Danilo S Sanches
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Explore the profile of Danilo S Sanches including associated specialties, affiliations and a list of published articles.
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9
Citations
58
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Recent Articles
1.
Rocha U, Kasmanas J, Toscan R, Sanches D, Magnusdottir S, Saraiva J
PLoS Comput Biol
. 2024 Oct;
20(10):e1012530.
PMID: 39436938
We hypothesize that sample species abundance, sequencing depth, and taxonomic relatedness influence the recovery of metagenome-assembled genomes (MAGs). To test this hypothesis, we assessed MAG recovery in three in silico...
2.
Avila Santos A, de Almeida B, Bonidia R, Stadler P, Stefanic P, Mandic-Mulec I, et al.
RNA Biol
. 2024 Mar;
21(1):1-12.
PMID: 38528797
The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory...
3.
Garbelini J, Sanches D, Pozo A
BMC Bioinformatics
. 2024 Mar;
25(1):128.
PMID: 38528492
Background: Discovery biological motifs plays a fundamental role in understanding regulatory mechanisms. Computationally, they can be efficiently represented as kmers, making the counting of these elements a critical aspect for...
4.
Bonidia R, Avila Santos A, de Almeida B, Stadler P, da Rocha U, Sanches D, et al.
Entropy (Basel)
. 2023 Jul;
24(10).
PMID: 37420418
In recent years, there has been an exponential growth in sequencing projects due to accelerated technological advances, leading to a significant increase in the amount of data and resulting in...
5.
Bonidia R, Avila Santos A, de Almeida B, Stadler P, da Rocha U, Sanches D, et al.
Brief Bioinform
. 2022 Jun;
23(4).
PMID: 35753697
Recent technological advances have led to an exponential expansion of biological sequence data and extraction of meaningful information through Machine Learning (ML) algorithms. This knowledge has improved the understanding of...
6.
Bonidia R, Domingues D, Sanches D, de Carvalho A
Brief Bioinform
. 2021 Nov;
23(1).
PMID: 34750626
One of the main challenges in applying machine learning algorithms to biological sequence data is how to numerically represent a sequence in a numeric input vector. Feature extraction techniques capable...
7.
Alkhnbashi O, Mitrofanov A, Bonidia R, Raden M, Tran V, Eggenhofer F, et al.
Nucleic Acids Res
. 2021 Jun;
49(W1):W125-W130.
PMID: 34133710
CRISPR-Cas systems are adaptive immune systems in prokaryotes, providing resistance against invading viruses and plasmids. The identification of CRISPR loci is currently a non-standardized, ambiguous process, requiring the manual combination...
8.
Feature extraction approaches for biological sequences: a comparative study of mathematical features
Bonidia R, Sampaio L, Domingues D, Paschoal A, Lopes F, de Carvalho A, et al.
Brief Bioinform
. 2021 Feb;
22(5).
PMID: 33585910
As consequence of the various genomic sequencing projects, an increasing volume of biological sequence data is being produced. Although machine learning algorithms have been successfully applied to a large number...
9.
Garbelini J, Kashiwabara A, Sanches D
BMC Bioinformatics
. 2018 Jan;
19(1):4.
PMID: 29298679
Background: De novo prediction of Transcription Factor Binding Sites (TFBS) using computational methods is a difficult task and it is an important problem in Bioinformatics. The correct recognition of TFBS...