القائمة
Use when after uploading a sample list to InjectionDesign and before performing inter-batch balancing and intra-batch randomization.
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استنادا إلى تصنيف SOC المهني
Use when when you have a neural network or machine learning model with multiple tunable hyperparameters (layer size, regularization strength, dropout) or design choices (e.
Use when when building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations.
Use when after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series.
Use when after running inference on a trained structure prediction model with one or more input modalities (1H NMR, 13C NMR, or combined), you have generated predicted molecular formulas and connectivity graphs that need to be compared against known ground truth structures.
Use when after computing a histogram of all pairwise mass differences from an MSI dataset, use this skill when you have observed mass difference peaks that may correspond to known adducts (e.g., [M+H]+, [M+Na]+, [M−H2O]+).
Use when you have a trained formula ranking model (such as MIST-CF) and want to measure the specific performance gain from incorporating multiple positive-mode adduct types (e.g., [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+) instead of restricting predictions to [M+H]+ only.
| name | plate-layout-parameter-configuration |
| description | Use when after uploading a sample list to InjectionDesign and before performing inter-batch balancing and intra-batch randomization. |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3435","edam_topics":["http://edamontology.org/topic_0091","http://edamontology.org/topic_3520"],"tools":["InjectionDesign"],"techniques":["GC-MS"]} |
| derived_from | [{"doi":"10.1101/2023.02.26.530140v1.article-info","title":"InjectionDesign"}] |
| evidence_spans | ["LC/GC-MS-based Multi-Omics Injection-Plate Design Web Service"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_injectiondesign_cq","doi":"10.1101/2023.02.26.530140v1.article-info","title":"InjectionDesign"}],"dedup_kept_from":"coll_injectiondesign_cq"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.1101/2023.02.26.530140v1.article-info","all_source_dois":["10.1101/2023.02.26.530140v1.article-info"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Configure plate type, sample capacity, injection order, and axis labeling in InjectionDesign to define the physical and logical layout constraints for LC/GC-MS injection plate design. This skill bridges sample list preparation and QC sample position predefinition by establishing the plate geometry and visualization parameters.
After uploading a sample list to InjectionDesign and before performing inter-batch balancing and intra-batch randomization. Use this skill when you need to specify how many analytical samples fit on a single plate (excluding QC samples), choose between 96-well or 384-well plate formats, and define whether samples will be injected row-wise or column-wise to match your LC/GC-MS instrument's autosampler configuration.
In InjectionDesign Step 2 (Design Parameters and Predefinition for QC samples position), set the plate type (e.g., 96-well, 384-well) based on your physical plate format. Specify the maximum number of analytical samples per plate, excluding QC sample positions—this determines how many rows or columns remain available for experimental samples after QC slots are reserved. Define the layout direction (row-wise or column-wise) to match your autosampler's injection sequence capability. Configure the yAxis label format for plate visualization (typically row labels A–H for 96-well plates). These parameters constrain the optimization algorithms in Step 3 and determine the final worksheet structure in Step 4.
