ACADSTAFF UGM

CREATION
Title : Deep learning for protein secondary structure prediction: Pre and post- AlphaFold
Author :

DEWI PRAMUDI ISMI (1) Prof. Dr.-Ing. Mhd. Reza M. I. Pulungan, S.Si., M.Sc. (2) Afiahayati, S.Kom., M.Cs., Ph.D (3)
Date : 0 2022
Keyword : Protein,Protein secondary structure prediction,Deep neural networks,Machine learning,Prediction accuracy Protein,Protein secondary structure prediction,Deep neural networks,Machine learning,Prediction accuracy
Abstract : This paper aims to provide a comprehensive review of the trends and challenges of deep neural networks for protein secondary structure prediction (PSSP). In recent years, deep neural networks have become the primary method for protein secondary structure prediction. Previous studies showed that deep neural networks had uplifted the accuracy of three-state secondary structure prediction to more than 80%. Favored deep learning methods, such as convolutional neural networks, recurrent neural networks, inception networks, and graph neural networks, have been implemented in protein secondary structure prediction. Methods adapted from natural language processing (NLP) and computer vision are also employed, including attention mechanism, ResNet, and U-shape networks. In the post-AlphaFold era, PSSP studies focus on different objectives, such as enhancing the quality of evolutionary information and exploiting protein language models as the PSSP input. The recent trend to utilize pre-trained language models as input features for secondary structure prediction provides a new direction for PSSP studies. Moreover, the state-of-the-art accuracy achieved by previous PSSP models is still below its theoretical limit. There are still rooms for improvement to be made in the field.
Group of Knowledge : Ilmu Komputer
Original Language : English
Level : Internasional
Status :
Published
Document
No Title Document Type Action
1 IPA-CSBJ-22.pdf
Document Type : [PAK] Full Dokumen
[PAK] Full Dokumen View
2 IPA-CSBJ-22-turnitin.pdf
Document Type : [PAK] Cek Similarity
[PAK] Cek Similarity View
3 Bukti-Korespondensi.pdf
Document Type : [PAK] Bukti Korespondensi Penulis
[PAK] Bukti Korespondensi Penulis View
4 SuratPernyataan-Afiahayati-DPI-CSBJ.pdf
Document Type : Dokumen Pendukung Karya Ilmiah (Hibah, Publikasi, Penelitian, Pengabdian)
Dokumen Pendukung Karya Ilmiah (Hibah, Publikasi, Penelitian, Pengabdian) View