Search Results for author:
Found 6 papers, 5 papers with code
RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks
RiNALMo is the largest RNA language model to date with $650$ million parameters pre-trained on $36$ million non-coding RNA sequences from several available databases.
Finding Hamiltonian cycles with graph neural networks
Instead of devising a heuristic by hand, one can train it end-to-end using a neural network.
Learning to Untangle Genome Assembly with Graph Convolutional Networks
In this work, we explore a different approach to the central part of the genome assembly task that consists of untangling a large assembly graph from which a genomic sequence needs to be reconstructed.
A step towards neural genome assembly
De novo genome assembly focuses on finding connections between a vast amount of short sequences in order to reconstruct the original genome.
Read classification using semi-supervised deep learning
In this paper, we propose a semi-supervised deep learning method for detecting the specific types of reads that impede the de novo genome assembly process.
MinCall - MinION end2end convolutional deep learning basecaller
The model is based on deep learning and uses convolutional neural networks (CNN) in its implementation.
