5 citations · 6 across the 10 of their papers we have counts for
17 papers
Evaluating COVID-19 Sequence Data Using Nearest-Neighbors Based Network Model
Sarwan Ali
The SARS-CoV-2 coronavirus is the cause of the COVID-19 disease in humans. Like many coronaviruses, it can adapt to different hosts and evolve into different lineages. It is well-k…
Informative Initialization and Kernel Selection Improves t-SNE for Biological Sequences
Prakash Chourasia, Sarwan Ali, Murray Patterson
The t-distributed stochastic neighbor embedding (t- SNE) is a method for interpreting high dimensional (HD) data by mapping each point to a low dimensional (LD) space (usually two-…
Reads2Vec: Efficient Embedding of Raw High-Throughput Sequencing Reads Data
Prakash Chourasia, Sarwan Ali, Simone Ciccolella +2
The massive amount of genomic data appearing for SARS-CoV-2 since the beginning of the COVID-19 pandemic has challenged traditional methods for studying its dynamics. As a result,…
Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers
Haris Mansoor, Sarwan Ali, Shafiq Alam +3
Analysis of the fairness of machine learning (ML) algorithms recently attracted many researchers' interest. Most ML methods show bias toward protected groups, which limits the appl…
Efficient Approximate Kernel Based Spike Sequence Classification
Sarwan Ali, Bikram Sahoo, Muhammad Asad Khan +3
Machine learning (ML) models, such as SVM, for tasks like classification and clustering of sequences, require a definition of distance/similarity between pairs of sequences. Severa…
PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences
Sarwan Ali, Babatunde Bello, Prakash Chourasia +3
COVID-19 pandemic, is still unknown and is an important open question. There are speculations that bats are a possible origin. Likewise, there are many closely related (corona-) vi…