ArticleScientific reports2020
Effect of sequence padding on the performance of deep learning models in archaeal protein functional prediction.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- LiBRe: A Ligand-Aware Sequence-Based Binding Residue Prediction Model for Virtual Screening.Journal of chemical information and modeling · 2026Article
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- Developing a logistic regression model to predict spontaneous preterm birth from maternal socio-demographic and obstetric history at initial pregnancy registration.BMC pregnancy and childbirth · 2024Article
- Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers.Cancer medicine · 2024Article
- DeepRCI: predicting RNA-chromatin interactions via deep learning with multi-omics data.Quantitative biology (Beijing, China) · 2023Article
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- FFP: joint Fast Fourier transform and fractal dimension in amino acid property-aware phylogenetic analysis.BMC bioinformatics · 2022Article
- Zero-Padding and Spatial Augmentation-Based Gas Sensor Node Optimization Approach in Resource-Constrained 6G-IoT Paradigm.Sensors (Basel, Switzerland) · 2022Article
- Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms.Materials (Basel, Switzerland) · 2021Article
- Balancing Data on Deep Learning-Based Proteochemometric Activity Classification.Journal of chemical information and modeling · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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 performance and data structure is yet unknown. We propose and implement four novel types of padding the amino acid sequences. Then, we analysed the impact of different ways of padding the amino acid sequences in a hierarchical Enzyme Commission number prediction problem. Results show that padding has an effect on model performance even when there are convolutional layers implied. Contrastingly to most of deep learning works which focus mainly on architectures, this study highlights the relevance of the deemed-of-low-importance process of padding and raises awareness of the need to refine it for better performance. The code of this analysis is publicly available at https://github.com/b2slab/padding_benchmark .
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