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Autoencoder trained on transcriptomic signals

dataset
posted on 20.11.2019, 12:37 by sanjiv dwivedisanjiv dwivedi
(A) Microarray normalised gene expression data used in reverse training to define the disease modules: MicroarrayDataForDiseaseGene.zip

(B) 3 layer with 512 nodes in first, second and third layer deep autoencoder trained on the 20K microarray data samples: MicroarrayDeep512_512_512_AE20K.h5

(C) 3 layer with 256 nodes in first, second and third layer deep autoencoder trained on the 20K microarray data samples: MicroarrayDeep256_256_256_AE20K.h5

(D) 3 layer with 1024 nodes in first, second and third layer deep autoencoder trained on the 20K microarray data samples: MicroarrayDeep1024_1024_1024_AE20K.h5

(E) 1 layer shallow autoencoder trained on the 20K microarray data samples: MicroarrayShallow512AE20K.h5

(F) 3 layer sparsified deep autoencoder trained on the 20K micro-array data samples: MicroarrayDeep512_512_512_AE_Regularised20K.h5

(G) 3 layer denoised deep autoencoder trained on 20K micro-array data samples: MicroarrayDeep512_512_512_AE_Denoised20K.h5

(H) 5 layer funnel shaped deep autoencoder trained on 20K microarray data samples: MicroarrayDeep512_256_128_256_512_AE20K.h5

(I) 3 layer funnel shaped deep autoencoder trained on 20K microarray data samples: MicroarrayDeep512_128_512_AE20K.h5

(J) RNA seq normalised gene expression data used in reverse training to define the disease modules: RNAseqDataForDiseaseGene.zip

(K) 3 layer deep autoencoder trained on the 50K samples: RNAseqDeep512_512_512_AE50K.h5

(L) 3 layer deep autoencoder trained on the 20K samples as a first realisation: RNAseqDeep512_512_512_AE20K1.h5

(M) 3 layer deep autoencoder trained on the 20K samples as a second realisation: RNAseqDeep512_512_512_AE20K2.h5

(N) 3 layer deep autoencoder trained on the 20K samples as a third realisation: RNAseqDeep512_512_512_AE20K3.h5

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