ArticleScientific reports2022
Tracking mutational semantics of SARS-CoV-2 genomes.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multimodal data fusion using sparse canonical correlation analysis and cooperative learning: a COVID-19 cohort study.NPJ digital medicine · 2024Article
- Multimodal Biomedical Data Fusion Using Sparse Canonical Correlation Analysis and Cooperative Learning: A Cohort Study on COVID-19.Research square · 2023Article
- VariantHunter: a method and tool for fast detection of emerging SARS-CoV-2 variants.Database : the journal of biological databases and curation · 2023Article
- Analysis of co-occurring and mutually exclusive amino acid changes and detection of convergent and divergent evolution events in SARS-CoV-2.Computational and structural biotechnology journal · 2022Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Natural language processing (NLP) algorithms process linguistic data in order to discover the associated word semantics and develop models that can describe or even predict the latent meanings of the data. The applications of NLP become multi-fold while dealing with dynamic or temporally evolving datasets (e.g., historical literature). Biological datasets of genome-sequences are interesting since they are sequential as well as dynamic. Here we describe how SARS-CoV-2 genomes and mutations thereof can be processed using fundamental algorithms in NLP to reveal the characteristics and evolution of the virus. We demonstrate applicability of NLP in not only probing the temporal mutational signatures through dynamic topic modelling, but also in tracing the mutation-associations through tracing of semantic drift in genomic mutation records. Our approach also yields promising results in unfolding the mutational relevance to patient health status, thereby identifying putative signatures linked to known/highly speculated mutations of concern.
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