Breakthroughs in high-throughput sequencing technology have resulted in exponential
growth in acquired genetic data. Classic machine learning systems have difficulties in …

Genomic signal processing (GSP) is based on the use of digital signal processing methods
for the analysis of genomic data. Convolutional neural networks (CNN) are the state‐of‐the …

Genome analysis is a complex and challenging task that concerns the differences of an
individual's genetic makeup which requires advanced computational methods. In recent …

Background: In the analysis of genomic data, feature analysis is a crucial step before
classification. Most of the feature filtering methods are based on the ranking score of the …

The advancements in genomics research have led to an exponential growth in the amount
of data generated from various sequencing technologies. Analyzing this vast amount of …

Deep learning shows potential for empowering devices to help individuals in the
examination of extensive, massive data sets through developing specific algorithms that get …

The COVID-19 pandemic declared by the World Health Organization in March 2020 is a
global challenge. This has drawn research interest in various fields, such as drug design …

Machine learning has been used in solving various bioinformatics tasks such as storage of
biomedical data, naming conventions for new and existing organisms, gene expression …

While machine learning algorithms show unparalleled promise in genomic data
classification and feature detection, many of said algorithms still pose a challenge in proper …

Abstract Next Generation Sequencing (NGS) or deep sequencing technology enables
parallel reading of multiple individual DNA fragments, thereby enabling the identification of …