PROSES: A Web Server for Sequence-Based Protein Encoding

Kosesoy İ., Gok M., Oz C.

JOURNAL OF COMPUTATIONAL BIOLOGY, cilt.25, sa.10, ss.1120-1122, 2018 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25 Sayı: 10
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1089/cmb.2018.0049
  • Dergi Adı: JOURNAL OF COMPUTATIONAL BIOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1120-1122
  • Anahtar Kelimeler: feature extraction, machine learning, protein encoding, protein-protein interactions, AMINO-ACID-SEQUENCE, PHYSICOCHEMICAL FEATURES, PREDICTION, CLASSIFICATION, PEPTIDES, PROFEAT
  • Kocaeli Üniversitesi Adresli: Hayır

Özet

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.