Hilten et al. |

GenNet: Interpretable Neural Network Framework for Genetics

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Genomics & Multi-Omics
Step 1: Upload your data

Upload .bim file

Drag your file(s) or upload
  • Your file can be in the following formats:bim
  • Extended variant information file. A text file with no header line, and one line per variant with the following six fields: Chromosome code Variant identifier Base-pair coordinate Allele 1 Allele 2 Example: 16 16:60686_C_A 60686 60686 C A Reference: https://www.cog-genomics.org/plink/1.9/formats
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Upload .bed file

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  • Your file can be in the following formats:bed
  • (PLINK binary biallelic genotype table). Primary representation of genotype calls at biallelic variants. Must be accompanied by .bim and .fam files. Do not confuse this with the UCSC Genome Browser's BED format, which is totally different. Reference: https://www.cog-genomics.org/plink/1.9/formats
or
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Use our demo data to run
Use Demo Data

Upload .fam file

Drag your file(s) or upload
  • Your file can be in the following formats:fam
  • (PLINK sample information file).Sample information file accompanying a .bed binary genotype table.A text file with no header line, and one line per sample with the following six fields: Family ID Within-family ID ('IID'; cannot be '0') Within-family ID of father ('0' if father isn't in dataset) Within-family ID of mother ('0' if mother isn't in dataset) Sex code ('1' = male, '2' = female, '0' = unknown) Phenotype value ('1' = control, '2' = case, '-9'/'0'/non-numeric = missing data if case/control) Example: family_1 sample_1 0 0 0 1 Reference: https://www.cog-genomics.org/plink/1.9/formats
or
Don’t have a file?
Use our demo data to run
Use Demo Data
Step 2: Set Parameters
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0.990
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GRCh37

You can use GenNet to create neural networks specifically for genetics. GenNet gives you the ability to decide what knowledge should be connected to what. 

Citation:
van Hilten, A., Kushner, S.A., Kayser, M. et al. GenNet framework: interpretable deep learning for predicting phenotypes from genetic data. Commun Biol 4 , 1094 (2021). https://doi.org/10.1038/s42003-021-02622-z
Released:
Aug-31-2022
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