predictions. These models...
Prognosis Pituitary Neoplasms Adenoma Humans Machine Learning Neoplasm Recurrence, Local
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