Signal Peptide Prediction Using Machine Learning
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Can anyone please guide me on how do I predict the signal peptide from a protein sequence using machine learning technique?
Any guide, reference or tutorial would be very helpful.
Thank you in advance.
machine-learning deep-learning bioinformatics data-science protein-database
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Can anyone please guide me on how do I predict the signal peptide from a protein sequence using machine learning technique?
Any guide, reference or tutorial would be very helpful.
Thank you in advance.
machine-learning deep-learning bioinformatics data-science protein-database
New contributor
add a comment |
up vote
-1
down vote
favorite
up vote
-1
down vote
favorite
Can anyone please guide me on how do I predict the signal peptide from a protein sequence using machine learning technique?
Any guide, reference or tutorial would be very helpful.
Thank you in advance.
machine-learning deep-learning bioinformatics data-science protein-database
New contributor
Can anyone please guide me on how do I predict the signal peptide from a protein sequence using machine learning technique?
Any guide, reference or tutorial would be very helpful.
Thank you in advance.
machine-learning deep-learning bioinformatics data-science protein-database
machine-learning deep-learning bioinformatics data-science protein-database
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asked Nov 18 at 8:56
Gabriel Bekhar
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There are many tools out there to predict signal peptides. I'd use them first. Most signal peptides are also annotated in the Uniprot database.
But if you decide to go further with developing this, you would first need to build a dataset of signal peptide sequences in the context of the full protein sequences. I would then train a recurrent neural network on these sequences with full protein sequence as an input and signal peptide probability as an output. This probably won't work out of the box, so you'll need to do quite a bit of tweaking.
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
There are many tools out there to predict signal peptides. I'd use them first. Most signal peptides are also annotated in the Uniprot database.
But if you decide to go further with developing this, you would first need to build a dataset of signal peptide sequences in the context of the full protein sequences. I would then train a recurrent neural network on these sequences with full protein sequence as an input and signal peptide probability as an output. This probably won't work out of the box, so you'll need to do quite a bit of tweaking.
New contributor
add a comment |
up vote
1
down vote
There are many tools out there to predict signal peptides. I'd use them first. Most signal peptides are also annotated in the Uniprot database.
But if you decide to go further with developing this, you would first need to build a dataset of signal peptide sequences in the context of the full protein sequences. I would then train a recurrent neural network on these sequences with full protein sequence as an input and signal peptide probability as an output. This probably won't work out of the box, so you'll need to do quite a bit of tweaking.
New contributor
add a comment |
up vote
1
down vote
up vote
1
down vote
There are many tools out there to predict signal peptides. I'd use them first. Most signal peptides are also annotated in the Uniprot database.
But if you decide to go further with developing this, you would first need to build a dataset of signal peptide sequences in the context of the full protein sequences. I would then train a recurrent neural network on these sequences with full protein sequence as an input and signal peptide probability as an output. This probably won't work out of the box, so you'll need to do quite a bit of tweaking.
New contributor
There are many tools out there to predict signal peptides. I'd use them first. Most signal peptides are also annotated in the Uniprot database.
But if you decide to go further with developing this, you would first need to build a dataset of signal peptide sequences in the context of the full protein sequences. I would then train a recurrent neural network on these sequences with full protein sequence as an input and signal peptide probability as an output. This probably won't work out of the box, so you'll need to do quite a bit of tweaking.
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answered 2 days ago
Tilen K
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Gabriel Bekhar is a new contributor. Be nice, and check out our Code of Conduct.
Gabriel Bekhar is a new contributor. Be nice, and check out our Code of Conduct.
Gabriel Bekhar is a new contributor. Be nice, and check out our Code of Conduct.
Gabriel Bekhar is a new contributor. Be nice, and check out our Code of Conduct.
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