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Found 4 papers, 4 papers with code
Towards a more inductive world for drug repurposing approaches
In this work, we first perform an in-depth evaluation of current DTI datasets and prediction models through a robust benchmarking process, and show that DTI prediction methods based on transductive models lack generalization and lead to inflated performance when evaluated as previously done in the literature, hence not being suited for drug repurposing approaches.
Sweetwater: An interpretable and adaptive autoencoder for efficient tissue deconvolution
Also, we demonstrate that Sweetwater effectively uncovers biologically meaningful patterns during the training process, increasing the reliability of the results.
DZip: improved general-purpose lossless compression based on novel neural network modeling
We propose DZip, a general-purpose compressor for sequential data that exploits the well-known modeling capabilities of neural networks (NNs) for prediction, followed by arithmetic coding.
DeepZip: Lossless Data Compression using Recurrent Neural Networks
We combine recurrent neural network predictors with an arithmetic coder and losslessly compress a variety of synthetic, text and genomic datasets.
