↓ Skip to main content

Derivative-free neural network for optimizing the scoring functions associated with dynamic programming of pairwise-profile alignment

Overview of attention for article published in Algorithms for Molecular Biology, February 2018
Altmetric Badge

About this Attention Score

  • Among the highest-scoring outputs from this source (#50 of 186)
  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
8 tweeters
facebook
3 Facebook pages

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
7 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Derivative-free neural network for optimizing the scoring functions associated with dynamic programming of pairwise-profile alignment
Published in
Algorithms for Molecular Biology, February 2018
DOI 10.1186/s13015-018-0123-6
Pubmed ID
Authors

Kazunori D. Yamada

Abstract

A profile-comparison method with position-specific scoring matrix (PSSM) is among the most accurate alignment methods. Currently, cosine similarity and correlation coefficients are used as scoring functions of dynamic programming to calculate similarity between PSSMs. However, it is unclear whether these functions are optimal for profile alignment methods. By definition, these functions cannot capture nonlinear relationships between profiles. Therefore, we attempted to discover a novel scoring function, which was more suitable for the profile-comparison method than existing functions, using neural networks. Although neural networks required derivative-of-cost functions, the problem being addressed in this study lacked them. Therefore, we implemented a novel derivative-free neural network by combining a conventional neural network with an evolutionary strategy optimization method used as a solver. Using this novel neural network system, we optimized the scoring function to align remote sequence pairs. Our results showed that the pairwise-profile aligner using the novel scoring function significantly improved both alignment sensitivity and precision relative to aligners using existing functions. We developed and implemented a novel derivative-free neural network and aligner (Nepal) for optimizing sequence alignments. Nepal improved alignment quality by adapting to remote sequence alignments and increasing the expressiveness of similarity scores. Additionally, this novel scoring function can be realized using a simple matrix operation and easily incorporated into other aligners. Moreover our scoring function could potentially improve the performance of homology detection and/or multiple-sequence alignment of remote homologous sequences. The goal of the study was to provide a novel scoring function for profile alignment method and develop a novel learning system capable of addressing derivative-free problems. Our system is capable of optimizing the performance of other sophisticated methods and solving problems without derivative-of-cost functions, which do not always exist in practical problems. Our results demonstrated the usefulness of this optimization method for derivative-free problems.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 29%
Student > Bachelor 2 29%
Student > Ph. D. Student 1 14%
Researcher 1 14%
Student > Doctoral Student 1 14%
Other 0 0%
Readers by discipline Count As %
Computer Science 2 29%
Environmental Science 1 14%
Biochemistry, Genetics and Molecular Biology 1 14%
Agricultural and Biological Sciences 1 14%
Engineering 1 14%
Other 0 0%
Unknown 1 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 21 February 2018.
All research outputs
#3,524,095
of 12,544,629 outputs
Outputs from Algorithms for Molecular Biology
#50
of 186 outputs
Outputs of similar age
#89,585
of 263,992 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 8 outputs
Altmetric has tracked 12,544,629 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 186 research outputs from this source. They receive a mean Attention Score of 2.9. This one has gotten more attention than average, scoring higher than 71% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,992 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.