Search Results for author:
Found 4 papers, 1 papers with code
Efficient Approximate Kernel Based Spike Sequence Classification
More specifically, we improve the quality of the approximate kernel using domain knowledge (computed using information gain) and efficient preprocessing (using minimizers computation) to classify coronavirus spike protein sequences corresponding to different variants (e. g., Alpha, Beta, Gamma).
Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification
The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome -- millions of sequences and counting.
Benchmarking Machine Learning Robustness in Covid-19 Spike Sequence Classification
The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 viral genome --- millions of sequences and counting.
A k-mer Based Approach for SARS-CoV-2 Variant Identification
With the rapid spread of the novel coronavirus (COVID-19) across the globe and its continuous mutation, it is of pivotal importance to design a system to identify different known (and unknown) variants of SARS-CoV-2.
