With the biomedical field generating large quantities of time series data, there has been a growing interest in developing and refining machine learning methods that allow its mining …
Healthcare professionals have long envisioned using the enormous processing powers of computers to discover new facts and medical knowledge locked inside electronic health …
Medical time series of laboratory tests has been collected in electronic health records (EHRs) in many countries. Machine-learning algorithms have been proposed to analyze the …
M Richter-Laskowska, P Trybek… - PLOS Computational …, 2022 - journals.plos.org
The large conductance voltage-and Ca2+-activated K+ channels from the inner mitochondrial membrane (mitoBK) are modulated by a number of factors. Among them …
Kernel methods are a powerful approach for learning on structured data. However, as we show in this paper, simple but common instances of the popular R-convolution kernel …
C Oliver, D Chen, V Mallet, P Philippopoulos… - arXiv preprint arXiv …, 2022 - arxiv.org
Frequent and structurally related subgraphs, also known as network motifs, are valuable features of many graph datasets. However, the high computational complexity of identifying …
Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to …
Motivation Correlating genetic loci with a disease phenotype is a common approach to improve our understanding of the genetics underlying complex diseases. Standard analyses …
Motivation Temporal biomarker discovery in longitudinal data is based on detecting reoccurring trajectories, the so-called shapelets. The search for shapelets requires …