Literature DB >> 31834361

MLDSP-GUI: an alignment-free standalone tool with an interactive graphical user interface for DNA sequence comparison and analysis.

Gurjit S Randhawa1, Kathleen A Hill2, Lila Kari3.   

Abstract

SUMMARY: Machine Learning with Digital Signal Processing and Graphical User Interface (MLDSP-GUI) is an open-source, alignment-free, ultrafast, computationally lightweight, and standalone software tool with an interactive GUI for comparison and analysis of DNA sequences. MLDSP-GUI is a general-purpose tool that can be used for a variety of applications such as taxonomic classification, disease classification, virus subtype classification, evolutionary analyses, among others.
AVAILABILITY AND IMPLEMENTATION: MLDSP-GUI is open-source, cross-platform compatible, and is available under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/). The executable and dataset files are available at https://sourceforge.net/projects/mldsp-gui/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Year:  2020        PMID: 31834361     DOI: 10.1093/bioinformatics/btz918

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  3 in total

1.  Exploring the Lethality of Human-Adapted Coronavirus Through Alignment-Free Machine Learning Approaches Using Genomic Sequences.

Authors:  Rui Yin; Zihan Luo; Chee Keong Kwoh
Journal:  Curr Genomics       Date:  2021-12-31       Impact factor: 2.689

2.  WalkIm: Compact image-based encoding for high-performance classification of biological sequences using simple tuning-free CNNs.

Authors:  Saeedeh Akbari Rokn Abadi; Amirhossein Mohammadi; Somayyeh Koohi
Journal:  PLoS One       Date:  2022-04-15       Impact factor: 3.752

3.  Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study.

Authors:  Gurjit S Randhawa; Maximillian P M Soltysiak; Hadi El Roz; Camila P E de Souza; Kathleen A Hill; Lila Kari
Journal:  PLoS One       Date:  2020-04-24       Impact factor: 3.240

  3 in total

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