AIMC Topic: Humans

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Sceptic: pseudotime analysis for time-series single-cell sequencing and imaging data.

Genome biology
Several computational methods have been developed to construct single-cell pseudotime embeddings for extracting the temporal order of transcriptional cell states from time-series scRNA-seq datasets. However, existing methods suffer from low predictiv...

Microbiome-based prediction of allogeneic hematopoietic stem cell transplantation outcome.

Genome medicine
BACKGROUND: Allogeneic hematopoietic stem cell transplantation (HSCT) is potentially curative for hematologic malignancies but is frequently complicated by relapse and immune-mediated complications, such as graft-versus-host disease (GVHD). Emerging ...

Electrocardiographic sex index: a continuous representation of sex.

Biology of sex differences
Clinical risk calculators consider sex as a binary variable. However, sex is a complex trait with anatomic, physiologic, and metabolic attributes that are not easily summarized in this manner [1]. We propose a continuous representation of sex, the EC...

Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data.

BMC medical research methodology
BACKGROUND: Individualized prediction of health outcomes supports clinical medicine and decision making. Our primary objective was to offer a comprehensive survey of methods for the dynamic prediction of Alzheimer's disease (AD), encompassing both co...

The application of super-resolution ultrasound radiomics models in predicting the failure of conservative treatment for ectopic pregnancy.

Reproductive biology and endocrinology : RB&E
BACKGROUND: Conservative treatment remains a viable option for selected patients with ectopic pregnancy (EP), but failure may lead to rupture and serious complications. Currently, serum β-hCG is the main predictor for treatment outcomes, yet its accu...

Ensemble learning for microbiome-based caries diagnosis: multi-group modeling and biological interpretation from salivary and plaque metagenomic data.

BMC oral health
BACKGROUND: Oral microbiota is a major etiological factor in the development of dental caries. Next-generation sequencing techniques have been widely used, generating vast amounts of data which is underexplored. The advancement of artificial intellig...

Automated web-based typing of Clostridioides difficile ribotypes via MALDI-TOF MS.

BMC bioinformatics
BACKGROUND: Clostridioides difficile is a major cause of hospital-acquired diarrhea and a driver of nosocomial outbreaks, yet rapid, accurate ribotype identification remains challenging. We sought to develop a MALDI-TOF MS-based workflow coupled with...

Impact of regional digital transformation on public health: an empirical analysis based on 31 provinces in China.

BMC public health
BACKGROUND: With the rapid development of digital transformation driven by big data and artificial intelligence in China, the field of public health is undergoing profound transformations. This study introduces technological innovation as a mediating...

Development of a machine learning-derived model to predict unplanned ICU admissions after major non-cardiac surgery.

BMC anesthesiology
BACKGROUND: Unplanned postoperative intensive care unit admissions (UIAs) are rare events that cause significant challenges to perioperative workflow. We describe the development of a machine-learning derived model to predict UIAs using only widely u...

Explainable machine learning-driven models for predicting Parkinson's disease and its prognosis: obesity patterns associations and models development using NHANES 1999-2018 data.

Lipids in health and disease
BACKGROUND: Parkinson's disease (PD) is a prevalent neurodegenerative condition, the effect of obesity on PD remains controversial. We aimed to investigate the associations of obesity patterns on PD and all-cause mortality, while developing machine l...