Effect of sequence padding on the performance of deep learning models in archaeal protein functional prediction
Abstract
The use of raw amino acid sequences as input for deep learning models for protein functional prediction has gained popularity in recent years. This scheme obliges to manage proteins with different lengths, while deep learning models require same-shape input. To accomplish this, zeros are usually added to each sequence up to a established common length in a process called zero-padding. However, the effect of different padding strategies on model...
Paper Details
Title
Effect of sequence padding on the performance of deep learning models in archaeal protein functional prediction
Published Date
Sep 3, 2020
Journal
Volume
10
Issue
1
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