Konferans Bildirisi

Effect of Benchmark Datasets on Protein Structure Prediction As a Concept

Sayı: 29 1 Aralık 2021
PDF İndir
TR EN

Effect of Benchmark Datasets on Protein Structure Prediction As a Concept

Abstract

Knowing the protein structures is essential in understanding the job descriptions of proteins involved in vital functions, drug design, and many more. On the other hand, protein structure prediction is an alternative bioinformatics sub-study field to shorten the process that takes a long time in the laboratory environment. Performance analyzes of the methods developed in this field are generally made on benchmark datasets. The size of the datasets directly affects the algorithm runtime. In this study, how to benchmark datasets are reflected in the results is analyzed. Within the scope of the study, two different benchmark datasets, CB513 and EVASet, and two different protein structure prediction methods, JPred and Porter, were used. The study is a source of inspiration for further studies with the idea of developing benchmark datasets that are comprehensive in terms of protein properties but contain as little data as possible in terms of data size.

Keywords

Destekleyen Kurum

Kayseri University Scientific Research Projects Unit

Proje Numarası

FHD-2021-1045

Teşekkür

This study was supported as Project Number: FHD-2021-1045 by Kayseri University Scientific Research Projects Unit. We thank Kayseri University Scientific Research Projects unit for their contributions.

Kaynakça

  1. Asai, K., Hayamizu, S., & Handa, K. I. (1993). Prediction of protein secondary structure by the hidden Markov model. Bioinformatics, 9(2), 141-146.
  2. Atasever, S., Azgınoglu, N., Erbay, H., & Aydın, Z. (2021). 3-State Protein Secondary Structure Prediction based on SCOPe Classes. Brazilian Archives of Biology and Technology, 64.
  3. Aydin, Z., Azginoglu, N., Bilgin, H. I., & Celik, M. (2019). Developing structural profile matrices for protein secondary structure and solvent accessibility prediction. Bioinformatics, 35(20), 4004-4010.
  4. Azginoglu, N., Aydin, Z., & Celik, M. (2020). Structural profile matrices for predicting structural properties of proteins. Journal of Bioinformatics and Computational Biology, 18(04), 2050022.
  5. Bouziane, H., Messabih, B., & Chouarfia, A. (2015). Effect of simple ensemble methods on protein secondary structure prediction. Soft Computing, 19(6), 1663-1678.
  6. Bujnicki, J. M., Elofsson, A., Fischer, D., & Rychlewski, L. (2001). LiveBench‐1: Continuous benchmarking of protein structure prediction servers. Protein Science, 10(2), 352-361.
  7. Cuff, J. A., & Barton, G. J. (1999). Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins: Structure, Function, and Bioinformatics, 34(4), 508-519.
  8. Drozdetskiy, A., Cole, C., Procter, J., & Barton, G. J. (2015). JPred4: a protein secondary structure prediction server. Nucleic acids research, 43(W1), W389-W394.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Konferans Bildirisi

Yayımlanma Tarihi

1 Aralık 2021

Gönderilme Tarihi

25 Ekim 2021

Kabul Tarihi

9 Aralık 2021

Yayımlandığı Sayı

Yıl 2021 Sayı: 29

Kaynak Göster

APA
Azgınoğlu, N. (2021). Effect of Benchmark Datasets on Protein Structure Prediction As a Concept. Avrupa Bilim ve Teknoloji Dergisi, 29, 117-121. https://doi.org/10.31590/ejosat.1014716