Related papers: Spike2Vec: An Efficient and Scalable Embedding App…
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, without data sharing, to detect different variants of this…
Genomics has become an essential technology for surveilling emerging infectious disease outbreaks. A wide range of technologies and strategies for pathogen genome enrichment and sequencing are being used by laboratories worldwide, together…
Understanding the host-specificity of different families of viruses sheds light on the origin of, e.g., SARS-CoV-2, rabies, and other such zoonotic pathogens in humans. It enables epidemiologists, medical professionals, and policymakers to…
Coronaviruses are membrane-enveloped, non-segmented positive-strand RNA viruses belonging to the Coronaviridae family. Various animal species, mainly mammalian and avian, are severely infected by various coronaviruses, causing serious…
This paper reviews strategies for solving problems encountered when analyzing large genomic data sets and describes the implementation of those strategies in R by packages from the Bioconductor project. We treat the scalable processing,…
Subject classification schemes are foundational to the organization, evaluation, and navigation of scientific knowledge. While expert-curated systems like Scopus provide widely used taxonomies, they often suffer from coarse granularity,…
Next-generation sequencing technologies have enhanced the scope of Internet-of-Things (IoT) to include genomics for personalized medicine through the increased availability of an abundance of genome data collected from heterogeneous sources…
As the major target of many vaccines and neutralizing antibodies against SARS-CoV-2, the spike (S) protein is observed to mutate over time. In this paper, we present statistical approaches to tackle some challenges associated with the…
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 many countries heavily finance genomic surveillance efforts.…
Pattern detection and string matching are fundamental problems in computer science and the accelerated expansion of bioinformatics and computational biology have made them a core topic for both disciplines. The SARS-CoV-2 pandemic has made…
We now need more than ever to make genome analysis more intelligent. We need to read, analyze, and interpret our genomes not only quickly, but also accurately and efficiently enough to scale the analysis to population level. There currently…
Computational complexity is a key limitation of genomic analyses. Thus, over the last 30 years, researchers have proposed numerous fast heuristic methods that provide computational relief. Comparing genomic sequences is one of the most…
Spontaneous neural activity, crucial in memory, learning, and spatial navigation, often manifests itself as repetitive spatiotemporal patterns. Despite their importance, analyzing these patterns in large neural recordings remains…
In the past several months, COVID-19 has spread over the globe and caused severe damage to the people and the society. In the context of this severe situation, an effective drug discovery method to generate potential drugs is extremely…
How can we identify similar repositories and clusters among a large online archive, such as GitHub? Determiningrepository similarity is an essential building block in studying the dynamics and the evolution of such software ecosystems. The…
The SARS-CoV-2 virus and COVID-19 disease have posed unprecedented and overwhelming demand, challenges and opportunities to domain, model and data driven modeling. This paper provides a comprehensive review of the challenges, tasks,…
The Vendi score (VS), a diversity metric recently conceived in the context of machine learning, with applications in a wide range of fields, has a few distinct advantages over the metrics commonly used in ecology. It is…
Kernel classifiers and regressors designed for structured data, such as sequences, trees and graphs, have significantly advanced a number of interdisciplinary areas such as computational biology and drug design. Typically, kernels are…
Node2Vec is a state-of-the-art general-purpose feature learning method for network analysis. However, current solutions cannot run Node2Vec on large-scale graphs with billions of vertices and edges, which are common in real-world…
With the spread of COVID-19 over the world, the need arose for fast and precise automatic triage mechanisms to decelerate the spread of the disease by reducing human efforts e.g. for image-based diagnosis. Although the literature has shown…