System and methods for upsampling of decompressed genomic data after lossy compression using a neural network
A system and methods for upsampling of decompressed biological data after lossy compression using a neural network integrates AI-based techniques to enhance compression quality. It incorporates a novel deep-learning neural network that upsamples decompressed data to restore information lost during lossy compression, taking advantage of cross-correlations between biological data sets.
1 . A system for upsampling of decompressed biological data after lossy compression using a neural network, comprising:
a computing system comprising at least a memory and a processor;
two or more datasets that are substantially correlated and which have been compressed with lossy compression, the two or more datasets comprising biological data;
a deep learning neural network configured to recover lost information associated with a compressed bit stream; and
a decoder comprising a first plurality of programming instructions that, when operating on the processor, cause the computing system to:
receive a compressed bit stream, the compressed bit stream comprising cross-correlated biological data;
decompress each of the compressed bit stream; and
use the decompressed bit stream as an input into the deep learning neural network to recover lost information associated with the biological data.
2 . The system of claim 1 , wherein the biological data comprises parallel biological datasets.
3 . The system of claim 1 , wherein the two or more datasets comprise genomic data from a subset of the human genome.
4 . The system of claim 1 , wherein the deep learning neural network is a neural network that can recover signals from a compressed bitstream.
5 . The system of claim 1 , wherein the compressed bit stream comprises a plurality of channels, wherein each of the plurality of channels is associated with a genomic dataset.
6 . A method for upsampling of decompressed biological data after lossy compression using a neural network, comprising the steps of:
training a deep learning neural network to recover lost information associated with a compressed bit stream;
receiving the compressed bit stream, the compressed bit stream comprising cross-correlated biological data;
decompressing the compressed bit stream; and
using the decompressed bit stream as an input into the deep learning neural network to recover information lost during lossy compression of the biological data.
7 . The method of claim 6 , wherein the biological data comprises parallel biological datasets.
8 . The method of claim 6 , wherein the two or more datasets comprise genomic data from a subset of the human genome.
9 . The method of claim 6 , wherein the deep learning neural network is a neural network that can recover signals from a compressed bitstream.
10 . The method of claim 6 , wherein the compressed bit stream comprises a plurality of channels, wherein each of the plurality of channels is associated with a genomic dataset.
11 . One or more non-transitory computer-storage media having computer-executable instructions embodied thereon that, when executed by one or more processors of a computing system employing a neural upsampler configured for biological data, cause the computing system to perform the method of claim 6 .