EventsMOL2NET'20, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 6th ed.
Event submissions
Published
with-doi10.3390/mol2net-06-06819 (registering DOI)
This submission belongs to the session 04. NICEXSM-06: North-Ibero-American Congress on Exp. and Simul. Methods, Valencia, Bilbao, Spain-Miami, USA, 2020 of the event MOL2NET'20, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 6th ed.
Published date
08 May, 2020
Citation
Guillermin Agüero-Chapin, Graph Theory and Remote Homology Prediction., in Proceedings of MOL2NET'20, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 6th ed., 30 January 2020–30 January 2021, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-06-06819
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Graph Theory and Remote Homology Prediction.

image
Guillermin Agüero-Chapin 1
1. CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n 4450-208 Porto, Portugal. 2 Department of Biology, Faculty of Sciences, University
Abstract

MOL2NET Conference highlights fragments of abstracts published in special issues if journals associated to the conference. This is a fragment of the abstract of the original article that belongs to:

Big Data Analysis in Biomolecular Research, Bioinformatics, and Systems Biology with Complex Networks and Multi-Label Machine Learning Models), Biomolecules 2020, 10(1), 26; https://doi.org/10.3390/biom10010026 - 23 Dec 2019

Fragment: Alignment-free (AF) methodologies have increased in popularity in the last decades as alternative tools to alignment-based (AB) algorithms for performing comparative sequence analyses. They have been especially useful to detect remote homologs within the twilight zone of highly diverse gene/protein families and superfamilies...

(This article belongs to the Special Issue Big Data Analysis in Biomolecular Research, Bioinformatics, and Systems Biology with Complex Networks and Multi-Label Machine Learning Models)

References

Reference (Read Full Paper Free): Biomolecules 2020, 10(1), 26; https://doi.org/10.3390/biom10010026

Keywords
Graph theory
Machine Learning
Remote analogs
Manuscript
Aguero et al summary template.pdf
Poster
Aguero et al summary template.pdf
Aguero et al summary template.pdf
Highlights on: Exploring Alzheimer’s Disease with Personalized Transcriptional Signatures.
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