After deriving parameters for each of the GLIF models, we assessed how well these parameters could classify neurons into putative types corresponding to transgenic lines. We used two different clustering algorithms to identify trends that were not method-specific. The first of these is an iterative ...
Hierarchical clustering methods are relevant to developing classifiers of motor activities from data recorded using wearable systems. They allow users to assess feasibility of a classification problem and choose architectures that maximize accuracy. By relying on this approach, the clinical importance of di...
The currently described methods and systems then employ clustering methods to identify outlying transformed-metric-data observations, accordingly label the transformed metric-data observations to generate a training dataset, and then apply one or more of various types of machine-learning techniques to the...
Cell classification methods typically involve manual gating or clustering of the expression matrix using algorithms that were developed for isolated cells, such as cytometry or single cell RNA sequencing (scRNAseq)8,13,19,24,25,26,27,28,29,30. However, deriving cell classifications from multiplexed...
It is also possible to combine the two aforementioned clustering methods, an approach known as two-stage clustering: the first stage makes use of hierarchical clustering, while the second stage makes use of the k-means algorithm. As a matter of fact, the use of the dendrogram obtained in the...
Various molecular typing methods that can differenti- ate across the 8 major TB lineages, have been used to gain clues as to whether a particular infection contains more than 1 M. tuberculosis strain. Restriction Fragment Length Polymorphism (RFLP) analysis relies on the posi- tioning and copy...
[2]. Multi-omic assays like CITE-Seq introduce new dimensionality to the data, but often require nuanced analyses to extract meaningful results. For example, a common single cell analysis consists of unsupervised clustering of cells followed by the classification of cell populations. However, there...
Plants are finally classified using the hierarchical clustering method, and the resulting classification is evaluated by comparing it with the NCBI taxonomy [26]. Our classification results reveal both the phylogeny- and bioactivity-based relations among plants. We also use a support vector machine (...
Specifically, by effectively extracting and utilizing this edge information, the model can better distinguish between various types of ground objects, thereby reducing confusion and the likelihood of misclassification. In future research, we can explore integrating an image spectral clustering module into ...
In addition, we plan to apply these clustering methods to additional types of SVs and develop more sophisti- cated classification methods that would place new candi- date SVs in one of these categories of different types of true or false positive SVs. We plan for the methods developed in ...