Unsupervised learning methods for molecular simulation data
Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …
amounts of data produced by atomistic and molecular simulations, in material science, solid …
Towards the next generation of machine learning models in additive manufacturing: A review of process dependent material evolution
Additive manufacturing facilitates producing of complex parts due to its design freedom in a
wide range of applications. Despite considerable advancements in additive manufacturing …
wide range of applications. Despite considerable advancements in additive manufacturing …
[HTML][HTML] Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST
Spatial transcriptomics technologies generate gene expression profiles with spatial context,
requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample …
requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample …
The widespread IS200/IS605 transposon family encodes diverse programmable RNA-guided endonucleases
IscB proteins are putative nucleases encoded in a distinct family of IS200/IS605 transposons
and are likely ancestors of the RNA-guided endonuclease Cas9, but the functions of IscB …
and are likely ancestors of the RNA-guided endonuclease Cas9, but the functions of IscB …
SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network
Recent advances in spatially resolved transcriptomics (SRT) technologies have enabled
comprehensive characterization of gene expression patterns in the context of tissue …
comprehensive characterization of gene expression patterns in the context of tissue …
Cluster quality analysis using silhouette score
KR Shahapure, C Nicholas - 2020 IEEE 7th international …, 2020 - ieeexplore.ieee.org
Clustering is an important phase in data mining. Selecting the number of clusters in a
clustering algorithm, eg choosing the best value of k in the various k-means algorithms [1] …
clustering algorithm, eg choosing the best value of k in the various k-means algorithms [1] …
Learning invariant graph representations for out-of-distribution generalization
Graph representation learning has shown effectiveness when testing and training graph
data come from the same distribution, but most existing approaches fail to generalize under …
data come from the same distribution, but most existing approaches fail to generalize under …
A cellular hierarchy in melanoma uncouples growth and metastasis
Although melanoma is notorious for its high degree of heterogeneity and plasticity,, the
origin and magnitude of cell-state diversity remains poorly understood. Equally, it is unclear …
origin and magnitude of cell-state diversity remains poorly understood. Equally, it is unclear …
[HTML][HTML] A novel k-means clustering algorithm with a noise algorithm for capturing urban hotspots
With the development of cities, urban congestion is nearly an unavoidable problem for
almost every large-scale city. Road planning is an effective means to alleviate urban …
almost every large-scale city. Road planning is an effective means to alleviate urban …
[HTML][HTML] Promotion of cholangiocarcinoma growth by diverse cancer-associated fibroblast subpopulations
Cancer-associated fibroblasts (CAF) are a poorly characterized cell population in the context
of liver cancer. Our study investigates CAF functions in intrahepatic cholangiocarcinoma …
of liver cancer. Our study investigates CAF functions in intrahepatic cholangiocarcinoma …