Learning functional properties of proteins with language models

Unsal, SERBÜLENT; Atas, Heval; Albayrak, MUAMMER; Turhan, KEMAL; Acar, Aybar; Doğan, Tunca

doi:10.1038/s42256-022-00457-9

Learning functional properties of proteins with language models

Unsal S., Atas H., Albayrak M., Turhan K., Acar A. C., Doğan T.

NATURE MACHINE INTELLIGENCE, cilt.4, sa.3, ss.227-245, 2022 (SCI-Expanded, Scopus)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 4 Sayı: 3
Basım Tarihi: 2022
Doi Numarası: 10.1038/s42256-022-00457-9
Dergi Adı: NATURE MACHINE INTELLIGENCE
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
Sayfa Sayıları: ss.227-245
Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

Deep learning methods have in recent years shown promising results in characterizing proteins and extracting complex sequence-structure-function relationships. This Analysis describes a benchmarking study to compare the performances and advantages of recent deep learning approaches in a range of protein prediction tasks.