Literature DB >> 30052052

PROSES: A Web Server for Sequence-Based Protein Encoding.

İrfan Kösesoy1, Murat Gök1, Cemil Öz2.   

Abstract

Recently, the number of the amino acid sequences shared in online databases is growing rapidly in huge amounts. By using sequence-derived features, machine learning algorithms are successfully applied to prediction of protein functional classes, protein-protein interactions, subcellular location, and peptides of specific properties in many studies. Protein Sequence Encoding System (PROSES) is a web server designed as freely and easily accessible for all researchers who want to use computational methods on protein sequence data. That is, PROSES provides users to encode their protein sequences easily without writing any programming code.

Entities:  

Keywords:  feature extraction; machine learning; protein encoding; protein–protein interactions

Mesh:

Substances:

Year:  2018        PMID: 30052052     DOI: 10.1089/cmb.2018.0049

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  2 in total

Review 1.  In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm.

Authors:  Mahsa Torkamanian-Afshar; Sajjad Nematzadeh; Maryam Tabarzad; Ali Najafi; Hossein Lanjanian; Ali Masoudi-Nejad
Journal:  Mol Divers       Date:  2021-02-07       Impact factor: 2.943

2.  Prediction of host-pathogen protein interactions by extended network model.

Authors:  İrfan Kösesoy; Murat Gök; Tamer Kahveci
Journal:  Turk J Biol       Date:  2021-04-20
  2 in total

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