Article

Analysis of SARS-CoV-2 RNA-Sequences by Interpretable Machine Learning Models

2020-05-15

Abstract excerpt

We present an approach to investigate SARS-CoV-2 virus sequences based on alignment-free methods for RNA sequence comparison. In particular, we verify a given clustering result for the GISAID data set, which was obtained analyzing the molecular differences in coronavirus populations by phylogenetic trees. For this purpose, we use alignment-free dissimilarity measures for sequences and combine them with learning ve...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 8f08decb-25ba-5c36-b553-73c8ffbfb42c
DOI: 10.1101/2020.05.15.097741

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 8 closest

Analysis of SARS-CoV-2 RNA-Sequences by Interpretable Machine Learning ModelsDOI 10.1101/2020.05.15.097741Read

CAN A MACHINE LEARNING ALGORITHM IDENTIFY SARS-COV-2 VARIANTS BASED ON CONVENTIONAL rRT-PCR? PROOF OF CONCEPTSemantic proximity Read

A novel alignment-free method for HIV-1 subtype classification.Semantic proximity Read

Vorpal: A novel RNA virus feature-extraction algorithm demonstrated through interpretable genotype-to-phenotype linear modelsSemantic proximity Read

Cluster Analysis of Visual Differences on Pairs of SARS-CoV-2 GenomesSemantic proximity Read

Unsupervised explainable AI for the collective analysis of a massive number of genome sequences: various examples from the small genome of pandemic SARS-CoV-2 to the human genomeSemantic proximity Read

An unsupervised framework for comparing SARS-CoV-2 protein sequences using LLMsSemantic proximity Read

Visual Representation of SARS-CoV-2 Genomes in Multiple Regions on Integrated MapsSemantic proximity Read

Relating SARS-CoV-2 variants using cellular automata imaging.Semantic proximity Read