Simulation of a structured illumination microscope imaging process. حسب Jorge Sola-Pikabea (7256042)

Table_1_Illustrating the biological functions and diagnostic value of transmembrane protein family members in glioma.xlsx حسب Ying Zhang (40767)

Estimation of the spatial map of epileptogenicity across different brain regions in the BVEP model. حسب Meysam Hashemi (4114738)

Comparing steady states in BM3. حسب Bastiaan J. R. Cockx (18078759)

Antibody challenge outcomes. حسب Andrea Blasco (7439102)

"…Also shown is the benchmark algorithm implemented in Python (A1) and C++ (A2); note that benchmark algorithms A1 and A2 have perfect accuracy (<i>ACC</i> equal to unity). …"

Integration of datasets. حسب Meili Liu (327309)

Artifact and instructions to generate experimental results for Euro-Par 2019 paper "A Comparison of Random Task Graph Generation Methods for Scheduling Problems" حسب Louis-Claude Canon (569406)

Bespoke software controls the sorter GUI and reports on instrument status to the user. حسب Diane M. Wiener (1956583)

Resting state functional MRI brain signatures of fast disease progression in amyotrophic lateral sclerosis: a retrospective study حسب Francesca Trojsi (707124)

"…<p>Advanced neuroimaging techniques may offer the potential to monitor disease spreading in amyotrophic lateral sclerosis (ALS). We aim to investigate brain functional and structural magnetic resonance imaging (MRI) changes in a cohort of ALS patients, examined at diagnosis and clinically monitored over 18 months, in order to early discriminate fast progressors (FPs) from slow progressors (SPs). …"

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) حسب Erola Fenollosa (20977421)

"…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…"

Image_2_Interpretable, Scalable, and Transferrable Functional Projection of Large-Scale Transcriptome Data Using Constrained Matrix Decomposition.JPEG حسب Nicholas Panchy (6456764)

"…Finally, we demonstrate this approach is broadly applicable to data and gene sets beyond EMT and provide several recommendations on the choice between the two linear methods and the optimal algorithmic parameters. Our methods show that simple constrained matrix decomposition can produce to low-dimensional information in functionally interpretable and transferrable space, and can be widely useful for analyzing large-scale transcriptome data.…"

Image_6_Interpretable, Scalable, and Transferrable Functional Projection of Large-Scale Transcriptome Data Using Constrained Matrix Decomposition.JPEG حسب Nicholas Panchy (6456764)

"…Finally, we demonstrate this approach is broadly applicable to data and gene sets beyond EMT and provide several recommendations on the choice between the two linear methods and the optimal algorithmic parameters. Our methods show that simple constrained matrix decomposition can produce to low-dimensional information in functionally interpretable and transferrable space, and can be widely useful for analyzing large-scale transcriptome data.…"

Image_4_Interpretable, Scalable, and Transferrable Functional Projection of Large-Scale Transcriptome Data Using Constrained Matrix Decomposition.JPEG حسب Nicholas Panchy (6456764)

"…Finally, we demonstrate this approach is broadly applicable to data and gene sets beyond EMT and provide several recommendations on the choice between the two linear methods and the optimal algorithmic parameters. Our methods show that simple constrained matrix decomposition can produce to low-dimensional information in functionally interpretable and transferrable space, and can be widely useful for analyzing large-scale transcriptome data.…"

Image_5_Interpretable, Scalable, and Transferrable Functional Projection of Large-Scale Transcriptome Data Using Constrained Matrix Decomposition.JPEG حسب Nicholas Panchy (6456764)

"…Finally, we demonstrate this approach is broadly applicable to data and gene sets beyond EMT and provide several recommendations on the choice between the two linear methods and the optimal algorithmic parameters. Our methods show that simple constrained matrix decomposition can produce to low-dimensional information in functionally interpretable and transferrable space, and can be widely useful for analyzing large-scale transcriptome data.…"

Image_1_Interpretable, Scalable, and Transferrable Functional Projection of Large-Scale Transcriptome Data Using Constrained Matrix Decomposition.JPEG حسب Nicholas Panchy (6456764)

"…Finally, we demonstrate this approach is broadly applicable to data and gene sets beyond EMT and provide several recommendations on the choice between the two linear methods and the optimal algorithmic parameters. Our methods show that simple constrained matrix decomposition can produce to low-dimensional information in functionally interpretable and transferrable space, and can be widely useful for analyzing large-scale transcriptome data.…"

Image_3_Interpretable, Scalable, and Transferrable Functional Projection of Large-Scale Transcriptome Data Using Constrained Matrix Decomposition.JPEG حسب Nicholas Panchy (6456764)

"…Finally, we demonstrate this approach is broadly applicable to data and gene sets beyond EMT and provide several recommendations on the choice between the two linear methods and the optimal algorithmic parameters. Our methods show that simple constrained matrix decomposition can produce to low-dimensional information in functionally interpretable and transferrable space, and can be widely useful for analyzing large-scale transcriptome data.…"

Presentation_1_Interpretable, Scalable, and Transferrable Functional Projection of Large-Scale Transcriptome Data Using Constrained Matrix Decomposition.PDF حسب Nicholas Panchy (6456764)

"…Finally, we demonstrate this approach is broadly applicable to data and gene sets beyond EMT and provide several recommendations on the choice between the two linear methods and the optimal algorithmic parameters. Our methods show that simple constrained matrix decomposition can produce to low-dimensional information in functionally interpretable and transferrable space, and can be widely useful for analyzing large-scale transcriptome data.…"

Computational cost of the analyses. حسب Raúl López-Martín (21386066)

Image_5_Integrated analysis of single-cell RNA-seq and chipset data unravels PANoptosis-related genes in sepsis.tif حسب Wei Dai (95957)

"…Differential analysis of the subtypes identified 48 DEGs. Boruta algorithm PCA analysis identified 16 DEGs as PANoptosis-related signature genes. …"

Image_3_Integrated analysis of single-cell RNA-seq and chipset data unravels PANoptosis-related genes in sepsis.tif حسب Wei Dai (95957)

"…Differential analysis of the subtypes identified 48 DEGs. Boruta algorithm PCA analysis identified 16 DEGs as PANoptosis-related signature genes. …"