Latest AI and machine learning research in practice management for healthcare professionals.
Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields ...
Transposable elements (TEs) play an essential role in the genetic variability of eukaryotic species. In plants, they may comprise up to 90% of the total genome. Non-coding RNAs (ncRNAs) are known to control gene expression and regulation. Although the relationship between ncRNAs and TEs is known, obtaining the organized data for sequenced genomes is not straightforward. In this study, we describe ...
Efficient extraction of knowledge from biological data requires the development of structured vocabularies to unambiguously define biological terms. T...
BACKGROUND: Critical limb ischemia (CLI) is a complication of advanced peripheral artery disease (PAD) with diagnosis based on the presence of clinica...
Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of l...
The nature of mental illness remains a conundrum. Traditional disease categories are increasingly suspected to misrepresent the causes underlying ment...
BACKGROUND: RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Mea...
Rising admissions from emergency departments (EDs) to hospitals are a primary concern for many healthcare systems. The issue of how to differentiate u...
When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the vi...
This letter proposes a novel predictive coding type neural network model, the predictive multiple spatiotemporal scales recurrent neural network (P-MS...
BACKGROUND: In recent years, a rapidly increasing number of RNA transcripts has been generated by thousands of sequencing projects around the world, c...
Electrocardiogram (ECG) data analysis is of great significance to the diagnosis of cardiovascular disease. ECG compression should be processed in real...
Assessing attachment in adolescents is important due to relations between insecurity and psychopathology. The child attachment interview (CAI) holds p...
This work focuses on data mining applied to the clinical documentation domain. Diagnostic terms (DTs) are used as keywords to retrieve valuable inform...
It is well known that dense coding with local bases (via Least Square coding schemes) can lead to large quantization errors or poor performances of ma...
The molecular evolution of genomic DNA across diverse plant and animal phyla involved dynamic registrations of sequence modifications to maintain exis...
BACKGROUND: Information about renal function is important for drug safety studies using administrative health databases. However, serum creatinine val...
OBJECTIVE: This paper builds upon work submitted as part of the 2016 PhysioNet/CinC Challenge, which used sparse coding as a feature extraction tool o...
A long-standing and influential hypothesis in neural information processing is that early sensory networks adapt themselves to produce efficient codes...
Disease and trait-associated variants represent a tiny minority of all known genetic variation, and therefore there is necessarily an imbalance betwee...