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Graph Theory and Remote Homology Prediction.
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

https://doi.org/10.3390/mol2net-06-06819 (registering DOI)
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
Comments on this paper
Sandra Mahan
Great write-up
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Sandra Mahan
Great write-up
This is such a great resource that you are providing and you give it away for free. I love seeing blog that understand the value. Im glad to have found this post as its such an interesting one! I am always on the lookout for quality posts and articles so i suppose im lucky to have found this! I hope you will be adding more in the future
Guillermin Agüero-Chapin
Many thanks for your encouraging words



 
 
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