RESUMEN
The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.
Asunto(s)
Aprendizaje Profundo , Microbioma Gastrointestinal , Microbiota , Humanos , Metagenoma , Metagenómica/métodosRESUMEN
The aim of this study was to assess a technique for serum ferritin determination by means of chemoluminiscence (Magic Lite, Ciba Corning) in a series of unselected patients. A group of 100 healthy blood donors (50 men and 50 women), in whom iron deficiency had been previously excluded, was used as control. The results were validate according to the Societé Francaise de Biologie Clinique guidances. The characteristics of the method and its comparison with IRMA and ELISA techniques are described here. Its major advantages are related with simplicity and sensitivity; the main disadvantage is the necessity to repeat every step in samples with ferritin levels over 1,500 ng/dl.