# US 12,224,044 B1 · System and methods for upsampling of decompressed genomic data after lossy compression using a neural network

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- Number: US 12,224,044 B1
- Document ID: US12224044B1
- Status: Granted
- Kind: B1
- Application number: 18/769,416
- Filed: 2024-07-11
- Granted: 2025-02-11
- Earliest claimed: 2023-12-12
- Assignees: Atombeam Technologies Inc.
- Applicants: AtomBeam Technologies Inc.
- Inventors: Zhu Li; Paras Maharjan; Brian R. Galvin
- Web page: https://patentgrep.com/patent/US12224044B1

## CPC

- G06N 3/0455: Auto-encoder networks; Encoder-decoder networks
- G16B 50/50: Compression of genetic data
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/096: Transfer learning
- G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- H03M 7/3059: {Digital compression and data reduction techniques where the original information is represented by a subset or similar information, e.g. lossy compression}
- H03M 7/6005: {Decoder aspects}
- H03M 7/70: {Type of the data to be coded, other than image and sound}
- H04N 19/132: Sampling, masking or truncation of coding units, e.g. adaptive resampling, frame skipping, frame interpolation or high-frequency transform coefficient masking
- H04N 19/42: characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation (H04N19/635 takes precedence)
- H04N 19/59: involving spatial sub-sampling or interpolation, e.g. alteration of picture size or resolution
- H04N 19/80: Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation (H04N19/635, H04N19/86 take precedence)
- H04N 19/86: involving reduction of coding artifacts, e.g. of blockiness
- G16B 25/10: Gene or protein expression profiling; Expression-ratio estimation or normalisation
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- H03M: CODING; DECODING; CODE CONVERSION IN GENERAL (using fluidic means F15C4/00; optical analogue/digital converters G02F7/00; coding, decoding or code conversion, specially adapted for particular applications, see the relevant subclasses, e.g. G01D, G01R, G06F, G06T, G09G, G10L, G11B, G11C, H04B, H04L, H04M, H04N; ciphering or deciphering for cryptography or other purposes involving the need for secrecy G09C)
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION

## Abstract

A system and methods for upsampling of decompressed genomic 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 genomic data sets.

## Claims

### Claim 1

A system for upsampling of decompressed genomic 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 genomic data;
a deep learning neural network configured to recover lost information associated with a compressed bit stream, wherein the deep learning neural network comprises a multi-task learning architecture with shared layers for learning common representations across two or more datasets and task specific layers for adapting the common representations to specific upsampling tasks; 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 genomic 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 genomic data.

### Claim 2 (depends on 1)

The system of claim 1, wherein the genomic data comprises parallel genome datasets.

### Claim 3 (depends on 1)

The system of claim 1, wherein the two or more datasets comprise genomic data from a subset of the human genome.

### Claim 4 (depends on 1)

The system of claim 1, wherein the data comprises integrative-omics data.

### Claim 5 (depends on 1)

The system of claim 1, wherein the deep learning neural network is a neural network that can recover signals from a compressed bitstream.

### Claim 6 (depends on 1)

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.

### Claim 7

A method for upsampling of decompressed genomic data after lossy compression using a neural network, comprising the steps of:
training a deep learning neural network configured to recover lost information associated with a compressed bit stream, wherein the deep learning neural network comprises a multi-task learning architecture with shared layers for learning common representations across two or more datasets and task specific layers for adapting the common representations to specific upsampling tasks; and
receiving the compressed bit stream, the compressed bit stream comprising cross-correlated genomic 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 genomic data.

### Claim 8 (depends on 7)

The method of claim 7, wherein the genomic data comprises parallel genome datasets.

### Claim 9 (depends on 7)

The method of claim 7, wherein the two or more datasets comprise genomic data from a subset of the human genome.

### Claim 10 (depends on 7)

The method of claim 7, wherein the data comprises integrative-omics data.

### Claim 11 (depends on 7)

The method of claim 7, wherein the deep learning neural network is a neural network that can recover signals from a compressed bitstream.

### Claim 12 (depends on 7)

The method of claim 7, wherein the compressed bit stream comprises a plurality of channels, wherein each of the plurality of channels is associated with a genomic dataset.

### Claim 13 (depends on 7)

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 genomic data, cause the computing system to perform the method of claim 7.