AIMC Topic: Humans

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DHerbKB for CKD: knowledge base of diet and toxic herbal medicines for clinical support of chronic kidney disease.

Journal of translational medicine
BACKGROUND: Dietary management and nephrotoxic herbal medicine control are essential and sophisticated in chronic kidney disease(CKD) care. Information gap between clinical principles and real-world practice hinders the relevant management. It is nec...

A novel modality contribution confidence-enhanced multimodal deep learning framework for multiomics data.

BMC bioinformatics
Multimodal learning for classification tasks has recently gained significant attention in bioinformatics. Current approaches primarily concentrate on devising efficient deep learning architectures to capture features within and across modalities. How...

Development and prospective evaluation of a machine learning model to predict vomiting among pediatric cancer and hematopoietic cell transplant patients.

BMC cancer
PURPOSE: Objectives were to develop a machine learning (ML) model based on electronic health record (EHR) data to predict the risk of vomiting within a 96-hour window after admission to the pediatric oncology and hematopoietic cell transplant (HCT) s...

Characteristics of brain glucose metabolism in Parkinson's disease patients with freezing of gait: a study based on F-FDG PET imaging and deep learning.

BMC neurology
OBJECTIVE: Freezing of gait (FOG) is a common gait disorder in the advanced stages of Parkinson's disease (PD), closely associated with impaired balance and executive function. This study aimed to investigate specific changes in brain glucose metabol...

Application of interpretable machine learning to predict activities of daily living disability in sarcopenia: insights from the CHARLS dataset.

BMC geriatrics
PURPOSE: The decline in activities of daily living (ADL) among older persons is a significant public health concern. Sarcopenia is a major risk factor for ADL disability. This study aimed to develop and validate an interpretable machine learning (IML...

ESAE-SDA: ensemble sparse autoencoder framework for epigenomics-informed snoRNA-disease associations prediction.

BMC bioinformatics
Small nucleolar RNAs (snoRNAs), a class of non-coding RNAs broadly distributed in eukaryotes, are emerging as pivotal regulators in the field of epigenomics. In addition to guiding 2'-O-methylation and pseudouridylation modifications at specific rRNA...

Machine learning to predict the role of CHWs in shifting birth preferences away from homebirth in India.

Scientific reports
This study utilized well-known supervised machine learning algorithms to NFHS‑5 data of West Bengal, India, to predict the place of birth (home vs facility) by integrating CHW (community health worker) contact factors and women participant's percepti...

Hierarchical multi-scale vision transformer model for accurate detection and classification of brain tumors in MRI-based medical imaging.

Scientific reports
Automated brain tumor detection represents a fundamental challenge in contemporary medical imaging, demanding both precision and computational feasibility for practical implementation. This research introduces a novel Vision Transformer (ViT) framewo...

Deep learning for motion classification in ankle exoskeletons using surface EMG and IMU signals.

Scientific reports
Ankle exoskeletons have garnered considerable interest for their potential to enhance mobility, support rehabilitation, and reduce fall risks, particularly among the aging population. Their effectiveness depends on accurate, real-time prediction of u...

DeepEGFR a graph neural network for bioactivity classification of EGFR inhibitors.

Scientific reports
Epidermal Growth Factor Receptor (EGFR) plays a critical role in the development of several cancers. Thus, modulation/inhibition of EGFR activity is an appealing target of developing novel cancer therapeutics. With the advent of modern machine learni...