three‐dimensional dataGenomic interactions reveal the spatial organization of genomes and genomic domains, which is known to play key roles in cell function. Physical proximity can be represented as two‐dimen
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The complexity of Big Data analysis presents an undeniable challenge: visualization techniques and methods need to be improved. Many companies and open-source projects see the future of Big Data Analytics via Visualization, and are establishing new interactive platforms and supporting research in this a...
Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. https://doi.org/10.1038/nbt.4314 (2018). Article PubMed Google Scholar Weinreb, C., Wolock, S. & Klein, A. M. SPRING: a kinetic interface for visualizing high dimensional single-cell expression data. ...
2019). It shows the environment from the autonomous system’s perspective, visualizing global and local paths and detecting obstacles. Autonomous vehicles rely on a variety of sensors, including cameras, LiDAR, and radar, to perceive their environment. Each sensor type has its strengths; for ...
The problem of imbalanced data is one of the most relevant issues in environment-related research with a focus on spatial capturing of target events or features. Imbalance occurs when the number of samples belonging to one class or class (majority class[es]) significantly surpasses the number of...
b Microbial co-occurrence and co-exclusion networks help visualizing microbial interactions. In such networks, nodes usually represent taxa of microorganisms, and edges represent statistically significant associations between nodes. Green edges usually stay for positive interactions, while red edges visualize...
Visualizing spatiotemporal dynamics of multicellular cell-cycle progression Cell, 132 (3) (2008), pp. 487-498, 10.1016/j.cell.2007.12.033 View PDFView articleView in ScopusGoogle Scholar Samdurkar et al., 2017a Samdurkar S.A., Kamble S., Thakur N., Patharkar S.A. Overview of object...
Joo et al. suggested using Grad-CAM one of the XAI approaches for visualizing the actions of AI players who had undergone DRL training. Their experimental findings demonstrate which areas of the input state are targeted when a trained agent takes action....
By embedding and visualizing these simulated data over time using Principal Component Analysis (PCA) and BGA we observed that the three main trajectories are preserved under familiar conditions (Supplementary Fig. 1). We also analyzed the differential activation of the pathways due to the intrinsic ...