Jacob BienJournal of the American Statistical AssociationXiaohan Yan and Jacob Bien. Rare feature selection in high dimensions. arXiv preprint arXiv:1803.06675, 2018.X. Yan and J. Bien. Rare feature selection in high dimen- sions. arXiv preprint arXiv:1803.06675, 2018....
of gene/peak expression/accessibility in the high selection region. Such rare-related genes and peaks have a higher probability of being sampled to the key features of rare cells in our multi-head attention graph transformer. The multi-head attention mechanism facilitates the update of joint embedd...
Testing was performed in the discovery cohort (TCGA) across 6799 individuals of European ancestry and 12 different cancer types as well as in a pan-cancer analysis for all 15 models. Genes were only tested via the dominant or additive model when at least 2 individuals carried a rare pLoF va...
On the one hand, 256 nodes means that the pre-trained CLIP output feature dimension is 256 dimensions. An appropriate dimension size implies a trade-off between computational resources and performance. The experimental results show that 256 is an appropriate feature dimension size in the lightweight...
Gdip_GetHICONDimensions() get icon dimensions FoxitInvoke() wm_command wrapper for FoxitReader Version: 9.1 WinSaveCheckboxes() save the status of checkboxes in other apps GetButtonType() uses the style of a button to get it’s name KeyValueObjectFromLists() merge two lists into one key-valu...
23,24 Although these advancements have been less prominent in RDs, AIMDs have demonstrated notable results in several dimensions of common diseases (CDs) diagnosis and prognosis (eg, breast cancers, brain tumors, fractures).25, 26, 27 One reason for such lack of progress in RDs is the ...
More in details, Whittaker dissimilarity values were graphically represented using heatmaps, whereas Bray–Curtis dissimilarity values were used to build distance matrices on which Non-metric Multidimensional Scaling (NMDS) dimensionality reduction was performed and the first two dimensions were plotted. ...
For example, after being processed, the unstructured time-stamped data may be aggregated by time (e.g., into daily time period units) to generate time series data or structured hierarchically according to one or more dimensions (e.g., parameters, attributes, or variables). For example, data...
Typically a large number of cell population features (thousands9 or millions10) are required to detect rare cell subsets from high-dimensional measurements (i.e., 20+ dimensions). Most such features are not relevant, leading to overfitting or even precluding the identification of disease-associated...
FiRE makes multiple estimations of the proximity between a pair of cells, in low-dimensional spaces, as determined by the parameterM. The notion of similarity for LOF21, on the other hand, is confounded by the arbitrary scales of the input dimensions. As a result, even though LOF consistentl...