pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks-Reference-Cited by-同舟云学术

pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks

Published:2018-04-06 Issue:17 Volume:34 Page:3035-3037
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Budach Stefan¹,Marsico Annalisa¹²

Affiliation:

1. Otto-Warburg-Laboratory, RNA Bioinformatics, Max Planck Institute for Molecular Genetics, Berlin, Germany

2. Department of Mathematics and Computer Science, Free University Berlin, Berlin, Germany

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/article-pdf/34/17/3035/25702952/bty222.pdf

Reference10 articles.

1. Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning;Alipanahi;Nat. Biotechnol,2015

2. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning;Angermueller;Genome Biol,2017

3. ssHMM: extracting intuitive sequence-structure motifs from high-throughput rna-binding protein data;Heller;Nucleic Acids Res,2017

4. RNAcontext: a new method for learning the sequence and structure binding preferences of RNA-binding proteins;Kazan;PLoS Comput. Biol,2010

Cited by 89 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Big data and deep learning for RNA biology;Experimental & Molecular Medicine;2024-06-14

2. ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences;Microbiome;2024-05-09

3. CrnnCrispr: An Interpretable Deep Learning Method for CRISPR/Cas9 sgRNA On-Target Activity Prediction;International Journal of Molecular Sciences;2024-04-17

4. Proformer: a hybrid macaron transformer model predicts expression values from promoter sequences;BMC Bioinformatics;2024-02-20

5. A Universal Non-parametric Approach for Improved Molecular Sequence Analysis;Lecture Notes in Computer Science;2024