Physical Gene Regulatory Networks In C.elegans by Kaggle | Healthcare

About this Dataset

Physical Gene Regulatory Networks In C.elegans

Physical Gene Regulatory Networks in C.elegans

239,001 Regulatory Interactions from 289 Wild-type Young Adult Datasets

About this dataset

This dataset provides highly complex physical gene regulatory networks in young adult wild-type (WT) C.elegans worms. With a total of 239,001 regulatory interactions collected from 289 datasets, this dataset is a great resource for studying gene regulation and exploring how this gene activity contributes to organism function under varying bio-environmental conditions. Our collection of datasets contains 126 genes and 495 transcription factors, along with functional knockdown data that has been used to validate the physical gene regulatory networks present in the young adult C.elegans worms. Moreover, researchers and biologists can leverage this data to gain valuable insights on how various genotypes, ages and strains are associated with different perturbations in their biological features and ultimately uncover new discoveries about the network of relationships that exist between these genes inside animals. This comprehensive dataset will be essential for conducting research related to such topics as life development processes or age-related diseases - further enriching our understanding of life!

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How to use the dataset

This guide will help you understand how to use this dataset of physical gene regulatory networks to research and analyze young adult C.elegans worms.

Understand the columns in the dataset: In this dataset, there are 239,001 regulatory interactions from 289 datasets consisting of 126 genes and 495 transcription factors registered with their genotype, age, strain, perturbation type, data type, data source and source used. Additionally, comments and regulator are also included in the columns for more information about each interaction.

Know your research goal: Determine what it is you wish to discover when working with this dataset so that you can work efficiently when sorting or exploring the data within it. Knowing your goals for the analysis will be helpful for deciding which column may provide valuable insights in relation to our project objectives when doing any kind of filter or sorting within the internal structure of our database file itself.

Analyzing Specific Types Of Data: Once your goals have been established it is then important to start analyzing specific types of data that are relevant for achieving those objectives as we go further into understanding what kind of database structures we will need to read from on a molecular level (this includes focusing on different types such as transcription factor levels). When looking at all these individual components together they can offer insight into how regulation may be changing within a cell’s environment & which pathways could become activated/ deactivated due its presence or absence throughout different conditions).

4 Keeping Logs And Documents Up To Date: Once done with some sortings or filters on certain columns make sure that your logs/documents stay up-to-date and match up with any changes made during analysis so as not mix-up usage across different documents/sessions throughout our project lifespan itself! This is highly recommended as having an organized record keeping system helps ensure accuracy when dealing with large volumes of information over time periods (thus making sure nothing gets overlooked accidentally!).

We hope these tips help get you started into exploring Physical Gene Regulatory Networks in C Elegans’! If you have any questions feel free to reach out via message – we would love hearing about how things go after implementing them into practice!

Research Ideas

Training machine-learning algorithms to develop automated approaches in predicting gene expression levels of individual regulatory networks.

Using this dataset alongside data from RNA-seq experiments to investigate how genetic mutations, environmental changes, and other factors can affect gene regulation across C.elegans populations.

Exploring the correlation between transcription factor binding sites and gene expression levels to predict potential target genes for a given transcription factor

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: TableS1_datasets_for_prior.csv

Column name	Description
Genotype	The genetic makeup of the organism. (String)
Age	The age of the organism. (Integer)
Strain	The strain of the organism. (String)
Perturbation type	The type of perturbation used to study the gene regulatory network. (String)
Data type	The type of data used to study the gene regulatory network. (String)
Data source	The source of the data used to study the gene regulatory network. (String)
Source	The source of the gene regulatory network. (String)
Comments	Any additional comments related to the gene regulatory network. (String)

File: TableS3_WT_functional_priors.csv

Column name	Description
regulator	The gene that is regulating the target gene. (String)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Tables1 Datasets For Prior

@kaggle.thedevastator_physical_gene_regulatory_networks_in_c_elegans.tables1_datasets_for_prior

9.79 KB
168 rows
8 columns


CREATE TABLE tables1_datasets_for_prior (
  "genotype" VARCHAR,
  "age" VARCHAR,
  "strain" VARCHAR,
  "perturbation_type" VARCHAR,
  "data_type" VARCHAR,
  "data_source" VARCHAR,
  "source" VARCHAR,
  "comments" VARCHAR
);