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https://github.com/applicativesystem/go-microrna-deep

a microRNA deep learning go package
https://github.com/applicativesystem/go-microrna-deep

bioinformatics deep-learning deep-neural-networks genome-analysis micrornas neural-networks

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a microRNA deep learning go package

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# go-microRNAs-deep

- a microRNA go package for implementing the deep learning approaches.
- takes the predicted microRNAs, fasta and extracts the fasta, upstream, downstream, tokenization and neural network.
- prepares the microRNA predictions from all microRNA target predictions tools into a single package and gives you the structured data with tokenization for direct input as weights into the neural networks.
- I implemented this package in both GO and RUST for a special issue in Springer Invitation.

```
gauavsablok@gauravsablok ~/Desktop/go/go-microRNA-deep ±main⚡ » \
go run main.go
This analyzes the microRNA prediction and makes them ready for the deep learning approaches

Usage:
analyzePred [command]

Available Commands:
completion Generate the autocompletion script for the specified shell
help Help about any command
psRNAanalyzer
psRobot
psRNAmapanalyze
tapiranalyzer
tarHunter
targetFinder

Flags:
-h, --help help for analyzePred

Use "analyzePred [command] --help" for more information about a command.
gauavsablok@gauravsablok ~/Desktop/go/go-microRNA-deep ±main⚡ » \
go run main.go psRNAanalyzer -h
Analyzes and prepares the psRNA target predictions for the deep learning

Usage:
analyzePred psRNAanalyzer [flags]

Flags:
--expectation value float expectation value (default 0.5)
-f, --fastapred string fasta predict (default "fasta file for the predictions")
-h, --help help for psRNAanalyzer
-p, --psRNAPred string psRNA predictions (default "psRNA microRNA predictions")
gauavsablok@gauravsablok ~/Desktop/go/go-microRNA-deep ±main⚡ » \
go run main.go psRNAmapanalyze -h
Analyze the psRNA map alignment of the reads to the genome

Usage:
analyzePred psRNAmapanalyze [flags]

Flags:
-f, --fastapred string fasta predict (default "fasta file for the predictions")
-h, --help help for psRNAmapanalyze
-P, --psRNAfile string map reads to the genome file (default "RNA mapping file")
gauavsablok@gauravsablok ~/Desktop/go/go-microRNA-deep ±main⚡ » \
go run main.go tapiranalyzer -h
Analyzes tapir target predictions for the deep learning

Usage:
analyzePred tapiranalyzer [flags]

Flags:
-f, --fastapred string fasta predict (default "fasta file for the predictions")
-h, --help help for tapiranalyzer
-p, --psRNAPred string psRNA predictions (default "psRNA microRNA predictions")
gauavsablok@gauravsablok ~/Desktop/go/go-microRNA-deep ±main⚡ » \
go run main.go tarHunter -h
analyze the tarHunter results for the miRNA predictions

Usage:
analyzePred tarHunter [flags]

Flags:
-D, --downstream int downstream of the miRNA predictions (default 10)
-f, --fastapred string fasta predict (default "fasta file for the predictions")
-h, --help help for tarHunter
-T, --tarhunterfile string tarhunter analysis (default "tarHunter predictions")
-U, --upstream int upstream of the miRNA predictions (default 10)
gauavsablok@gauravsablok ~/Desktop/go/go-microRNA-deep ±main⚡ » \
go run main.go targetFinder -h
analyzes the targetFinder results for the miRNA predictions

Usage:
analyzePred targetFinder [flags]

Flags:
-D, --downstream int downstream of the miRNA predictions (default 10)
-f, --fastapred string fasta predict (default "fasta file for the predictions")
-h, --help help for targetFinder
-T, --targetFinderfile string targetFinder analysis (default "targetFinder predictions")
-U, --upstream int upstream of the miRNA predictions (default 10)

go run main.go psRobot -h
analyzes the results from the psRobot microRNA predictions

Usage:
analyzePred psRobot [flags]

Flags:
-D, --downstream int downstream of the miRNA predictions (default 10)
-f, --fastapred string fasta predict (default "fasta file for the predictions")
-h, --help help for psRobot
-R, --psRobotfile string psRobot analysis (default "psRobot predictions")
-U, --upstream int upstream of the miRNA predictions (default 10)

```

Gaurav Sablok