Assays

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592 Assays visible to you, out of a total of 1151

A Jaccard Index of the overlap between prognostic and hallmark genes for 17 cancer types across different mapping schemes. The impact of selecting different mapping schemes was assessed by pairwise comparisons where there were 5 or more shared genes.

Submitter: Katy Wolstencroft

Biological problem addressed: Biological Network Analysis

Snapshots: No snapshots

Model files for FMv1.5. The model is based on FMv1 of Chew et al. PNAS 2014, which is also in FAIRDOMHub and linked to the Model record as an 'Attribution'. FMv1 was extended in this work by Hannah Kinmonth-Schultz and Daniel Seaton, in Matlab.

A Weighted Gene Co-Expression Network Analysis (WGCNA) of breast cancer prognostic genes (derived from transcriptome data from the TCGA Genomics Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/)), and cancer hallmark genes.

No description specified

Submitter: Katy Wolstencroft

Biological problem addressed: Biological Network Analysis

Snapshots: No snapshots

Application of the LoRAS oversampling approach on single-cell/single-nuclei data to annotate/identify specific cell populations in new data based on previously, manually curated data.

Submitter: Markus Wolfien

Biological problem addressed: Annotation

Snapshots: No snapshots

Build the chemical defensome gene list for 5 fish: Zebrafish (Danio rerio), Atlantic cod (Gadus morhua), medaka (Oryzias latipes), Atlantic killifish (Fundulus heteroclitus) and stickleback (Gasterosteus aculeatus). Source code and relevant files can be found on GitHub: https://github.com/zhxiaokang/fishDefensome/tree/main/defensomeGenes

Submitter: Xiaokang Zhang

Biological problem addressed: Stress response/Adaptation

Snapshots: No snapshots

To study the defensome genes' expression in early developmental stages of zebrafish and stickleback. Souce code and relevant files can be found on GitHub: https://github.com/zhxiaokang/fishDefensome/tree/main/developmentalStages

Submitter: Xiaokang Zhang

Biological problem addressed: Stress response/Adaptation

Snapshots: Snapshot 1

DOI: 10.15490/fairdomhub.1.assay.1379.1

Exposing zebrafish to benzo(a)pyrene (B(a)P) (gene counts from NCBI GEO: GSE64198, previously published by Fang, et al. 2015. Souce code and relevant files can be found on GitHub: https://github.com/zhxiaokang/fishDefensome/tree/main/exposureResponse

Submitter: Xiaokang Zhang

Biological problem addressed: Stress response/Adaptation

Snapshots: No snapshots

No description specified

Submitter: Jacqueline Wolf

Assay type: Experimental Assay Type

Technology type: Rna-seq

Snapshots: No snapshots

No description specified

Transcriptome analysis suggests a compensatory role of the cofactors coenzyme A and NAD+ in medium-chain acyl-CoA dehydrogenase knockout mice. Expression profiling by high throughput sequencing

Submitter: PoLiMeR_user Martines_data

Assay type: Transcriptomics

Technology type: Rna-seq

Snapshots: No snapshots

Motivated by an increasing population and the desire to grow plants more efficiently,
attention has turned to the use of Light Emitting Diodes (LEDs) to illuminate plants
which are grown indoors. Indoor growing facilities enable closely controlled and mon-
itored environmental conditions. More and more of these facilities exchange High
Pressure Sodium (HPS) lamps for LED lighting since they provide more efficient
lighting and the possibility to control light intensity and quality in order to
...

Submitter: Felix Steimle

Assay type: Experimental Assay Type

Technology type: Chlorophyll Fluorescence Analysis

Snapshots: No snapshots

Differential expression analysis (using R package edgeR) on liver gene expression between salmon with all four fads2 genes knockout, only fads2d6b & fads2d6c knockout and wildtype. All salmon was given either a low-PUFA diet or a high-PUFA diet

Submitter: Yang Jin

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

No description specified

Submitter: Yang Jin

Assay type: Experimental Assay Type

Technology type: Technology Type

Snapshots: No snapshots

Here, we conduct a proof of principle by comparing a 2D and 3D fluorescent image analysis based approach on unlabeled cardiomyocytes. Based on the CellProfiler software, we extracted high-dimensional features of individual cells and nuclei, which are subsequently down-sampled and clustered. These clusters are furthermore benchmarked via different machine learning classifiers (e.g., AdaBoost, Gradient Boosting, Random Forest) as the ground truth for our proposed approach.

Submitter: Markus Wolfien

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

Docking results of pteridine-based compounds in different target PTR1 and DHFR receptors and the off-target human DHFR when using an induced fit docking routine with an initial crude ligand placement step, subsequent receptor optimization in response to ligand binding and another docking step into the optimized receptor.

Computational prediction of physicochemical and advanced descriptors related to ADME-Tox.

Assessment of possible linear correlations between predicted ADMET descriptors from QikProp (Schrödinger, LLC, New York, NY) runs and experimentally determined activities against T. brucei brucei bloodstream forms with the help of a Python script.

Submitter: Ina Poehner

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

Assessment of the possible multiple correlation between experimentally determined TbPTR1 and TbDHFR inhibition values and corresponding anti-parasitic activities against T. brucei brucei bloodstream forms using a Python script.

Submitter: Ina Poehner

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

In silico check and filtering for potential Pan-assay interference compounds.

Submitter: Ina Poehner

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

Compound data, library construction schemes and preparation routine for small drug-like molecules as ligands in docking and for further analysis.

No description specified

Submitter: Gudrun Gygli

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

To obtain each of the figure 4A - 4D please download "Main Figure 4 Copasi" and open the sub-directory with the name of the sub-figure, run the Copasi files and the time dependence simulation. This will reproduce the figure in this paper.

Submitter: Alexey Kolodkin

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

To obtain each of the figure 2A - 2E please download "Main Figure Copasi" and open the sub-directory with the name of the sub-figure, run the Copasi files and the time dependence simulation. This will reproduce the figure in this paper.

Submitter: Alexey Kolodkin

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

No description specified

Submitter: Alexey Kolodkin

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

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