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DASSI: differential architecture search for splice identification from DNA sequences

Overview of attention for article published in BioData Mining, February 2021
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6 tweeters

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4 Mendeley
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Title
DASSI: differential architecture search for splice identification from DNA sequences
Published in
BioData Mining, February 2021
DOI 10.1186/s13040-021-00237-y
Pubmed ID
Authors

Shabir Moosa, Prof. Abbes Amira, Dr. Sabri Boughorbel

Abstract

The data explosion caused by unprecedented advancements in the field of genomics is constantly challenging the conventional methods used in the interpretation of the human genome. The demand for robust algorithms over the recent years has brought huge success in the field of Deep Learning (DL) in solving many difficult tasks in image, speech and natural language processing by automating the manual process of architecture design. This has been fueled through the development of new DL architectures. Yet genomics possesses unique challenges that requires customization and development of new DL models. We proposed a new model, DASSI, by adapting a differential architecture search method and applying it to the Splice Site (SS) recognition task on DNA sequences to discover new high-performance convolutional architectures in an automated manner. We evaluated the discovered model against state-of-the-art tools to classify true and false SS in Homo sapiens (Human), Arabidopsis thaliana (Plant), Caenorhabditis elegans (Worm) and Drosophila melanogaster (Fly). Our experimental evaluation demonstrated that the discovered architecture outperformed baseline models and fixed architectures and showed competitive results against state-of-the-art models used in classification of splice sites. The proposed model - DASSI has a compact architecture and showed very good results on a transfer learning task. The benchmarking experiments of execution time and precision on architecture search and evaluation process showed better performance on recently available GPUs making it feasible to adopt architecture search based methods on large datasets. We proposed the use of differential architecture search method (DASSI) to perform SS classification on raw DNA sequences, and discovered new neural network models with low number of tunable parameters and competitive performance compared with manually engineered architectures. We have extensively benchmarked DASSI model with other state-of-the-art models and assessed its computational efficiency. The results have shown a high potential of using automated architecture search mechanism for solving various problems in the field of genomics.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 25%
Student > Master 1 25%
Unknown 2 50%
Readers by discipline Count As %
Unspecified 1 25%
Biochemistry, Genetics and Molecular Biology 1 25%
Unknown 2 50%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 February 2021.
All research outputs
#10,173,980
of 17,389,828 outputs
Outputs from BioData Mining
#187
of 271 outputs
Outputs of similar age
#138,348
of 273,190 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 17,389,828 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 271 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
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We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them