5 papers
ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation
Sarwan Ali, Prakash Chourasia, Zahra Tayebi +2
The amount of sequencing data for SARS-CoV-2 is several orders of magnitude larger than any virus. This will continue to grow geometrically for SARS-CoV-2, and other viruses, as ma…
T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification
Zahra Tayebi, Sarwan Ali, Prakash Chourasia +2
Cancer is a complex disease characterized by uncontrolled cell growth and proliferation. T cell receptors (TCRs) are essential proteins for the adaptive immune system, and their sp…
Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning
Prakash Chourasia, Taslim Murad, Zahra Tayebi +3
This paper presents a federated learning (FL) approach to train an AI model for SARS-Cov-2 variant classification. We analyze the SARS-CoV-2 spike sequences in a distributed way, w…
Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants
Zahra Tayebi, Sarwan Ali, Murray Patterson
The widespread availability of large amounts of genomic data on the SARS-CoV-2 virus, as a result of the COVID-19 pandemic, has created an opportunity for researchers to analyze th…
Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location
Sarwan Ali, Babatunde Bello, Zahra Tayebi +1
With the rapid spread of COVID-19 worldwide, viral genomic data is available in the order of millions of sequences on public databases such as GISAID. This Big Data creates a uniqu…