This paper introduces an enhancement to DeSTIN – a compositional deep learning architecture in which each layer consists of multiple instantiations of a common node – that learns to represent spatiotemporal patterns in data based on a novel recurrent clustering algorithm.
The premenstrual dysphoric disorder (PMDD) is characterized by a recurrent cluster of physical and negative mood symptoms during the luteal phase of the menstrual cycle.
However, for α = 0 we have to use a high hybrid similarity threshold to obtain recurrent edge clusters similar to the ones observed for α = 0.5.
To achieve such behavior through mutations, these must occur at specific places in the sequence, which results in fewer numbers of recurrent sites (clusters) than in CGC Rec genes (Supplementary Fig. S1).
In 2007, novel recurrent mutations clustered in a highly conserved region in exon 12 of the JAK2 gene have been described in patients with PV or Idiopathic erythrocytosis [7].
These can be classified as recurrent (with clustered breakpoints, usually within low copy repeats), non-recurrent with breakpoint grouping, and non-recurrent with little overlap.
The recurrent co-clustering of dinoflagellates, hapthophytes and diatoms in 4CL, CCR and CAD phylogenies is more parsimoniously explained by a common origin.
UTI susceptibility runs in families and UTI-prone individuals experience recurrent, often clustered infections with potentially severe complications, depending on the site of infection and type of UTI.
A subset analysis of 15 groups of recurrent STM MLVA clusters (consisting of 1-2 reachrevealedach) revealedifferenceses in size, duration and spatial density between initial clusters (15 clusters) and corresponding reoccurred clusters (24 clusters) due to the same MLVA type (Table 2).
Clustering the weighted hybrid similarity graph yields recurrent coexpression link clusters (modules).
