NUCLEIC ACIDS RESEARCH - Journal - Bridge of Knowledge

Search

NUCLEIC ACIDS RESEARCH

ISSN:

0305-1048

eISSN:

1362-4962

Disciplines
(Field of Science):

  • information and communication technology (Engineering and Technology)
  • biomedical engineering (Engineering and Technology)
  • medical biology (Medical and Health Sciences )
  • pharmacology and pharmacy (Medical and Health Sciences )
  • medical sciences (Medical and Health Sciences )
  • health sciences (Medical and Health Sciences )
  • family studies (Family studies)
  • agriculture and horticulture (Agricultural sciences)
  • food and nutrition technology (Agricultural sciences)
  • animal science and fisheries (Agricultural sciences)
  • biotechnology (Natural sciences)
  • computer and information sciences (Natural sciences)
  • biological sciences (Natural sciences)
  • chemical sciences (Natural sciences)

Ministry points: Help

Ministry points - current year
Year Points List
Year 2024 200 Ministry scored journals list 2024
Ministry points - previous years
Year Points List
2024 200 Ministry scored journals list 2024
2023 200 Ministry Scored Journals List
2022 200 Ministry Scored Journals List 2019-2022
2021 200 Ministry Scored Journals List 2019-2022
2020 200 Ministry Scored Journals List 2019-2022
2019 200 Ministry Scored Journals List 2019-2022
2018 40 A
2017 40 A
2016 40 A
2015 40 A
2014 40 A
2013 40 A
2012 40 A
2011 40 A
2010 32 A

Model:

Open Access

Points CiteScore:

Points CiteScore - current year
Year Points
Year 2023 27.1
Points CiteScore - previous years
Year Points
2023 27.1
2022 32.3
2021 28
2020 23.5
2019 21.1
2018 20.2
2017 19.7
2016 18.6
2015 17.7
2014 16.1
2013 13
2012 14.6
2011 13.9

Impact Factor:

Log in to see the Impact Factor.

Filters

total: 12

  • Category
  • Year
  • Options

clear Chosen catalog filters disabled

Catalog Journals

Year 2022
  • DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors
    Publication
    • S. Barissi
    • A. Sala
    • M. Wieczór
    • F. Battistini
    • M. Orozco

    - NUCLEIC ACIDS RESEARCH - Year 2022

    We present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations. The method is able to predict affinities obtained with techniques as different as uPBM, gcPBM and HT-SELEX with an excellent performance, much better than existing algorithms. Due to its nature, the method can...

    Full text available to download

Year 2021
Year 2020
Year 2018
Year 2017
Year 2016
Year 2014
Year 2003
Year 2002
Year 1992

seen 1383 times