Autoencoder trained on transcriptomic signals
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下载链接：
https://figshare.com/articles/dataset/Autoencoder_trained_on_transcriptomic_signals/9092045
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资源简介：
(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
创建时间：
2019-07-25
