Assessing Resilience of Machine Learning Algorithms on SARS-CoV-2 Genome Sequences with Long-Read Specific Errors

The article titled Assessing the Resilience of Machine Learning Classification Algorithms on SARS-CoV-2 Genome Sequences Generated with Long-Read Specific Errors explores the ability of machine learning classification algorithms to handle errors that arise while sequencing the genome of SARS-CoV-2. The study focuses on long-read specific errors and works towards assessing the resiliency of algorithms to handle them. The article highlights the importance of open access research, which allows for easy reuse of published work, and the need for proper citation to acknowledge the original source. MDPI is the publishing company that makes all their published articles available under open access licenses. Dr. Paolo Missier is the person mentioned in the article and has contributed to research in the areas of data management, provenance, and scientific workflows.