Search Results for author:
Found 10 papers, 9 papers with code
PhaBOX: A web server for identifying and characterizing phage contigs in metagenomic data
Motivation: There is accumulating evidence showing the important roles of bacteriophages (phages) in regulating the structure and functions of the microbiome.
PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer
Thus, there is a great demand to develop a computational method for fast and accurate phage virion protein classification.
EDU-level Extractive Summarization with Varying Summary Lengths
To fill these gaps, this paper first conducts the comparison analysis of oracle summaries based on EDUs and sentences, which provides evidence from both theoretical and experimental perspectives to justify and quantify that EDUs make summaries with higher automatic evaluation scores than sentences.
Phage family classification under Caudoviricetes: a review of current tools using the latest ICTV classification framework
Therefore, a comprehensive review and comparison of taxonomic classification tools under the new standard are needed to establish the state-of-the-art.
PhaTYP: Predicting the lifestyle for bacteriophages using BERT
In this work, we develop PhaTYP (Phage TYPe prediction tool) to improve the accuracy of lifestyle prediction on short contigs.
Accurate identification of bacteriophages from metagenomic data using Transformer
In the real metagenomic data experiment, PhaMer improves the F1-score of phage detection by 27\%.
CHERRY: a Computational metHod for accuratE pRediction of virus-pRokarYotic interactions using a graph encoder-decoder model
Experimental methods for host prediction cannot keep pace with the fast accumulation of sequenced phages.
Predicting the hosts of prokaryotic viruses using GCN-based semi-supervised learning
High-throughput sequencing and its application to the microbiome have offered new opportunities for computational approaches for predicting which hosts particular viruses can infect.
Bacteriophage classification for assembled contigs using Graph Convolutional Network
Results: In this work, we present a novel semi-supervised learning model, named PhaGCN, to conduct taxonomic classification for phage contigs.
ProDOMA: improve PROtein DOMAin classification for third-generation sequencing reads using deep learning
In summary, ProDOMA is a useful end-to-end protein domain analysis tool for long noisy reads without relying on error correction.
