Comments (2)
In a larger scheme, sample from a length distribution of the available reads and correct for random samples on ends of chromosomes 11 and 20 i.e. if possible, sample randomly from across the genome, lots of data though, see nanopore-wgs-consortium/NA12878
. Controls like this on training data might be better for generalization.
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Closing for now. Brief testing looks like pass or fail can be used interchangeably for B. pseudomallei.
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Related Issues (20)
- Fix logging integer assignment and implement logging module
- Adding dropout and recurrent dropout to bidirectional LSTM HOT 1
- Signal normalization HOT 1
- Code annotations
- Bug writing output history with Pandas HOT 1
- Baseline data set HOT 2
- Select task, random or largest files HOT 1
- Evaluation dataset HOT 1
- Paperspace GPU node integration HOT 2
- Spitting data into training and validation after data extraction HOT 1
- Random seeds
- Add logo
- Input file checks HOT 1
- Data exploration task HOT 1
- Speedup prediction HOT 4
- Resample chromosomes for independent evaluation data HOT 2
- Task for cleaning read data
- Pre-release
- Real-time analysis option for predict CLI HOT 1
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