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BioData Mining
Scopus
ISSN 1756-0381 · BMC · 528 artikel di indeks
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Artikel Jurnal
Disease- and gene-specific deep learning for pathogenicity prediction of rare missense variants in cancer predisposition genes
Abstract Background Hereditary cancers frequently arise from germline pathogenic variants, yet only a small proportion of reported variants have been clinically classified, leaving most missense variants unresolved as variants of uncertain significance (VUS). Although recent machine-learning…
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Artikel Jurnal
Quantum Angle–distance kernel for ECG classification and anomaly detection: a quantum-inspired framework for biomedical signal analysis
Abstract Reliable, clear and lightweight ECG classification is important for immediate medical and wearable applications, yet challenges due to confounding signals, rare, labeled examples as well as high model complexity. Our method introduces a Quantum Angle–Distance Kernel (QADK), inspired by…
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Artikel Jurnal
Explainable AI-driven graph-based neural networks for mucopolysaccharidoses diagnosis
Abstract Background Mucopolysaccharidoses (MPS) are a group of rare lysosomal storage disorders caused by enzyme deficiencies leading to the accumulation of glycosaminoglycans (GAGs). Their clinical heterogeneity and low prevalence contribute to delayed and often missed diagnoses. Early detection…
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Artikel Jurnal
Network analysis of longitudinal electronic health records using linear mixed models
Abstract Background The accelerating development of healthcare data stored in electronic health records (EHRs) has created novel opportunities for biomedical research. Clinical data contains rich, heterogeneous, and longitudinal information of diverse cohorts of population. These data can be…
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Genotype subtyping approach to identify unnoticed variants in diseases from GWAS data
Abstract Genome-wide association studies (GWAS) are vital for investigating single-nucleotide polymorphisms (SNPs) in diseases. Comparisons of detected SNPs for the same disease between populations often reveal gaps, which are attributed to factors such as population, sample size, and rare…
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Artikel Jurnal
Deep learning based prediction of RNA 5hmC modifications using composite feature representations and comparative benchmarking with transformer models
Abstract RNA 5-hydroxymethylcytosine (5hmC) post-transcriptional modification plays an important role in the regulation of gene expression, its stability and evolution. These kinds of modifications emphasizes to understand their further biological impacts. Although several sequencing technologies…
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Artikel Jurnal
A deep ensemble encoder network method for improved polygenic risk score prediction
Abstract Genome-wide association studies of various heritable human traits and diseases have identified numerous associated single-nucleotide polymorphisms (SNPs), most of which have small or modest effects. Polygenic risk scores aim to better estimate individuals’ genetic predisposition by…
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Construction of an interpretable machine learning model for predicting gestational diabetes mellitus based on 45 dietary nutrients
Abstract Objective This study aimed to develop and validate a machine learning (ML)-based model for predicting the risk of gestational diabetes mellitus (GDM) using 45 dietary nutrients and baseline data. Methods A retrospective analysis was conducted on 3,649 pregnant women from the NHANES…
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Artikel Jurnal
An online non-radiographic osteoporosis prediction calculator constructed using interpretable machine learning
Abstract Background Osteoporosis represents a major health challenge in aging populations, yet its diagnosis largely depends on dual-energy X-ray absorptiometry (DXA), which is both costly and radiation-based. This study aimed to develop a practical, non-radiographic prediction model for…
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Artikel Jurnal
A crisis of overconfidence: Why confidence, not accuracy, is the real risk in clinical AI
Abstract Language models today are trained to convey confidence in their outputs, regardless of whether those outputs are correct. The alignment methods we use to make them helpful also push them toward unwarranted certainty, rewarding decisive answers over appropriate hedging. As these foundation…
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Artikel Jurnal
Early differentiation between paroxysmal and persistent atrial fibrillation based on interpretable machine learning: a multicenter retrospective study
Abstract Aims Atrial fibrillation (AF) is a common arrhythmia associated with increased risks of stroke and heart failure. Early differentiation between paroxysmal and persistent AF at first diagnosis is critical for guiding treatment decisions. This study aimed to develop an interpretable machine…
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Artikel Jurnal
Automatic clear cell renal cell carcinoma grading framework using histopathological images via artificial intelligence: a benchmarking study
Abstract Background Renal cell carcinoma (RCC) is a common malignant tumor of the urinary system. Postoperative patients face the risk of recurrence or metastasis, and those who experience disease progression often have a poor prognosis. Therefore, investigating prognostic factors for RCC is…
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Artikel Jurnal
KeySDL: sparse dictionary learning for keystone microbe identification from steady-state observations using a dynamical-systems model
Abstract Background Identification of microbes with large impacts on their microbial communities, known as keystone microbes, is a topic of long-standing interest in microbiome research. However, many approaches to identify keystone microbes are limited by the inherent nonlinearity and…
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Artikel Jurnal
Interpretable machine learning for Parkinson’s disease diagnosis, staging, and biological mechanism exploration: a multicenter analysis
Abstract Objective To develop and validate an interpretable machine learning model based on multicenter T1-weighted MRI radiomics data for the three-way classification of Parkinson’s disease (PD)—Normal (NM), early-stage, and mid-late-stage—and to clarify the diagnostic value of key cortical…
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Artikel Jurnal
TENTACLES: a consensus machine learning tool for robust biomarker discovery in heterogeneous data
Abstract Background Transcriptomic biomarker discovery often fails to produce reproducible gene signatures across independent cohorts due to model-specific biases and dataset heterogeneity. While single-algorithm approaches may perform well on training data, they frequently fail to generalize…
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Artikel Jurnal
Automated candidate confounder scoping for adjustment in clinical research: a retrieval-augmented generation approach
Abstract Background Identifying confounding variables is fundamental for robust observational studies, yet the traditional manual process is a time-consuming and subjective barrier for researchers. Recent advances in Retrieval-Augmented Generation (RAG) offer a promising solution, but most existing…
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TLEUDS: a cascade Dual-Transfer learning system with quality- and knowledge-enhanced for precise fetal CHD screening
Abstract Congenital heart disease (CHD) is the most common major congenital anomaly and a leading cause of perinatal mortality and long-term neurodevelopmental disability globally, among which severe ductal dependent CHD (D-CHD) requires urgent clinical intervention due to the high risk of…
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Artikel Jurnal
Early prediction of longitudinal treatment adherence in obstructive sleep apnea using machine learning approaches
Abstract Background Continuous Positive Airway Pressure (CPAP) is the most prescribed treatment for Obstructive Sleep Apnea (OSA), but adherence remains a critical challenge, especially long term. This study aims to predict CPAP adherence at 3, 6, and 12 months based on baseline patient status and…
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Artikel Jurnal
A peak-oriented diffusion model for high-fidelity electrocardiogram reconstruction from photoplethysmogram: development and usability study
Abstract Background Cardiovascular disease is a leading cause of death, accounting for nearly one-third of all fatalities worldwide. The electrocardiogram (ECG), an electrophysiological signal representing cardiac activity, is a widely used non-invasive method for diagnosing cardiovascular…
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Artikel Jurnal
CRM-TI: an enhanced pipeline for computationally assigning the target genes of cis-regulatory modules by considering comprehensive long-range regulation mechanisms
Abstract Background Metazoan transcriptional regulation is mediated by modular DNA segments termed cis-regulatory modules (CRMs). The precise identification of CRM target genes is essential for understanding cellular functions and disease mechanisms. While reporter assays can validate CRM targets…