T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification

1 code implementation • 25 Apr 2023 • Zahra Tayebi, Sarwan Ali, Prakash Chourasia, Taslim Murad, Murray Patterson

Sparse coding is a popular technique in machine learning that enables the representation of data with a set of informative features and can capture complex relationships between amino acids and identify subtle patterns in the sequence that might be missed by low-dimensional methods.

Multi-class Classification Specificity

ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation

1 code implementation • 6 Apr 2023 • Sarwan Ali, Prakash Chourasia, Zahra Tayebi, Babatunde Bello, Murray Patterson

In this work, we propose \emph{ViralVectors}, a compact feature vector generation from virome sequencing data that allows effective downstream analysis.

4k Decision Making

Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning

no code implementations • 17 Feb 2023 • Prakash Chourasia, Taslim Murad, Zahra Tayebi, Sarwan Ali, Imdad Ullah Khan, Murray Patterson

This paper presents a federated learning (FL) approach to train an AI model for SARS-Cov-2 variant classification.

Federated Learning Privacy Preserving

Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants

1 code implementation • 18 Oct 2021 • Zahra Tayebi, Sarwan Ali, Murray Patterson

We then show that with the appropriate feature selection, we can efficiently and effectively cluster the spike sequences based on the different variants.

Clustering feature selection

Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location

1 code implementation • 2 Oct 2021 • Sarwan Ali, Babatunde Bello, Zahra Tayebi, Murray Patterson

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.