ArticleBioinformatics (Oxford, England)2018
pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Augmented prediction of multi-species protein-RNA interactions using evolutionary conservation of RNA-binding proteins.Nature communications · 2026Article
- Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity.Journal of cheminformatics · 2026Article
- MiLaSol: modeling protein solubility by mixing up multiple protein language models.Bioinformatics advances · 2026Article
- RBPsuite 2.0: an updated RNA-protein binding site prediction suite with high coverage on species and proteins based on deep learning.BMC biology · 2025Article
- Big data and deep learning for RNA biology.Experimental & molecular medicine · 2024Review
- ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences.Microbiome · 2024Article
- CrnnCrispr: An Interpretable Deep Learning Method for CRISPR/Cas9 sgRNA On-Target Activity Prediction.International journal of molecular sciences · 2024Article
- Proformer: a hybrid macaron transformer model predicts expression values from promoter sequences.BMC bioinformatics · 2024Article
- Discovery of a non-canonical GRHL1 binding site using deep convolutional and recurrent neural networks.BMC genomics · 2023Article
- Predictive analyses of regulatory sequences with EUGENe.Nature computational science · 2023Article
- Applications for Deep Learning in Epilepsy Genetic Research.International journal of molecular sciences · 2023Review
- A systematic benchmark of machine learning methods for protein-RNA interaction prediction.Briefings in bioinformatics · 2023Article
- Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning.Genome biology · 2023Article
- PrismNet: predicting protein-RNA interaction using in vivo RNA structural information.Nucleic acids research · 2023Article
- BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences.Cell systems · 2023Article
- MncR: Late Integration Machine Learning Model for Classification of ncRNA Classes Using Sequence and Structural Encoding.International journal of molecular sciences · 2023Article
- DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis.Nucleic acids research · 2023Article
- A computational map of the human-SARS-CoV-2 protein-RNA interactome predicted at single-nucleotide resolution.NAR genomics and bioinformatics · 2023Article
- Deep learning for detecting and elucidating human T-cell leukemia virus type 1 integration in the human genome.Patterns (New York, N.Y.) · 2023Article
- layerUMAP: A tool for visualizing and understanding deep learning models in biological sequence classification using UMAP.iScience · 2022Article
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Authors and funding
2 authors.
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Abstract
Summary: Convolutional neural networks (CNNs) have been shown to perform exceptionally well in a variety of tasks, including biological sequence classification. Available implementations, however, are usually optimized for a particular task and difficult to reuse. To enable researchers to utilize these networks more easily, we implemented pysster, a Python package for training CNNs on biological sequence data. Sequences are classified by learning sequence and structure motifs and the package offers an automated hyper-parameter optimization procedure and options to visualize learned motifs along with information about their positional and class enrichment. The package runs seamlessly on CPU and GPU and provides a simple interface to train and evaluate a network with a handful lines of code. Using an RNA A-to-I editing dataset and cross-linking immunoprecipitation (CLIP)-seq binding site sequences, we demonstrate that pysster classifies sequences with higher accuracy than previous methods, such as GraphProt or ssHMM, and is able to recover known sequence and structure motifs. Availability and implementation: pysster is freely available at https://github.com/budach/pysster. Supplementary information: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.