AIMC Topic: Genome, Viral
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A deep learning approach to programmable RNA switches.
Nature communications
Oct 7, 2020
Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced ...
Viral pandemic preparedness: A pluripotent stem cell-based machine-learning platform for simulating SARS-CoV-2 infection to enable drug discovery and repurposing.
Stem cells translational medicine
Sep 22, 2020
Infection with the SARS-CoV-2 virus has rapidly become a global pandemic for which we were not prepared. Several clinical trials using previously approved drugs and drug combinations are urgently under way to improve the current situation. A vaccine ...
Predicting host taxonomic information from viral genomes: A comparison of feature representations.
PLoS computational biology
May 26, 2020
The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host. Current machine learning approaches to predicting virus host interactions have a tendency to focus...
Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study.
PloS one
Apr 24, 2020
The 2019 novel coronavirus (renamed SARS-CoV-2, and generally referred to as the COVID-19 virus) has spread to 184 countries with over 1.5 million confirmed cases. Such major viral outbreaks demand early elucidation of taxonomic classification and or...
Machine-learning based patient classification using Hepatitis B virus full-length genome quasispecies from Asian and European cohorts.
Scientific reports
Dec 11, 2019
Chronic infection with Hepatitis B virus (HBV) is a major risk factor for the development of advanced liver disease including fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). The relative contribution of virological factors to disease progres...
A Deep Learning Approach for Detecting Copy Number Variation in Next-Generation Sequencing Data.
G3 (Bethesda, Md.)
Nov 5, 2019
Copy number variants (CNV) are associated with phenotypic variation in several species. However, properly detecting changes in copy numbers of sequences remains a difficult problem, especially in lower quality or lower coverage next-generation sequen...
ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels.
BMC genomics
Apr 3, 2019
BACKGROUND: Although software tools abound for the comparison, analysis, identification, and classification of genomic sequences, taxonomic classification remains challenging due to the magnitude of the datasets and the intrinsic problems associated ...
An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes.
PloS one
Nov 14, 2018
For many disease-causing virus species, global diversity is clustered into a taxonomy of subtypes with clinical significance. In particular, the classification of infections among the subtypes of human immunodeficiency virus type 1 (HIV-1) is a routi...
A machine learning approach for viral genome classification.
BMC bioinformatics
Apr 11, 2017
BACKGROUND: Advances in cloning and sequencing technology are yielding a massive number of viral genomes. The classification and annotation of these genomes constitute important assets in the discovery of genomic variability, taxonomic characteristic...
Prediction of virus-host infectious association by supervised learning methods.
BMC bioinformatics
Mar 14, 2017
BACKGROUND: The study of virus-host infectious association is important for understanding the functions and dynamics of microbial communities. Both cellular and fractionated viral metagenomic data generate a large number of viral contigs with missing...
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