GraphBNC source code (parent repository)


Pihlajamäki, Antti; Malola, Sami; Matus Cortés, Maria; Häkkinen, Hannu. (2024). GraphBNC source code (parent repository). V. 19.6.2024. University of Jyväskylä. https://doi.org/10.17011/jyx/dataset/96311.


JYU authors: 
  • Contact person (yes/no): Yes
  • Contact person (yes/no): Yes
  • Contact person (yes/no): Yes
  • Contact person (yes/no): Yes

All authors: Pihlajamäki, Antti; Malola, Sami; Matus Cortés, Maria; Häkkinen, Hannu

Funders: Research Council of Finland

Right-holders: 


Availability and identifiers

Availability: Direct download

Publication year: 2024

URN identifier in original repository: http://urn.fi/URN:NBN:fi:jyu-202407055138

DOI identifier in original repository: https://doi.org/10.17011/jyx/dataset/96311

URN identifier in JYX: http://urn.fi/URN:NBN:fi:jyu-202407055138

DOI identifier in JYX: https://doi.org/10.17011/jyx/dataset/96311


Description of the dataset

Description: GraphBNC is a framework that combines graph theory based methods, machine learning and other computational tools for placing protected gold nanoclusters on blood proteins. Machine learning part, artificial neural networks (ANNs) in this case, estimates interactions between the ligand molecules of the nanocluster and amino acid residues, which are used to find favorable site for the nanocluster on the protein. This dataset contains basic source codes needed to run the method. The first part contains methods to encode the nanoclusters and the proteins. The second part has the codes to train and to test ANNs, but pretrained weights are also provided. The rest focuses on the placement of the cluster utilizing Monte Carlo -based simulated annealing.

This is the metadata for the parent repository of the codes. Updates and possible corrections are documented in the GitLab project, where the material saved and shared. The GitLab project can be found and downloaded from the following address: https://gitlab.jyu.fi/graphbnc-project-group/graphbnc

Language: English

Keywords (YSO): machine learning; graphs (network theory); nanomaterials; nanoparticles; nanosciences; nanostructures; molecular dynamics

Fields of science: 114 Physical sciences; 113 Computer and information sciences; 1182 Biochemistry, cell and molecular biology; 116 Chemical sciences

Follow-up groups: Nanoscience Center (Department of Physics PHYS, JYFL) (Faculty of Mathematics and Science) (Department of Chemistry CHEM) (Department of Biological and Environmental Science BIOENV) NSC

Do you deal with data concerning special categories of personal data in your research?: No


Projects related to dataset


Last updated on 2024-05-07 at 21:10