Change search
Refine search result
1 - 2 of 2
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Systematic Literature Review Protocol:  Machine Learning and Microsimulation Techniques on the Prognosis of Dementia: A Systematic Literature Review2016Other (Other academic)
    Abstract [en]

     This document contains the protocol followed to conduct the systematic literature review entitled: “Machine Learning and Microsimulation Techniques on the Prognosis of Dementia: A Systematic Literature Review”.

  • 2.
    Sagar, Susheel
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Sidorova, Yulia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Sequence retriever for known, discovered, and user-specified molecular fragments2016In: 10TH INTERNATIONAL CONFERENCE ON PRACTICAL APPLICATIONS OF COMPUTATIONAL BIOLOGY & BIOINFORMATICS / [ed] Fdez-Riverola F.,De Paz J.F.,Rocha M.P.,Mayo F.J.D.,Mohamad M.S., Springer, 2016, Vol. 477, p. 51-58Conference paper (Refereed)
    Abstract [en]

    Typically, biological and chemical data are sequential, for example, as in genomic sequences or as in diverse chemical formats, such as InChI or SMILES. That poses a major problem for computational analysis, since the majority of the methods for data mining and prediction were developed to work on feature vectors. To address this challenge, a functionality of a Statistical Adapter has been proposed recently. It automatically converts parsable sequential input into feature vectors. During the conversion, insights are gained into the problem via finding regions of interest in the sequence and the level of abstraction for their representation, and the feature vectors are filled with the counts of interesting sequence fragments,-finally, making it possible to benefit from powerful vectorbased methods. For this submission, the Sequence Retriever has been added to the Adapter. While the Adapter performs the conversion: sequence → vector with the counts of interesting molecular fragments, the Retriever performs the mapping: molecular fragment → sequences from the database that contain this fragment.

1 - 2 of 2
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf